diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
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diff --git a/docs/articles/about.html b/docs/articles/about.html
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- About xpose
+ About xpose
Benjamin Guiastrennec
- 17 March, 2020
+ 25 May, 2020
Source: vignettes/about.Rmd
about.Rmd
@@ -227,25 +227,11 @@
-
@@ -256,7 +242,7 @@
diff --git a/docs/articles/access_xpdb_data.html b/docs/articles/access_xpdb_data.html
index 661ad60f..f680f34d 100644
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- Access the xpdb data
+ Access the xpdb data
Benjamin Guiastrennec
- 17 March, 2020
+ 25 May, 2020
Source: vignettes/access_xpdb_data.Rmd
access_xpdb_data.Rmd
@@ -141,25 +141,25 @@ 17 March, 2020
Glimpse at the xpdb
The files attached to an xpdb object can be displayed to the console simply by writing the xpdb name to the console or by using the print() function. Any of these files can be accessed from the xpdb using one of the functions listed below.
-
+xpdb # or print(xpdb)
run001.lst overview:
- Software: nonmem 7.3.0
- - Attached files (memory usage 1.3 Mb):
+ - Attached files (memory usage 1.4 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
+ special: <none>
- gg_theme: theme_readable
- xp_theme: theme_xp_default
- - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+ - Options: dir = data, quiet = FALSE, manual_import = NULL
Access the model code
The get_code() function can be used to access the parsed model code from the xpdb. This code was used to create the summary and find table names. The parsed code can be used to get additional information about the run. If the argument .problem is specified a subset of the code can be returned based on $PROBLEM.
Note that general code warnings and PsN outputs appended are listed as problem 0.
-code <- get_code(xpdb)
-code
+code <- get_code(xpdb)
+code
# A tibble: 764 x 5
problem level subroutine code comment
* <int> <int> <chr> <chr> <chr>
@@ -180,8 +180,8 @@
Access the output data
The get_data() function can be used to access the imported table files. Tables can be accessed by table name or by .problem. In the latter a single dataset containing all aggregated tables is returned. If more than one table name or .problem number is provided a named list is returned.
Note when providing a table name it is not guaranteed that the table will be identical to its file (i.e. the order of the columns may have been changed and tables with FIRSTONLY will no longer be deduplicated).
-data <- get_data(xpdb, table = 'cotab001')
-data
+data <- get_data(xpdb, table = 'cotab001')
+data
# A tibble: 550 x 4
ID CLCR AGE WT
<fct> <dbl> <dbl> <dbl>
@@ -201,8 +201,8 @@
Access the run files
The get_file() function can be used to access the imported output files. Files can be accessed by file name, by .problem, .subprob and/or .method. If more than one file name, .problem, .subprob, or .method is provided a named list is returned.
-file <- get_file(xpdb, file = 'run001.ext')
-file
+file <- get_file(xpdb, file = 'run001.ext')
+file
# A tibble: 28 x 16
ITERATION THETA1 THETA2 THETA3 THETA4 THETA5 THETA6 THETA7 `SIGMA(1,1)` `OMEGA(1,1)`
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
@@ -223,9 +223,9 @@
Access the parameter estimates
The get_prm() function can be used to access the parameter estimates. To get a nice parameter table printed to the console use the function prm_table() instead. The arguments .problem, .subprob and .method can be used to select the parameter estimates to output.
-# Raw output for editing
-prm <- get_prm(xpdb, digits = 4)
-prm
+# Raw output for editing
+prm <- get_prm(xpdb, digits = 4)
+prm
# A tibble: 11 x 10
type name label value se rse fixed diagonal m n
* <chr> <chr> <chr> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl>
@@ -240,8 +240,8 @@
9 ome OMEGA(2,2) "IIV V" 0.195 0.0320 0.164 FALSE TRUE 2 2
10 ome OMEGA(3,3) "IIV KA" 1.38 0.202 0.146 FALSE TRUE 3 3
11 sig SIGMA(1,1) "" 1 NA NA TRUE TRUE 1 1
-# Nicely formated table
-prm_table(xpdb, digits = 4)
+# Nicely formated table
+prm_table(xpdb, digits = 4)
Reporting transformed parameters:
For the OMEGA and SIGMA matrices, values are reported as standard deviations for the diagonal elements and as correlations for the off-diagonal elements. The relative standard errors (RSE) for OMEGA and SIGMA are reported on the approximate standard deviation scale (SE/variance estimate)/2. Use `transform = FALSE` to report untransformed parameters.
@@ -265,13 +265,13 @@
Access the run summary
The get_summary() function can be used to access the generated run summary from which the template_titles. If the argument .problem is specified a subset of the summary can be returned based on $PROBLEM.
Note that general summary information are listed as problem 0.
-run_sum <- get_summary(xpdb, .problem = 0)
-run_sum
+run_sum <- get_summary(xpdb, .problem = 0)
+run_sum
# A tibble: 12 x 5
problem subprob descr label value
<dbl> <dbl> <chr> <chr> <chr>
1 0 0 Run description descr NONMEM PK example for xpose
- 2 0 0 Run directory dir analysis/models/pk/
+ 2 0 0 Run directory dir data
3 0 0 Run errors errors na
4 0 0 ESAMPLE seed number esampleseed na
5 0 0 Run file file run001.lst
@@ -285,7 +285,7 @@
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diff --git a/docs/articles/customize_plots.html b/docs/articles/customize_plots.html
index ddf32add..74117d72 100644
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+++ b/docs/articles/customize_plots.html
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- Customize plots
+ Customize plots
Benjamin Guiastrennec
- 17 March, 2020
+ 25 May, 2020
Source: vignettes/customize_plots.Rmd
customize_plots.Rmd
@@ -167,33 +167,33 @@
Labels
-All xpose plots have by default an informative title, subtitle and caption. For example all plots using individual model predictions (IPRED) will display the epsilons’ shrinkage. These titles can easily be edited as templates using @keywords which will be replaced by their actual value stored in the summary level of the xpdb object when rendering the plots. Keywords are defined by a word preceded by a @ e.g. '@ofv'. A list of all available keyword can be accessed via help('template_titles'). The title, subtitle or caption can be disabled by setting them to NULL. Suffix can be automatically added to title, subtitle and caption of all plots. The suffixes can be defined in the xp_theme.
+All xpose plots have by default an informative title, subtitle and caption. For example all plots using individual model predictions (IPRED) will display the epsilons’ shrinkage. These titles can easily be edited as templates using @keywords which will be replaced by their actual value stored in the summary level of the xpdb object when rendering the plots. Keywords are defined by a word preceded by a @ e.g. '@ofv'. A list of all available keyword can be accessed via help('template_titles'). The title, subtitle or caption can be disabled by setting them to NULL. Suffix can be automatically added to title, subtitle and caption of all plots. The suffixes can be defined in the xp_theme.
From the plot functions
-# Using template titles
-dv_vs_ipred(xpdb,
- title = '@y vs. @x (@run, obj: @ofv)',
- subtitle = 'Based on: @nind subjects and @nobs records',
- caption = 'Dir: @dir')
+# Using template titles
+dv_vs_ipred(xpdb,
+ title = '@y vs. @x (@run, obj: @ofv)',
+ subtitle = 'Based on: @nind subjects and @nobs records',
+ caption = 'Dir: @dir')
-# Disabling all titles
-dv_vs_ipred(xpdb, title = NULL, subtitle = NULL, caption = NULL)
+# Disabling all titles
+dv_vs_ipred(xpdb, title = NULL, subtitle = NULL, caption = NULL)
-# Edit title suffix from the xp_theme for a specific plot
-dv_vs_ipred(xpdb, title = 'A title', xp_theme = list(title_suffix = ' | a suffix for @run'))
+# Edit title suffix from the xp_theme for a specific plot
+dv_vs_ipred(xpdb, title = 'A title', xp_theme = list(title_suffix = ' | a suffix for @run'))
Using the labs function
Labels can also modified later on by using the ggplot2::labs() function in combination with the ggplot2 + operator.
-dv_vs_ipred(xpdb) +
- labs(title = '@descr',
- subtitle = NULL,
- caption = 'Based on @nobs observations',
- x = 'Individual model predictions (@x)',
- y = 'Observations (@y) for @nind subjects')
+dv_vs_ipred(xpdb) +
+ labs(title = '@descr',
+ subtitle = NULL,
+ caption = 'Based on @nobs observations',
+ x = 'Individual model predictions (@x)',
+ y = 'Observations (@y) for @nind subjects')
@@ -202,18 +202,18 @@
Modify aesthetics
By default the aesthetics are read from the xp_theme level in the xpdb object but these can be modified in any plot function. xpose makes use of the ggplot2 functions mapping for any layer (e.g. points, lines, etc.) however to direct the mapping to a specific layer, a prefix appealing to the targeted layer should be used. The format is defined as layer_aesthetic = value. Hence to change the color of points in ggplot2 the argument color = 'green' could be used in geom_point(), while in xpose the same could be achieved with point_color = 'green'.
In basic goodness-of-fit plots, the layers have been named as: point_xxx, line_xxx, smooth_xxx, guide_xxx, xscale_xxx, yscale_xxx where xxx can be any option available in the ggplot2 layers: geom_point, geom_line, geom_smooth, geom_abline, scale_x_continuous, etc.
-dv_vs_ipred(xpdb,
- # Change points aesthetics
- point_color = 'blue', point_alpha = 0.5,
- point_stroke = 0, point_size = 1.5,
- # Change lines aesthetics
- line_alpha = 0.5, line_size = 0.5,
- line_color = 'orange', line_linetype = 'solid',
- # Change smooth aesthetics
- smooth_method = 'lm')
+dv_vs_ipred(xpdb,
+ # Change points aesthetics
+ point_color = 'blue', point_alpha = 0.5,
+ point_stroke = 0, point_size = 1.5,
+ # Change lines aesthetics
+ line_alpha = 0.5, line_size = 0.5,
+ line_color = 'orange', line_linetype = 'solid',
+ # Change smooth aesthetics
+ smooth_method = 'lm')
Aesthetics can also be defined mapped to a variable using the ggplot2 aes() function.
-dv_vs_ipred(xpdb, type = 'p', aes(point_color = SEX))
+dv_vs_ipred(xpdb, type = 'p', aes(point_color = SEX))
@@ -229,11 +229,11 @@
If facets is a string e.g facets = "SEX", ggforce::facet_wrap_paginate will be used
If facets is a formula e.g facets = SEX~MED1, ggforce::facet_grid_paginate will be used
-# Example with a string
-dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 1)
+# Example with a string
+dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 1)
-# Example with a formula
-dv_vs_ipred(xpdb, facets = SEX~MED1, margins = TRUE)
+# Example with a formula
+dv_vs_ipred(xpdb, facets = SEX~MED1, margins = TRUE)
All xpose plot functions accept arguments for facet_wrap_pagninate and facet_grid_paginate (e.g. ncol = 2, labeller = 'label_both', etc.). With the default xpose theme scales are set to 'free' from one panel to another (scales = 'free'), this behavior can be changed with scales = 'fixed', 'free_y' or 'free_x'.
For more information on the available faceting options read the help of facet_wrap_paginate and facet_grid_paginate from the package ggforce.
@@ -243,32 +243,32 @@
Additional layers
xpose offers the opportunity to add any additional layers from ggplot2. Hence, a ggplot2::geom_rug() layer could be added to the dv_vs_ipred() plot along with some annotations (ggplot2::annotate()). Note: the additional layers do not inherit from the xpose aesthetic mapping (i.e. colors or other options need to be defined in each layer as shown below).
Layers can also be used to modify the aesthetics scales for example ggplot2::scale_color_manual(), or remove a legend ggplot2::scale_fill_identity().
-dv_vs_ipred(xpdb) +
- geom_rug(alpha = 0.2, color = 'grey50',
- sides = 'lb', size = 0.4) +
- annotate(geom = 'text',
- fontface = 'bold',
- color = 'darkred',
- size = 3,
- label = 'Toxic concentrations', x = 1.35, y = 1.75) +
- annotate(geom = 'rect',
- alpha = 0.2, fill = 'red',
- xmin = 1, xmax = Inf,
- ymin = 1, ymax = Inf)
+dv_vs_ipred(xpdb) +
+ geom_rug(alpha = 0.2, color = 'grey50',
+ sides = 'lb', size = 0.4) +
+ annotate(geom = 'text',
+ fontface = 'bold',
+ color = 'darkred',
+ size = 3,
+ label = 'Toxic concentrations', x = 1.35, y = 1.75) +
+ annotate(geom = 'rect',
+ alpha = 0.2, fill = 'red',
+ xmin = 1, xmax = Inf,
+ ymin = 1, ymax = Inf)
Scales options
The argument log allows to log-transform the axes. Accepted values are x, y or xy.
-dv_vs_ipred(xpdb, log = 'xy', subtitle = 'Plot on log scale')
+dv_vs_ipred(xpdb, log = 'xy', subtitle = 'Plot on log scale')
Additional arguments can be provided to the scales via the mapping by using the naming convention xscale_xxx or yscale_xxx where xxx is the name of a ggplot2 scale argument such as name, breaks, labels, expand.
-dv_vs_ipred(xpdb,
- xscale_breaks = c(0.3, 0.8, 1.3),
- xscale_labels = c('Low', 'Med', 'High'),
- xscale_expand = c(0.2, 0),
- xscale_name = 'Individual model predictions')
+dv_vs_ipred(xpdb,
+ xscale_breaks = c(0.3, 0.8, 1.3),
+ xscale_labels = c('Low', 'Med', 'High'),
+ xscale_expand = c(0.2, 0),
+ xscale_name = 'Individual model predictions')
@@ -281,16 +281,16 @@
An xpose (xp_theme) theme controlling the aspect of the layers such as points or lines
Themes can be attached to an xpdb when importing the data with xpose_data() or using the function update_themes() on an xpdb object.
-# While creating the xpdb
-xpdb <- xpose_data(runno = '001',
- gg_theme = theme_minimal(),
- xp_theme = theme_xp_xpose4())
-
-# Update a pre-existing xpdb
-xpdb <- update_themes(xpdb = xpdb,
- gg_theme = theme_bw2(),
- xp_theme = list(point_color = 'dodgerblue4',
- line_color = 'dodgerblue4'))
+# While creating the xpdb
+xpdb <- xpose_data(runno = '001',
+ gg_theme = theme_minimal(),
+ xp_theme = theme_xp_xpose4())
+
+# Update a pre-existing xpdb
+xpdb <- update_themes(xpdb = xpdb,
+ gg_theme = theme_bw2(),
+ xp_theme = list(point_color = 'dodgerblue4',
+ line_color = 'dodgerblue4'))
-
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+ Frequently Asked Questions
Benjamin Guiastrennec
- 17 March, 2020
+ 25 May, 2020
Source: vignettes/faq.Rmd
faq.Rmd
@@ -189,20 +189,11 @@
-
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diff --git a/docs/articles/import_model_outputs.html b/docs/articles/import_model_outputs.html
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index f427bb96..c0a2fc4d 100644
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+++ b/docs/articles/interactive_plots.html
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- Creating interactive plots
+ Creating interactive plots
Benjamin Guiastrennec
- 17 March, 2020
+ 25 May, 2020
Source: vignettes/interactive_plots.Rmd
interactive_plots.Rmd
@@ -148,23 +148,23 @@
Install plotly
This command is only required for the first use.
-install.packages("plotly")
+install.packages("plotly")
Generate a plot with xpose
Then generate any plot in xpose and store it in an object.
-gof_plot <- dv_vs_ipred(xpdb_ex_pk, type = 'pl',
- aes(point_color = SEX,
- line_color = SEX)) +
- labs(color = 'Sex', title = 'DV vs IPRED')
+gof_plot <- dv_vs_ipred(xpdb_ex_pk, type = 'pl',
+ aes(point_color = SEX,
+ line_color = SEX)) +
+ labs(color = 'Sex', title = 'DV vs IPRED')
## Using data from $prob no.1
## Filtering data by EVID == 0
@@ -172,9 +172,9 @@
Convert the plot from ggplot2 to plotly
The ggplot2 object is now automatically converted to a plotly graph. The scales can now be changed interactively, groups can be hidden by clicking on the legend
-plotly::ggplotly(gof_plot)
-
-
+plotly::ggplotly(gof_plot)
+
+
@@ -182,26 +182,26 @@
A distribution
-xpdb_ex_pk %>%
- eta_distrib(title = 'Eta distribution') %>%
- plotly::ggplotly()
-
-
+xpdb_ex_pk %>%
+ eta_distrib(title = 'Eta distribution') %>%
+ plotly::ggplotly()
+
+
-
diff --git a/docs/articles/interactive_plots_files/plotly-binding-4.9.2.1/plotly.js b/docs/articles/interactive_plots_files/plotly-binding-4.9.2.1/plotly.js
new file mode 100644
index 00000000..a625ab2e
--- /dev/null
+++ b/docs/articles/interactive_plots_files/plotly-binding-4.9.2.1/plotly.js
@@ -0,0 +1,935 @@
+
+HTMLWidgets.widget({
+ name: "plotly",
+ type: "output",
+
+ initialize: function(el, width, height) {
+ return {};
+ },
+
+ resize: function(el, width, height, instance) {
+ if (instance.autosize) {
+ var width = instance.width || width;
+ var height = instance.height || height;
+ Plotly.relayout(el.id, {width: width, height: height});
+ }
+ },
+
+ renderValue: function(el, x, instance) {
+
+ // Plotly.relayout() mutates the plot input object, so make sure to
+ // keep a reference to the user-supplied width/height *before*
+ // we call Plotly.plot();
+ var lay = x.layout || {};
+ instance.width = lay.width;
+ instance.height = lay.height;
+ instance.autosize = lay.autosize || true;
+
+ /*
+ / 'inform the world' about highlighting options this is so other
+ / crosstalk libraries have a chance to respond to special settings
+ / such as persistent selection.
+ / AFAIK, leaflet is the only library with such intergration
+ / https://github.com/rstudio/leaflet/pull/346/files#diff-ad0c2d51ce5fdf8c90c7395b102f4265R154
+ */
+ var ctConfig = crosstalk.var('plotlyCrosstalkOpts').set(x.highlight);
+
+ if (typeof(window) !== "undefined") {
+ // make sure plots don't get created outside the network (for on-prem)
+ window.PLOTLYENV = window.PLOTLYENV || {};
+ window.PLOTLYENV.BASE_URL = x.base_url;
+
+ // Enable persistent selection when shift key is down
+ // https://stackoverflow.com/questions/1828613/check-if-a-key-is-down
+ var persistOnShift = function(e) {
+ if (!e) window.event;
+ if (e.shiftKey) {
+ x.highlight.persistent = true;
+ x.highlight.persistentShift = true;
+ } else {
+ x.highlight.persistent = false;
+ x.highlight.persistentShift = false;
+ }
+ };
+
+ // Only relevant if we haven't forced persistent mode at command line
+ if (!x.highlight.persistent) {
+ window.onmousemove = persistOnShift;
+ }
+ }
+
+ var graphDiv = document.getElementById(el.id);
+
+ // TODO: move the control panel injection strategy inside here...
+ HTMLWidgets.addPostRenderHandler(function() {
+
+ // lower the z-index of the modebar to prevent it from highjacking hover
+ // (TODO: do this via CSS?)
+ // https://github.com/ropensci/plotly/issues/956
+ // https://www.w3schools.com/jsref/prop_style_zindex.asp
+ var modebars = document.querySelectorAll(".js-plotly-plot .plotly .modebar");
+ for (var i = 0; i < modebars.length; i++) {
+ modebars[i].style.zIndex = 1;
+ }
+ });
+
+ // inject a "control panel" holding selectize/dynamic color widget(s)
+ if (x.selectize || x.highlight.dynamic && !instance.plotly) {
+ var flex = document.createElement("div");
+ flex.class = "plotly-crosstalk-control-panel";
+ flex.style = "display: flex; flex-wrap: wrap";
+
+ // inject the colourpicker HTML container into the flexbox
+ if (x.highlight.dynamic) {
+ var pickerDiv = document.createElement("div");
+
+ var pickerInput = document.createElement("input");
+ pickerInput.id = el.id + "-colourpicker";
+ pickerInput.placeholder = "asdasd";
+
+ var pickerLabel = document.createElement("label");
+ pickerLabel.for = pickerInput.id;
+ pickerLabel.innerHTML = "Brush color ";
+
+ pickerDiv.appendChild(pickerLabel);
+ pickerDiv.appendChild(pickerInput);
+ flex.appendChild(pickerDiv);
+ }
+
+ // inject selectize HTML containers (one for every crosstalk group)
+ if (x.selectize) {
+ var ids = Object.keys(x.selectize);
+
+ for (var i = 0; i < ids.length; i++) {
+ var container = document.createElement("div");
+ container.id = ids[i];
+ container.style = "width: 80%; height: 10%";
+ container.class = "form-group crosstalk-input-plotly-highlight";
+
+ var label = document.createElement("label");
+ label.for = ids[i];
+ label.innerHTML = x.selectize[ids[i]].group;
+ label.class = "control-label";
+
+ var selectDiv = document.createElement("div");
+ var select = document.createElement("select");
+ select.multiple = true;
+
+ selectDiv.appendChild(select);
+ container.appendChild(label);
+ container.appendChild(selectDiv);
+ flex.appendChild(container);
+ }
+ }
+
+ // finally, insert the flexbox inside the htmlwidget container,
+ // but before the plotly graph div
+ graphDiv.parentElement.insertBefore(flex, graphDiv);
+
+ if (x.highlight.dynamic) {
+ var picker = $("#" + pickerInput.id);
+ var colors = x.highlight.color || [];
+ // TODO: let users specify options?
+ var opts = {
+ value: colors[0],
+ showColour: "both",
+ palette: "limited",
+ allowedCols: colors.join(" "),
+ width: "20%",
+ height: "10%"
+ };
+ picker.colourpicker({changeDelay: 0});
+ picker.colourpicker("settings", opts);
+ picker.colourpicker("value", opts.value);
+ // inform crosstalk about a change in the current selection colour
+ var grps = x.highlight.ctGroups || [];
+ for (var i = 0; i < grps.length; i++) {
+ crosstalk.group(grps[i]).var('plotlySelectionColour')
+ .set(picker.colourpicker('value'));
+ }
+ picker.on("change", function() {
+ for (var i = 0; i < grps.length; i++) {
+ crosstalk.group(grps[i]).var('plotlySelectionColour')
+ .set(picker.colourpicker('value'));
+ }
+ });
+ }
+ }
+
+ // if no plot exists yet, create one with a particular configuration
+ if (!instance.plotly) {
+
+ var plot = Plotly.plot(graphDiv, x);
+ instance.plotly = true;
+
+ } else {
+
+ // this is essentially equivalent to Plotly.newPlot(), but avoids creating
+ // a new webgl context
+ // https://github.com/plotly/plotly.js/blob/2b24f9def901831e61282076cf3f835598d56f0e/src/plot_api/plot_api.js#L531-L532
+
+ // TODO: restore crosstalk selections?
+ Plotly.purge(graphDiv);
+ // TODO: why is this necessary to get crosstalk working?
+ graphDiv.data = undefined;
+ graphDiv.layout = undefined;
+ var plot = Plotly.plot(graphDiv, x);
+ }
+
+ // Trigger plotly.js calls defined via `plotlyProxy()`
+ plot.then(function() {
+ if (HTMLWidgets.shinyMode) {
+ Shiny.addCustomMessageHandler("plotly-calls", function(msg) {
+ var gd = document.getElementById(msg.id);
+ if (!gd) {
+ throw new Error("Couldn't find plotly graph with id: " + msg.id);
+ }
+ // This isn't an official plotly.js method, but it's the only current way to
+ // change just the configuration of a plot
+ // https://community.plot.ly/t/update-config-function/9057
+ if (msg.method == "reconfig") {
+ Plotly.react(gd, gd.data, gd.layout, msg.args);
+ return;
+ }
+ if (!Plotly[msg.method]) {
+ throw new Error("Unknown method " + msg.method);
+ }
+ var args = [gd].concat(msg.args);
+ Plotly[msg.method].apply(null, args);
+ });
+ }
+
+ // plotly's mapbox API doesn't currently support setting bounding boxes
+ // https://www.mapbox.com/mapbox-gl-js/example/fitbounds/
+ // so we do this manually...
+ // TODO: make sure this triggers on a redraw and relayout as well as on initial draw
+ var mapboxIDs = graphDiv._fullLayout._subplots.mapbox || [];
+ for (var i = 0; i < mapboxIDs.length; i++) {
+ var id = mapboxIDs[i];
+ var mapOpts = x.layout[id] || {};
+ var args = mapOpts._fitBounds || {};
+ if (!args) {
+ continue;
+ }
+ var mapObj = graphDiv._fullLayout[id]._subplot.map;
+ mapObj.fitBounds(args.bounds, args.options);
+ }
+
+ });
+
+ // Attach attributes (e.g., "key", "z") to plotly event data
+ function eventDataWithKey(eventData) {
+ if (eventData === undefined || !eventData.hasOwnProperty("points")) {
+ return null;
+ }
+ return eventData.points.map(function(pt) {
+ var obj = {
+ curveNumber: pt.curveNumber,
+ pointNumber: pt.pointNumber,
+ x: pt.x,
+ y: pt.y
+ };
+
+ // If 'z' is reported with the event data, then use it!
+ if (pt.hasOwnProperty("z")) {
+ obj.z = pt.z;
+ }
+
+ if (pt.hasOwnProperty("customdata")) {
+ obj.customdata = pt.customdata;
+ }
+
+ /*
+ TL;DR: (I think) we have to select the graph div (again) to attach keys...
+
+ Why? Remember that crosstalk will dynamically add/delete traces
+ (see traceManager.prototype.updateSelection() below)
+ For this reason, we can't simply grab keys from x.data (like we did previously)
+ Moreover, we can't use _fullData, since that doesn't include
+ unofficial attributes. It's true that click/hover events fire with
+ pt.data, but drag events don't...
+ */
+ var gd = document.getElementById(el.id);
+ var trace = gd.data[pt.curveNumber];
+
+ if (!trace._isSimpleKey) {
+ var attrsToAttach = ["key"];
+ } else {
+ // simple keys fire the whole key
+ obj.key = trace.key;
+ var attrsToAttach = [];
+ }
+
+ for (var i = 0; i < attrsToAttach.length; i++) {
+ var attr = trace[attrsToAttach[i]];
+ if (Array.isArray(attr)) {
+ if (typeof pt.pointNumber === "number") {
+ obj[attrsToAttach[i]] = attr[pt.pointNumber];
+ } else if (Array.isArray(pt.pointNumber)) {
+ obj[attrsToAttach[i]] = attr[pt.pointNumber[0]][pt.pointNumber[1]];
+ } else if (Array.isArray(pt.pointNumbers)) {
+ obj[attrsToAttach[i]] = pt.pointNumbers.map(function(idx) { return attr[idx]; });
+ }
+ }
+ }
+ return obj;
+ });
+ }
+
+
+ var legendEventData = function(d) {
+ // if legendgroup is not relevant just return the trace
+ var trace = d.data[d.curveNumber];
+ if (!trace.legendgroup) return trace;
+
+ // if legendgroup was specified, return all traces that match the group
+ var legendgrps = d.data.map(function(trace){ return trace.legendgroup; });
+ var traces = [];
+ for (i = 0; i < legendgrps.length; i++) {
+ if (legendgrps[i] == trace.legendgroup) {
+ traces.push(d.data[i]);
+ }
+ }
+
+ return traces;
+ };
+
+
+ // send user input event data to shiny
+ if (HTMLWidgets.shinyMode && Shiny.setInputValue) {
+
+ // Some events clear other input values
+ // TODO: always register these?
+ var eventClearMap = {
+ plotly_deselect: ["plotly_selected", "plotly_selecting", "plotly_brushed", "plotly_brushing", "plotly_click"],
+ plotly_unhover: ["plotly_hover"],
+ plotly_doubleclick: ["plotly_click"]
+ };
+
+ Object.keys(eventClearMap).map(function(evt) {
+ graphDiv.on(evt, function() {
+ var inputsToClear = eventClearMap[evt];
+ inputsToClear.map(function(input) {
+ Shiny.setInputValue(input + "-" + x.source, null, {priority: "event"});
+ });
+ });
+ });
+
+ var eventDataFunctionMap = {
+ plotly_click: eventDataWithKey,
+ plotly_sunburstclick: eventDataWithKey,
+ plotly_hover: eventDataWithKey,
+ plotly_unhover: eventDataWithKey,
+ // If 'plotly_selected' has already been fired, and you click
+ // on the plot afterwards, this event fires `undefined`?!?
+ // That might be considered a plotly.js bug, but it doesn't make
+ // sense for this input change to occur if `d` is falsy because,
+ // even in the empty selection case, `d` is truthy (an object),
+ // and the 'plotly_deselect' event will reset this input
+ plotly_selected: function(d) { if (d) { return eventDataWithKey(d); } },
+ plotly_selecting: function(d) { if (d) { return eventDataWithKey(d); } },
+ plotly_brushed: function(d) {
+ if (d) { return d.range ? d.range : d.lassoPoints; }
+ },
+ plotly_brushing: function(d) {
+ if (d) { return d.range ? d.range : d.lassoPoints; }
+ },
+ plotly_legendclick: legendEventData,
+ plotly_legenddoubleclick: legendEventData,
+ plotly_clickannotation: function(d) { return d.fullAnnotation }
+ };
+
+ var registerShinyValue = function(event) {
+ var eventDataPreProcessor = eventDataFunctionMap[event] || function(d) { return d ? d : el.id };
+ // some events are unique to the R package
+ var plotlyJSevent = (event == "plotly_brushed") ? "plotly_selected" : (event == "plotly_brushing") ? "plotly_selecting" : event;
+ // register the event
+ graphDiv.on(plotlyJSevent, function(d) {
+ Shiny.setInputValue(
+ event + "-" + x.source,
+ JSON.stringify(eventDataPreProcessor(d)),
+ {priority: "event"}
+ );
+ });
+ }
+
+ var shinyEvents = x.shinyEvents || [];
+ shinyEvents.map(registerShinyValue);
+ }
+
+ // Given an array of {curveNumber: x, pointNumber: y} objects,
+ // return a hash of {
+ // set1: {value: [key1, key2, ...], _isSimpleKey: false},
+ // set2: {value: [key3, key4, ...], _isSimpleKey: false}
+ // }
+ function pointsToKeys(points) {
+ var keysBySet = {};
+ for (var i = 0; i < points.length; i++) {
+
+ var trace = graphDiv.data[points[i].curveNumber];
+ if (!trace.key || !trace.set) {
+ continue;
+ }
+
+ // set defaults for this keySet
+ // note that we don't track the nested property (yet) since we always
+ // emit the union -- http://cpsievert.github.io/talks/20161212b/#21
+ keysBySet[trace.set] = keysBySet[trace.set] || {
+ value: [],
+ _isSimpleKey: trace._isSimpleKey
+ };
+
+ // Use pointNumber by default, but aggregated traces should emit pointNumbers
+ var ptNum = points[i].pointNumber;
+ var hasPtNum = typeof ptNum === "number";
+ var ptNum = hasPtNum ? ptNum : points[i].pointNumbers;
+
+ // selecting a point of a "simple" trace means: select the
+ // entire key attached to this trace, which is useful for,
+ // say clicking on a fitted line to select corresponding observations
+ var key = trace._isSimpleKey ? trace.key : Array.isArray(ptNum) ? ptNum.map(function(idx) { return trace.key[idx]; }) : trace.key[ptNum];
+ // http://stackoverflow.com/questions/10865025/merge-flatten-an-array-of-arrays-in-javascript
+ var keyFlat = trace._isNestedKey ? [].concat.apply([], key) : key;
+
+ // TODO: better to only add new values?
+ keysBySet[trace.set].value = keysBySet[trace.set].value.concat(keyFlat);
+ }
+
+ return keysBySet;
+ }
+
+
+ x.highlight.color = x.highlight.color || [];
+ // make sure highlight color is an array
+ if (!Array.isArray(x.highlight.color)) {
+ x.highlight.color = [x.highlight.color];
+ }
+
+ var traceManager = new TraceManager(graphDiv, x.highlight);
+
+ // Gather all *unique* sets.
+ var allSets = [];
+ for (var curveIdx = 0; curveIdx < x.data.length; curveIdx++) {
+ var newSet = x.data[curveIdx].set;
+ if (newSet) {
+ if (allSets.indexOf(newSet) === -1) {
+ allSets.push(newSet);
+ }
+ }
+ }
+
+ // register event listeners for all sets
+ for (var i = 0; i < allSets.length; i++) {
+
+ var set = allSets[i];
+ var selection = new crosstalk.SelectionHandle(set);
+ var filter = new crosstalk.FilterHandle(set);
+
+ var filterChange = function(e) {
+ removeBrush(el);
+ traceManager.updateFilter(set, e.value);
+ };
+ filter.on("change", filterChange);
+
+
+ var selectionChange = function(e) {
+
+ // Workaround for 'plotly_selected' now firing previously selected
+ // points (in addition to new ones) when holding shift key. In our case,
+ // we just want the new keys
+ if (x.highlight.on === "plotly_selected" && x.highlight.persistentShift) {
+ // https://stackoverflow.com/questions/1187518/how-to-get-the-difference-between-two-arrays-in-javascript
+ Array.prototype.diff = function(a) {
+ return this.filter(function(i) {return a.indexOf(i) < 0;});
+ };
+ e.value = e.value.diff(e.oldValue);
+ }
+
+ // array of "event objects" tracking the selection history
+ // this is used to avoid adding redundant selections
+ var selectionHistory = crosstalk.var("plotlySelectionHistory").get() || [];
+
+ // Construct an event object "defining" the current event.
+ var event = {
+ receiverID: traceManager.gd.id,
+ plotlySelectionColour: crosstalk.group(set).var("plotlySelectionColour").get()
+ };
+ event[set] = e.value;
+ // TODO: is there a smarter way to check object equality?
+ if (selectionHistory.length > 0) {
+ var ev = JSON.stringify(event);
+ for (var i = 0; i < selectionHistory.length; i++) {
+ var sel = JSON.stringify(selectionHistory[i]);
+ if (sel == ev) {
+ return;
+ }
+ }
+ }
+
+ // accumulate history for persistent selection
+ if (!x.highlight.persistent) {
+ selectionHistory = [event];
+ } else {
+ selectionHistory.push(event);
+ }
+ crosstalk.var("plotlySelectionHistory").set(selectionHistory);
+
+ // do the actual updating of traces, frames, and the selectize widget
+ traceManager.updateSelection(set, e.value);
+ // https://github.com/selectize/selectize.js/blob/master/docs/api.md#methods_items
+ if (x.selectize) {
+ if (!x.highlight.persistent || e.value === null) {
+ selectize.clear(true);
+ }
+ selectize.addItems(e.value, true);
+ selectize.close();
+ }
+ }
+ selection.on("change", selectionChange);
+
+ // Set a crosstalk variable selection value, triggering an update
+ var turnOn = function(e) {
+ if (e) {
+ var selectedKeys = pointsToKeys(e.points);
+ // Keys are group names, values are array of selected keys from group.
+ for (var set in selectedKeys) {
+ if (selectedKeys.hasOwnProperty(set)) {
+ selection.set(selectedKeys[set].value, {sender: el});
+ }
+ }
+ }
+ };
+ if (x.highlight.debounce > 0) {
+ turnOn = debounce(turnOn, x.highlight.debounce);
+ }
+ graphDiv.on(x.highlight.on, turnOn);
+
+ graphDiv.on(x.highlight.off, function turnOff(e) {
+ // remove any visual clues
+ removeBrush(el);
+ // remove any selection history
+ crosstalk.var("plotlySelectionHistory").set(null);
+ // trigger the actual removal of selection traces
+ selection.set(null, {sender: el});
+ });
+
+ // register a callback for selectize so that there is bi-directional
+ // communication between the widget and direct manipulation events
+ if (x.selectize) {
+ var selectizeID = Object.keys(x.selectize)[i];
+ var items = x.selectize[selectizeID].items;
+ var first = [{value: "", label: "(All)"}];
+ var opts = {
+ options: first.concat(items),
+ searchField: "label",
+ valueField: "value",
+ labelField: "label",
+ maxItems: 50
+ };
+ var select = $("#" + selectizeID).find("select")[0];
+ var selectize = $(select).selectize(opts)[0].selectize;
+ // NOTE: this callback is triggered when *directly* altering
+ // dropdown items
+ selectize.on("change", function() {
+ var currentItems = traceManager.groupSelections[set] || [];
+ if (!x.highlight.persistent) {
+ removeBrush(el);
+ for (var i = 0; i < currentItems.length; i++) {
+ selectize.removeItem(currentItems[i], true);
+ }
+ }
+ var newItems = selectize.items.filter(function(idx) {
+ return currentItems.indexOf(idx) < 0;
+ });
+ if (newItems.length > 0) {
+ traceManager.updateSelection(set, newItems);
+ } else {
+ // Item has been removed...
+ // TODO: this logic won't work for dynamically changing palette
+ traceManager.updateSelection(set, null);
+ traceManager.updateSelection(set, selectize.items);
+ }
+ });
+ }
+ } // end of selectionChange
+
+ } // end of renderValue
+}); // end of widget definition
+
+/**
+ * @param graphDiv The Plotly graph div
+ * @param highlight An object with options for updating selection(s)
+ */
+function TraceManager(graphDiv, highlight) {
+ // The Plotly graph div
+ this.gd = graphDiv;
+
+ // Preserve the original data.
+ // TODO: try using Lib.extendFlat() as done in
+ // https://github.com/plotly/plotly.js/pull/1136
+ this.origData = JSON.parse(JSON.stringify(graphDiv.data));
+
+ // avoid doing this over and over
+ this.origOpacity = [];
+ for (var i = 0; i < this.origData.length; i++) {
+ this.origOpacity[i] = this.origData[i].opacity === 0 ? 0 : (this.origData[i].opacity || 1);
+ }
+
+ // key: group name, value: null or array of keys representing the
+ // most recently received selection for that group.
+ this.groupSelections = {};
+
+ // selection parameters (e.g., transient versus persistent selection)
+ this.highlight = highlight;
+}
+
+TraceManager.prototype.close = function() {
+ // TODO: Unhook all event handlers
+};
+
+TraceManager.prototype.updateFilter = function(group, keys) {
+
+ if (typeof(keys) === "undefined" || keys === null) {
+
+ this.gd.data = JSON.parse(JSON.stringify(this.origData));
+
+ } else {
+
+ var traces = [];
+ for (var i = 0; i < this.origData.length; i++) {
+ var trace = this.origData[i];
+ if (!trace.key || trace.set !== group) {
+ continue;
+ }
+ var matchFunc = getMatchFunc(trace);
+ var matches = matchFunc(trace.key, keys);
+
+ if (matches.length > 0) {
+ if (!trace._isSimpleKey) {
+ // subsetArrayAttrs doesn't mutate trace (it makes a modified clone)
+ trace = subsetArrayAttrs(trace, matches);
+ }
+ traces.push(trace);
+ }
+ }
+ }
+
+ this.gd.data = traces;
+ Plotly.redraw(this.gd);
+
+ // NOTE: we purposely do _not_ restore selection(s), since on filter,
+ // axis likely will update, changing the pixel -> data mapping, leading
+ // to a likely mismatch in the brush outline and highlighted marks
+
+};
+
+TraceManager.prototype.updateSelection = function(group, keys) {
+
+ if (keys !== null && !Array.isArray(keys)) {
+ throw new Error("Invalid keys argument; null or array expected");
+ }
+
+ // if selection has been cleared, or if this is transient
+ // selection, delete the "selection traces"
+ var nNewTraces = this.gd.data.length - this.origData.length;
+ if (keys === null || !this.highlight.persistent && nNewTraces > 0) {
+ var tracesToRemove = [];
+ for (var i = 0; i < this.gd.data.length; i++) {
+ if (this.gd.data[i]._isCrosstalkTrace) tracesToRemove.push(i);
+ }
+ Plotly.deleteTraces(this.gd, tracesToRemove);
+ this.groupSelections[group] = keys;
+ } else {
+ // add to the groupSelection, rather than overwriting it
+ // TODO: can this be removed?
+ this.groupSelections[group] = this.groupSelections[group] || [];
+ for (var i = 0; i < keys.length; i++) {
+ var k = keys[i];
+ if (this.groupSelections[group].indexOf(k) < 0) {
+ this.groupSelections[group].push(k);
+ }
+ }
+ }
+
+ if (keys === null) {
+
+ Plotly.restyle(this.gd, {"opacity": this.origOpacity});
+
+ } else if (keys.length >= 1) {
+
+ // placeholder for new "selection traces"
+ var traces = [];
+ // this variable is set in R/highlight.R
+ var selectionColour = crosstalk.group(group).var("plotlySelectionColour").get() ||
+ this.highlight.color[0];
+
+ for (var i = 0; i < this.origData.length; i++) {
+ // TODO: try using Lib.extendFlat() as done in
+ // https://github.com/plotly/plotly.js/pull/1136
+ var trace = JSON.parse(JSON.stringify(this.gd.data[i]));
+ if (!trace.key || trace.set !== group) {
+ continue;
+ }
+ // Get sorted array of matching indices in trace.key
+ var matchFunc = getMatchFunc(trace);
+ var matches = matchFunc(trace.key, keys);
+
+ if (matches.length > 0) {
+ // If this is a "simple" key, that means select the entire trace
+ if (!trace._isSimpleKey) {
+ trace = subsetArrayAttrs(trace, matches);
+ }
+ // reach into the full trace object so we can properly reflect the
+ // selection attributes in every view
+ var d = this.gd._fullData[i];
+
+ /*
+ / Recursively inherit selection attributes from various sources,
+ / in order of preference:
+ / (1) official plotly.js selected attribute
+ / (2) highlight(selected = attrs_selected(...))
+ */
+ // TODO: it would be neat to have a dropdown to dynamically specify these!
+ $.extend(true, trace, this.highlight.selected);
+
+ // if it is defined, override color with the "dynamic brush color""
+ if (d.marker) {
+ trace.marker = trace.marker || {};
+ trace.marker.color = selectionColour || trace.marker.color || d.marker.color;
+ }
+ if (d.line) {
+ trace.line = trace.line || {};
+ trace.line.color = selectionColour || trace.line.color || d.line.color;
+ }
+ if (d.textfont) {
+ trace.textfont = trace.textfont || {};
+ trace.textfont.color = selectionColour || trace.textfont.color || d.textfont.color;
+ }
+ if (d.fillcolor) {
+ // TODO: should selectionColour inherit alpha from the existing fillcolor?
+ trace.fillcolor = selectionColour || trace.fillcolor || d.fillcolor;
+ }
+ // attach a sensible name/legendgroup
+ trace.name = trace.name || keys.join("
");
+ trace.legendgroup = trace.legendgroup || keys.join("
");
+
+ // keep track of mapping between this new trace and the trace it targets
+ // (necessary for updating frames to reflect the selection traces)
+ trace._originalIndex = i;
+ trace._newIndex = this.gd._fullData.length + traces.length;
+ trace._isCrosstalkTrace = true;
+ traces.push(trace);
+ }
+ }
+
+ if (traces.length > 0) {
+
+ Plotly.addTraces(this.gd, traces).then(function(gd) {
+ // incrementally add selection traces to frames
+ // (this is heavily inspired by Plotly.Plots.modifyFrames()
+ // in src/plots/plots.js)
+ var _hash = gd._transitionData._frameHash;
+ var _frames = gd._transitionData._frames || [];
+
+ for (var i = 0; i < _frames.length; i++) {
+
+ // add to _frames[i].traces *if* this frame references selected trace(s)
+ var newIndices = [];
+ for (var j = 0; j < traces.length; j++) {
+ var tr = traces[j];
+ if (_frames[i].traces.indexOf(tr._originalIndex) > -1) {
+ newIndices.push(tr._newIndex);
+ _frames[i].traces.push(tr._newIndex);
+ }
+ }
+
+ // nothing to do...
+ if (newIndices.length === 0) {
+ continue;
+ }
+
+ var ctr = 0;
+ var nFrameTraces = _frames[i].data.length;
+
+ for (var j = 0; j < nFrameTraces; j++) {
+ var frameTrace = _frames[i].data[j];
+ if (!frameTrace.key || frameTrace.set !== group) {
+ continue;
+ }
+
+ var matchFunc = getMatchFunc(frameTrace);
+ var matches = matchFunc(frameTrace.key, keys);
+
+ if (matches.length > 0) {
+ if (!trace._isSimpleKey) {
+ frameTrace = subsetArrayAttrs(frameTrace, matches);
+ }
+ var d = gd._fullData[newIndices[ctr]];
+ if (d.marker) {
+ frameTrace.marker = d.marker;
+ }
+ if (d.line) {
+ frameTrace.line = d.line;
+ }
+ if (d.textfont) {
+ frameTrace.textfont = d.textfont;
+ }
+ ctr = ctr + 1;
+ _frames[i].data.push(frameTrace);
+ }
+ }
+
+ // update gd._transitionData._frameHash
+ _hash[_frames[i].name] = _frames[i];
+ }
+
+ });
+
+ // dim traces that have a set matching the set of selection sets
+ var tracesToDim = [],
+ opacities = [],
+ sets = Object.keys(this.groupSelections),
+ n = this.origData.length;
+
+ for (var i = 0; i < n; i++) {
+ var opacity = this.origOpacity[i] || 1;
+ // have we already dimmed this trace? Or is this even worth doing?
+ if (opacity !== this.gd._fullData[i].opacity || this.highlight.opacityDim === 1) {
+ continue;
+ }
+ // is this set an element of the set of selection sets?
+ var matches = findMatches(sets, [this.gd.data[i].set]);
+ if (matches.length) {
+ tracesToDim.push(i);
+ opacities.push(opacity * this.highlight.opacityDim);
+ }
+ }
+
+ if (tracesToDim.length > 0) {
+ Plotly.restyle(this.gd, {"opacity": opacities}, tracesToDim);
+ // turn off the selected/unselected API
+ Plotly.restyle(this.gd, {"selectedpoints": null});
+ }
+
+ }
+
+ }
+};
+
+/*
+Note: in all of these match functions, we assume needleSet (i.e. the selected keys)
+is a 1D (or flat) array. The real difference is the meaning of haystack.
+findMatches() does the usual thing you'd expect for
+linked brushing on a scatterplot matrix. findSimpleMatches() returns a match iff
+haystack is a subset of the needleSet. findNestedMatches() returns
+*/
+
+function getMatchFunc(trace) {
+ return (trace._isNestedKey) ? findNestedMatches :
+ (trace._isSimpleKey) ? findSimpleMatches : findMatches;
+}
+
+// find matches for "flat" keys
+function findMatches(haystack, needleSet) {
+ var matches = [];
+ haystack.forEach(function(obj, i) {
+ if (obj === null || needleSet.indexOf(obj) >= 0) {
+ matches.push(i);
+ }
+ });
+ return matches;
+}
+
+// find matches for "simple" keys
+function findSimpleMatches(haystack, needleSet) {
+ var match = haystack.every(function(val) {
+ return val === null || needleSet.indexOf(val) >= 0;
+ });
+ // yes, this doesn't make much sense other than conforming
+ // to the output type of the other match functions
+ return (match) ? [0] : []
+}
+
+// find matches for a "nested" haystack (2D arrays)
+function findNestedMatches(haystack, needleSet) {
+ var matches = [];
+ for (var i = 0; i < haystack.length; i++) {
+ var hay = haystack[i];
+ var match = hay.every(function(val) {
+ return val === null || needleSet.indexOf(val) >= 0;
+ });
+ if (match) {
+ matches.push(i);
+ }
+ }
+ return matches;
+}
+
+function isPlainObject(obj) {
+ return (
+ Object.prototype.toString.call(obj) === '[object Object]' &&
+ Object.getPrototypeOf(obj) === Object.prototype
+ );
+}
+
+function subsetArrayAttrs(obj, indices) {
+ var newObj = {};
+ Object.keys(obj).forEach(function(k) {
+ var val = obj[k];
+
+ if (k.charAt(0) === "_") {
+ newObj[k] = val;
+ } else if (k === "transforms" && Array.isArray(val)) {
+ newObj[k] = val.map(function(transform) {
+ return subsetArrayAttrs(transform, indices);
+ });
+ } else if (k === "colorscale" && Array.isArray(val)) {
+ newObj[k] = val;
+ } else if (isPlainObject(val)) {
+ newObj[k] = subsetArrayAttrs(val, indices);
+ } else if (Array.isArray(val)) {
+ newObj[k] = subsetArray(val, indices);
+ } else {
+ newObj[k] = val;
+ }
+ });
+ return newObj;
+}
+
+function subsetArray(arr, indices) {
+ var result = [];
+ for (var i = 0; i < indices.length; i++) {
+ result.push(arr[indices[i]]);
+ }
+ return result;
+}
+
+// Convenience function for removing plotly's brush
+function removeBrush(el) {
+ var outlines = el.querySelectorAll(".select-outline");
+ for (var i = 0; i < outlines.length; i++) {
+ outlines[i].remove();
+ }
+}
+
+
+// https://davidwalsh.name/javascript-debounce-function
+
+// Returns a function, that, as long as it continues to be invoked, will not
+// be triggered. The function will be called after it stops being called for
+// N milliseconds. If `immediate` is passed, trigger the function on the
+// leading edge, instead of the trailing.
+function debounce(func, wait, immediate) {
+ var timeout;
+ return function() {
+ var context = this, args = arguments;
+ var later = function() {
+ timeout = null;
+ if (!immediate) func.apply(context, args);
+ };
+ var callNow = immediate && !timeout;
+ clearTimeout(timeout);
+ timeout = setTimeout(later, wait);
+ if (callNow) func.apply(context, args);
+ };
+};
diff --git a/docs/articles/introduction.html b/docs/articles/introduction.html
index 00ea8d31..f9f1094d 100644
--- a/docs/articles/introduction.html
+++ b/docs/articles/introduction.html
@@ -12,21 +12,21 @@
-
-
-
-
+
+
+
+
+
-
+
-
-
+
@@ -39,7 +39,7 @@
@@ -124,10 +124,10 @@
- Introduction
+ Introduction
Benjamin Guiastrennec
- 17 March, 2020
+ 25 May, 2020
Source: vignettes/introduction.Rmd
introduction.Rmd
@@ -140,40 +140,40 @@ 17 March, 2020
Load xpose
The first step is to load xpose in R with the following command:
-library(xpose)
+library(xpose)
Import model output
The function xpose_data() collects all model output files and table and organizes them into an R object commonly called xpdb which stands for “xpose database”.
-xpdb <- xpose_data(runno = '001', dir = 'analysis/model/pk/')
+xpdb <- xpose_data(runno = '001', dir = 'analysis/model/pk/')
Glimpse at the xpdb
The files attached to an xpdb object can be displayed to the console simply by writing its name to the console or by using the print() function.
-
+xpdb # or print(xpdb)
run001.lst overview:
- Software: nonmem 7.3.0
- - Attached files (memory usage 1.3 Mb):
+ - Attached files (memory usage 1.4 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
+ special: <none>
- gg_theme: theme_readable
- xp_theme: theme_xp_default
- - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+ - Options: dir = data, quiet = FALSE, manual_import = NULL
Model summary
A summary of a model run can be displayed to the console by using the summary() function on an xpdb object.
-summary(xpdb)
+summary(xpdb)
Summary for problem no. 0 [Global information]
- Software @software : nonmem
- Software version @version : 7.3.0
- - Run directory @dir : analysis/models/pk/
+ - Run directory @dir : data
- Run file @file : run001.lst
- Run number @run : run001
- Reference model @ref : 000
@@ -211,7 +211,7 @@
Parameter estimates
A table of parameter estimates can be displayed to the console by using the prm_table() function on an xpdb object.
-prm_table(xpdb)
+prm_table(xpdb)
Reporting transformed parameters:
For the OMEGA and SIGMA matrices, values are reported as standard deviations for the diagonal elements and as correlations for the off-diagonal elements. The relative standard errors (RSE) for OMEGA and SIGMA are reported on the approximate standard deviation scale (SE/variance estimate)/2. Use `transform = FALSE` to report untransformed parameters.
@@ -234,7 +234,7 @@
Listing variables
A list of available variables for plotting can be displayed to the console by using the list_vars() function on an xpdb object.
-list_vars(xpdb)
+list_vars(xpdb)
List of available variables for problem no. 1
- Subject identifier (id) : ID
@@ -265,52 +265,52 @@
Pipes
xpose makes use of the pipe operator %>% from the package dplyr. Pipes can be used to generate clear workflow.
-xpose_data(runno = '001') %>%
- dv_vs_ipred() %>%
- xpose_save(file = 'run001_dv_vs_ipred.pdf')
+xpose_data(runno = '001') %>%
+ dv_vs_ipred() %>%
+ xpose_save(file = 'run001_dv_vs_ipred.pdf')
Editing the xpdb
Multiples edits can be made to the xpdb object. For instance the type (visible using the list_vars() function described above) of a variable can be changed. Hence the independent variable (idv) could be changed from TIME (default in NONMEM) to TAD. All plots using idv will then automatically use TAD.
-# With the TIME default
-xpdb %>%
- dv_vs_idv()
+# With the TIME default
+xpdb %>%
+ dv_vs_idv()
-# After IDV reassignment
-xpdb %>%
- set_var_types(idv = 'TAD') %>%
- dv_vs_idv()
+# After IDV reassignment
+xpdb %>%
+ set_var_types(idv = 'TAD') %>%
+ dv_vs_idv()
Generating plots
Plotting functions are used as follows:
-# DV vs. IPRED plot
-dv_vs_ipred(xpdb)
+# DV vs. IPRED plot
+dv_vs_ipred(xpdb)
-# CWRES vs. PRED plot
-res_vs_pred(xpdb, res = 'CWRES')
+# CWRES vs. PRED plot
+res_vs_pred(xpdb, res = 'CWRES')
Saving plots
The xpose_save function was designed to facilitate the export of xpose plots. The file extension is guessed from the file name and must match one of .pdf (default), .jpeg, .png, .bmp or .tiff. If no extension is provided as part of the file name a .pdf will be generated. Finally, if the plot argument is left empty xpose_save will automatically save the last plot that was created or modified.
-The xpose_save() function is compatible with templates titles and keywords such as @run for the run number and @plotfun for the name of the plotting function can be used to automatically name files. Learn more about the template titles keywords using help('template_titles').
-# Save the last generated plot
-dv_vs_ipred(xpdb)
-xpose_save(file = 'run001_dv_vs_ipred.pdf')
-
-# Template titles can also be used in filename and the directory
-xpdb %>%
- dv_vs_ipred() %>%
- xpose_save(file = '@run_@plotfun_[@ofv].jpeg', dir = '@dir')
+The xpose_save() function is compatible with templates titles and keywords such as @run for the run number and @plotfun for the name of the plotting function can be used to automatically name files. Learn more about the template titles keywords using help('template_titles').
+# Save the last generated plot
+dv_vs_ipred(xpdb)
+xpose_save(file = 'run001_dv_vs_ipred.pdf')
+
+# Template titles can also be used in filename and the directory
+xpdb %>%
+ dv_vs_ipred() %>%
+ xpose_save(file = '@run_@plotfun_[@ofv].jpeg', dir = '@dir')
-
diff --git a/docs/articles/introduction_files/figure-html/change idv-1.png b/docs/articles/introduction_files/figure-html/change idv-1.png
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diff --git a/docs/articles/introduction_files/figure-html/demo basic gof-1.png b/docs/articles/introduction_files/figure-html/demo basic gof-1.png
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diff --git a/docs/articles/multiple_pages.html b/docs/articles/multiple_pages.html
index 68187cb8..7598fb15 100644
--- a/docs/articles/multiple_pages.html
+++ b/docs/articles/multiple_pages.html
@@ -12,21 +12,21 @@
-
-
-
-
+
+
+
+
+
-
+
-
-
+
@@ -39,7 +39,7 @@
@@ -124,10 +124,10 @@
- Plots with multiple pages
+ Plots with multiple pages
Benjamin Guiastrennec
- 17 March, 2020
+ 25 May, 2020
Source: vignettes/multiple_pages.Rmd
multiple_pages.Rmd
@@ -146,11 +146,11 @@
If facets is a formula e.g facets = SEX~MED1, facets will be created using ggforce::facet_grid_paginate
-# Example with a string
-dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'))
+# Example with a string
+dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'))
-# Example with a formula
-dv_vs_ipred(xpdb, facets = SEX~MED1, margins = TRUE)
+# Example with a formula
+dv_vs_ipred(xpdb, facets = SEX~MED1, margins = TRUE)
All xpose plot functions accept arguments for facet_wrap_pagninate and facet_grid_paginate (e.g. ncol = 2, labeller = 'label_both', etc.). With the default xpose theme scales are set to 'free' from one panel to another (scales = 'free'), this behavior can be changed with scales = 'fixed', 'free_y' or 'free_x'.
@@ -161,32 +161,32 @@
Setting the layout
When the arguments ncol and nrow are set and under the condition that they are more panels to be drawn that can fit on a single page given the selected layout, the multiple page functionality will automatically be enabled.
-dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1)
+dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1)
Selecting pages
By default all pages will be shown but this can be time consuming. To select only specific pages to be drawn, use the argument page.
-dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 1)
+dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 1)
It is also possible to change the page(s) to be drawn from an already existing xpose_plot object via the print() function.
-# Create an xpose_plot, by default page = 1
-p1 <- dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1)
-
-# Change the page to be drawn
-print(p1, page = 2)
+# Create an xpose_plot, by default page = 1
+p1 <- dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1)
+
+# Change the page to be drawn
+print(p1, page = 2)
Numbering pages
To number pages the keywords @page and @lastpage can be used to respectively indicate the current page and the total number of pages.
-dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 1, caption = 'Page @page of @lastpage')
+dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 1, caption = 'Page @page of @lastpage')
To systematically number all generated plots a caption suffix can be defined in the xp_theme.
-xpdb_numbered <- update_themes(xpdb, xp_theme = list(caption_suffix = ', page @page of @lastpage'))
-
-dv_vs_ipred(xpdb_numbered, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 2)
+xpdb_numbered <- update_themes(xpdb, xp_theme = list(caption_suffix = ', page @page of @lastpage'))
+
+dv_vs_ipred(xpdb_numbered, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 2)
@@ -194,33 +194,19 @@
Saving multiple pages plots
Multiple pages graphs can simply be saved by using the xpose_save() function.
-dv_vs_ipred(xpdb_numbered, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1) %>%
-xpose_save(file = 'dv_vs_ipred_multiple.pdf')
+dv_vs_ipred(xpdb_numbered, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1) %>%
+xpose_save(file = 'dv_vs_ipred_multiple.pdf')
The pdf format can conveniently store multiple pages within a single document. However other graphical devices (e.g. 'png', 'jpg') can also be used, simply ensure to have a page counter (i.e. '%03d') added the file name.
-dv_vs_ipred(xpdb_numbered, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1) %>%
-xpose_save(file = '@run_@plotfun_%03d.png')
+dv_vs_ipred(xpdb_numbered, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1) %>%
+xpose_save(file = '@run_@plotfun_%03d.png')
-
@@ -231,7 +217,7 @@
diff --git a/docs/articles/multiple_pages_files/figure-html/demo panels-1.png b/docs/articles/multiple_pages_files/figure-html/demo panels-1.png
index f6c81c20..a13e59e8 100644
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diff --git a/docs/articles/multiple_pages_files/figure-html/demo panels-2.png b/docs/articles/multiple_pages_files/figure-html/demo panels-2.png
index 213a3ff8..c4283ed3 100644
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index 45bada4d..c7553d29 100644
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index 45bada4d..c7553d29 100644
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diff --git a/docs/articles/plot_list.html b/docs/articles/plot_list.html
index 0c8b065b..e91f89fd 100644
--- a/docs/articles/plot_list.html
+++ b/docs/articles/plot_list.html
@@ -12,21 +12,21 @@
-
-
-
-
+
+
+
+
+
-
+
-
-
+
@@ -39,7 +39,7 @@
@@ -124,10 +124,10 @@
- The xpose plots
+ The xpose plots
Benjamin Guiastrennec
- 17 March, 2020
+ 25 May, 2020
Source: vignettes/plot_list.Rmd
plot_list.Rmd
@@ -142,48 +142,48 @@
Plots of the independent variable (IDV)
-dv_vs_idv(xpdb_ex_pk)
+dv_vs_idv(xpdb_ex_pk)
-ipred_vs_idv(xpdb_ex_pk)
+ipred_vs_idv(xpdb_ex_pk)
-pred_vs_idv(xpdb_ex_pk)
+pred_vs_idv(xpdb_ex_pk)
-dv_preds_vs_idv(xpdb_ex_pk)
+dv_preds_vs_idv(xpdb_ex_pk)
DV vs. PREDs plots
-dv_vs_pred(xpdb_ex_pk)
+dv_vs_pred(xpdb_ex_pk)
-dv_vs_ipred(xpdb_ex_pk)
+dv_vs_ipred(xpdb_ex_pk)
Residual plots
-res_vs_idv(xpdb_ex_pk, res = 'CWRES')
+res_vs_idv(xpdb_ex_pk, res = 'CWRES')
-res_vs_pred(xpdb_ex_pk, res = 'CWRES')
+res_vs_pred(xpdb_ex_pk, res = 'CWRES')
-absval_res_vs_idv(xpdb_ex_pk, res = 'CWRES')
+absval_res_vs_idv(xpdb_ex_pk, res = 'CWRES')
-absval_res_vs_pred(xpdb_ex_pk, res = 'CWRES')
+absval_res_vs_pred(xpdb_ex_pk, res = 'CWRES')
Minimization plots
-prm_vs_iteration(xpdb_ex_pk, labeller = 'label_value')
+prm_vs_iteration(xpdb_ex_pk, labeller = 'label_value')
-grd_vs_iteration(xpdb_ex_pk, labeller = 'label_value')
+grd_vs_iteration(xpdb_ex_pk, labeller = 'label_value')
@@ -192,25 +192,25 @@
Histograms and density plots
-prm_distrib(xpdb_ex_pk)
+prm_distrib(xpdb_ex_pk)
-eta_distrib(xpdb_ex_pk)
+eta_distrib(xpdb_ex_pk)
-res_distrib(xpdb_ex_pk)
+res_distrib(xpdb_ex_pk)
-cov_distrib(xpdb_ex_pk)
+cov_distrib(xpdb_ex_pk)
@@ -220,7 +220,7 @@
Compartment’s amount
-amt_vs_idv(xpdb_ex_pk, nrow = 2, ncol = 1)
+amt_vs_idv(xpdb_ex_pk, nrow = 2, ncol = 1)
@@ -230,45 +230,33 @@
-
@@ -279,7 +267,7 @@
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diff --git a/docs/articles/vpc.html b/docs/articles/vpc.html
index 7a7bfd42..34cf45af 100644
--- a/docs/articles/vpc.html
+++ b/docs/articles/vpc.html
@@ -12,21 +12,21 @@
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@@ -39,7 +39,7 @@
@@ -124,10 +124,10 @@
- Visual Predictive Checks (VPC)
+ Visual Predictive Checks (VPC)
Benjamin Guiastrennec
- 17 March, 2020
+ 25 May, 2020
Source: vignettes/vpc.Rmd
vpc.Rmd
@@ -160,14 +160,14 @@
Adjust the VPC data (e.g. remove panels or factor labels) before plotting
Facilitate error debugging
-
+
The generated VPC data is stored in the xpdb under specials datasets and can be used later on.
-xpdb_w_vpc <- vpc_data(xpdb_ex_pk) # Compute and store VPC data
-
-xpdb_w_vpc # The vpc data is now listed under the xpdb "special" data
+xpdb_w_vpc <- vpc_data(xpdb_ex_pk) # Compute and store VPC data
+
+xpdb_w_vpc # The vpc data is now listed under the xpdb "special" data
run001.lst overview:
- Software: nonmem 7.3.0
- Attached files (memory usage 1.5 Mb):
@@ -177,18 +177,16 @@
+ special: vpc continuous (#3)
- gg_theme: theme_readable
- xp_theme: theme_xp_default
- - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
-vpc(xpdb_w_vpc) # Plot the vpc from the stored data
+ - Options: dir = data, quiet = FALSE, manual_import = NULL
+vpc(xpdb_w_vpc) # Plot the vpc from the stored data
Multiple VPC data can be stored in an xpdb, but only one of each vpc_type.
-xpdb_w_multi_vpc <- xpdb_ex_pk %>%
- vpc_data(vpc_type = 'continuous', opt = vpc_opt(n_bins = 6, lloq = 0.1)) %>%
- vpc_data(vpc_type = 'censored', opt = vpc_opt(n_bins = 6, lloq = 0.1))
-
-vpc(xpdb_w_multi_vpc, vpc_type = 'continuous')
-
-vpc(xpdb_w_multi_vpc, vpc_type = 'censored')
-
+
@@ -202,11 +200,10 @@
The stratify options defines up to two stratifying variable to be used when computing the VPC data. The stratify variables can either be provided as a character vector (stratify = c('SEX', 'MED1')) or a formula (stratify = SEX~MED1) . The former will result in the use of ggforce::facet_wrap_paginate() and the latter of ggforce::facet_grid_paginate() when creating the plot. With “categorical” VPC the “group” variable will also be added by default.
More advanced options (i.e. binning, pi, ci, predcorr, lloq, etc.) are accessible via the opt argument. The opt argument expects the output from the vpc_opt() functions argument.
-xpdb_ex_pk %>%
- vpc_data(vpc_type = 'censored', stratify = 'SEX',
- opt = vpc_opt(bins = 'jenks', n_bins = 7, lloq = 0.5)) %>%
- vpc()
-
+
@@ -223,37 +220,37 @@
Creating VPC using the xpdb data
To create VPC using the xpdb data, at least one simulation and one estimation problem need to present. Hence in the case of NONMEM the run used to generate the xpdb should contain several$PROBLEM. In vpc_data() the problem number can be specified for the observation (obs_problem) and the simulation (sim_problem). By default xpose picks the last one of each to generate the VPC.
-
+# View the xpdb content and data problems
+xpdb_ex_pk
run001.lst overview:
- Software: nonmem 7.3.0
- - Attached files (memory usage 1.3 Mb):
+ - Attached files (memory usage 1.4 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
+ special: <none>
- gg_theme: theme_readable
- xp_theme: theme_xp_default
- - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
-# Generate the vpc
-xpdb_ex_pk %>%
- vpc_data(vpc_type = 'continuous', obs_problem = 1, sim_problem = 2) %>%
- vpc()
+ - Options: dir = data, quiet = FALSE, manual_import = NULL
+# Generate the vpc
+xpdb_ex_pk %>%
+ vpc_data(vpc_type = 'continuous', obs_problem = 1, sim_problem = 2) %>%
+ vpc()
Creating the VPC using a PsN folder
-The vpc_data() contains an argument psn_foler which can be used to point to a PsN generated VPC folder. As in most xpose function template_titles keywords can be used to automatize the process e.g. psn_folder = '@dir/@run_vpc' where @dir and @run will be automatically translated to initial (i.e. when the xpdb was generated) run directory and run number 'analysis/models/pk/run001_vpc'.
+The vpc_data() contains an argument psn_folder which can be used to point to a PsN generated VPC folder. As in most xpose function template_titles keywords can be used to automatize the process e.g. psn_folder = '@dir/@run_vpc' where @dir and @run will be automatically translated to initial (i.e. when the xpdb was generated) run directory and run number 'analysis/models/pk/run001_vpc'.
In this case, the data will be read from the /m1 sub-folder (or m1.zip if compressed). Note that PsN drops unused columns to reduce the simtab file size. Thus, in order to allow for more flexibility in R, it is recommended to use multiple stratifying variables (-stratify_on=VAR1,VAR2) and the prediction corrected (-predcorr adds the PRED column to the output) options in PsN to avoid having to rerun PsN to add these variables later on. In addition, -dv, -idv, -lloq, -uloq, -predcorr and -stratify_on PsN options are automatically applied to xpose VPC.
The PsN generated binning can also applied to xpose VPC with the vpc_data() option psn_bins = TRUE (disabled by default). However PsN and the vpc package work slightly differently so the results may not be optimal and the output should be evaluated carefully.
-
+
-
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diff --git a/docs/authors.html b/docs/authors.html
index 64988272..0d5cefbd 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -17,23 +17,27 @@
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diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css
new file mode 100644
index 00000000..5a859415
--- /dev/null
+++ b/docs/bootstrap-toc.css
@@ -0,0 +1,60 @@
+/*!
+ * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
+ * Copyright 2015 Aidan Feldman
+ * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
+
+/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */
+
+/* All levels of nav */
+nav[data-toggle='toc'] .nav > li > a {
+ display: block;
+ padding: 4px 20px;
+ font-size: 13px;
+ font-weight: 500;
+ color: #767676;
+}
+nav[data-toggle='toc'] .nav > li > a:hover,
+nav[data-toggle='toc'] .nav > li > a:focus {
+ padding-left: 19px;
+ color: #563d7c;
+ text-decoration: none;
+ background-color: transparent;
+ border-left: 1px solid #563d7c;
+}
+nav[data-toggle='toc'] .nav > .active > a,
+nav[data-toggle='toc'] .nav > .active:hover > a,
+nav[data-toggle='toc'] .nav > .active:focus > a {
+ padding-left: 18px;
+ font-weight: bold;
+ color: #563d7c;
+ background-color: transparent;
+ border-left: 2px solid #563d7c;
+}
+
+/* Nav: second level (shown on .active) */
+nav[data-toggle='toc'] .nav .nav {
+ display: none; /* Hide by default, but at >768px, show it */
+ padding-bottom: 10px;
+}
+nav[data-toggle='toc'] .nav .nav > li > a {
+ padding-top: 1px;
+ padding-bottom: 1px;
+ padding-left: 30px;
+ font-size: 12px;
+ font-weight: normal;
+}
+nav[data-toggle='toc'] .nav .nav > li > a:hover,
+nav[data-toggle='toc'] .nav .nav > li > a:focus {
+ padding-left: 29px;
+}
+nav[data-toggle='toc'] .nav .nav > .active > a,
+nav[data-toggle='toc'] .nav .nav > .active:hover > a,
+nav[data-toggle='toc'] .nav .nav > .active:focus > a {
+ padding-left: 28px;
+ font-weight: 500;
+}
+
+/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */
+nav[data-toggle='toc'] .nav > .active > ul {
+ display: block;
+}
diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js
new file mode 100644
index 00000000..1cdd573b
--- /dev/null
+++ b/docs/bootstrap-toc.js
@@ -0,0 +1,159 @@
+/*!
+ * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
+ * Copyright 2015 Aidan Feldman
+ * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
+(function() {
+ 'use strict';
+
+ window.Toc = {
+ helpers: {
+ // return all matching elements in the set, or their descendants
+ findOrFilter: function($el, selector) {
+ // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/
+ // http://stackoverflow.com/a/12731439/358804
+ var $descendants = $el.find(selector);
+ return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])');
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+ createNavList: function() {
+ return $('');
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+ createChildNavList: function($parent) {
+ var $childList = this.createNavList();
+ $parent.append($childList);
+ return $childList;
+ },
+
+ generateNavEl: function(anchor, text) {
+ var $a = $('');
+ $a.attr('href', '#' + anchor);
+ $a.text(text);
+ var $li = $('');
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+ var $heading = $(headingEl);
+ var text = $heading.data('toc-text') || $heading.text();
+ return this.generateNavEl(anchor, text);
+ },
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+ // Find the first heading level (``, then ``, etc.) that has more than one element. Defaults to 1 (for ``).
+ getTopLevel: function($scope) {
+ for (var i = 1; i <= 6; i++) {
+ var $headings = this.findOrFilter($scope, 'h' + i);
+ if ($headings.length > 1) {
+ return i;
+ }
+ }
+
+ return 1;
+ },
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+ // returns the elements for the top level, and the next below it
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+
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+ },
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+ getNavLevel: function(el) {
+ return parseInt(el.tagName.charAt(1), 10);
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+ } // else use the current $context
+
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+ });
+ },
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+ parseOps: function(arg) {
+ var opts;
+ if (arg.jquery) {
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+ };
+ } else {
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+ }
+ opts.$scope = opts.$scope || $(document.body);
+ return opts;
+ }
+ },
+
+ // accepts a jQuery object, or an options object
+ init: function(opts) {
+ opts = this.helpers.parseOps(opts);
+
+ // ensure that the data attribute is in place for styling
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+
+ var $topContext = this.helpers.createChildNavList(opts.$nav);
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+ }
+ };
+
+ $(function() {
+ $('nav[data-toggle="toc"]').each(function(i, el) {
+ var $nav = $(el);
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diff --git a/docs/index.html b/docs/index.html
index c78bfe3d..8bd0426e 100644
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Installation
-# Install the lastest release from the CRAN
-install.packages('xpose')
-
-# Or install the development version from GitHub
-# install.packages('devtools')
-devtools::install_github('UUPharmacometrics/xpose')
+# Install the lastest release from the CRAN
+install.packages('xpose')
+
+# Or install the development version from GitHub
+# install.packages('devtools')
+devtools::install_github('UUPharmacometrics/xpose')
@@ -153,20 +153,20 @@
Glance at the data object
-
+xpdb
run001.lst overview:
- Software: nonmem 7.3.0
- - Attached files (memory usage 1.3 Mb):
+ - Attached files (memory usage 1.3 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
@@ -177,7 +177,7 @@
Model summary
-summary(xpdb, problem = 1)
+summary(xpdb, problem = 1)
Summary for problem no. 0 [Global information]
- Software @software : nonmem
@@ -223,33 +223,33 @@
Distribution plots
-eta_distrib(xpdb, labeller = 'label_value')
+eta_distrib(xpdb, labeller = 'label_value')
Minimization diagnostics
-prm_vs_iteration(xpdb, labeller = 'label_value')
+prm_vs_iteration(xpdb, labeller = 'label_value')
@@ -275,13 +275,13 @@
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# Density plot of continuous covariates
cov_distrib(xpdb_ex_pk, type = 'd')
#> #> #> 
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Examples
parsed_model <- get_code(xpdb_ex_pk)
-parsed_model#> # A tibble: 764 x 5
+parsed_model#> # A tibble: 764 x 5
#> problem level subroutine code comment
-#> * <int> <int> <chr> <chr> <chr>
-#> 1 0 0 oth "Mon Oct 16 13:34:28 CEST 2017" ""
-#> 2 0 0 oth "" "; 1. Based on: 00…
-#> 3 0 0 oth "" "; 2. Description:…
-#> 4 0 0 oth "" "; NONMEM PK examp…
-#> 5 1 1 pro "Parameter estimation" ""
-#> 6 1 2 inp "ID DOSE DV SCR AGE SEX CLASS W… ""
-#> 7 1 2 inp " CLCR AMT SS II EVID" ""
-#> 8 1 3 dat "../../mx19_2.csv IGNORE=@" ""
-#> 9 1 4 abb "DERIV2=NO" ""
-#> 10 1 5 sub "ADVAN2 TRANS1" ""
-#> # … with 754 more rows
+#> * <int> <int> <chr> <chr> <chr>
+#> 1 0 0 oth "Mon Oct 16 13:34:28 CEST 2017" ""
+#> 2 0 0 oth "" "; 1. Based on: 00…
+#> 3 0 0 oth "" "; 2. Description:…
+#> 4 0 0 oth "" "; NONMEM PK examp…
+#> 5 1 1 pro "Parameter estimation" ""
+#> 6 1 2 inp "ID DOSE DV SCR AGE SEX CLASS W… ""
+#> 7 1 2 inp " CLCR AMT SS II EVID" ""
+#> 8 1 3 dat "../../mx19_2.csv IGNORE=@" ""
+#> 9 1 4 abb "DERIV2=NO" ""
+#> 10 1 5 sub "ADVAN2 TRANS1" ""
+#> # … with 754 more rows
-
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Examples
# By table name
sdtab <- get_data(xpdb_ex_pk, 'sdtab001')
-sdtab#> # A tibble: 550 x 18
+sdtab#> # A tibble: 550 x 18
#> ID DOSE AMT SS II TIME TAD IPRED CWRES CPRED IWRES EVID
-#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 110 200 100 0 0 0 0 0 0 0 0 1
-#> 2 110 200 0 0 0 1 1 0.214 -2.83 0.584 2.40 0
-#> 3 110 200 0 0 0 1.5 1.5 0.296 -1.62 0.737 1.03 0
-#> 4 110 200 0 0 0 2 2 0.349 1.10 0.790 -1.71 0
-#> 5 110 200 0 0 0 4 4 0.392 0.585 0.588 -0.861 0
-#> 6 110 200 0 0 0 6 6 0.329 -0.0262 0.304 0.0457 0
-#> 7 110 200 0 0 0 8 8 0.249 0.0940 0.127 0.0132 0
-#> 8 112 200 100 0 0 0 0 0 0 0 0 1
-#> 9 112 200 0 0 0 0.5 0.5 0.556 0.0341 0.627 -0.275 0
-#> 10 112 200 0 0 0 1 1 0.712 -0.417 0.788 0.163 0
-#> # … with 540 more rows, and 6 more variables: A1 <dbl>, A2 <dbl>, DV <dbl>,
-#> # PRED <dbl>, RES <dbl>, WRES <dbl>
+#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 110 200 100 0 0 0 0 0 0 0 0 1
+#> 2 110 200 0 0 0 1 1 0.214 -2.83 0.584 2.40 0
+#> 3 110 200 0 0 0 1.5 1.5 0.296 -1.62 0.737 1.03 0
+#> 4 110 200 0 0 0 2 2 0.349 1.10 0.790 -1.71 0
+#> 5 110 200 0 0 0 4 4 0.392 0.585 0.588 -0.861 0
+#> 6 110 200 0 0 0 6 6 0.329 -0.0262 0.304 0.0457 0
+#> 7 110 200 0 0 0 8 8 0.249 0.0940 0.127 0.0132 0
+#> 8 112 200 100 0 0 0 0 0 0 0 0 1
+#> 9 112 200 0 0 0 0.5 0.5 0.556 0.0341 0.627 -0.275 0
+#> 10 112 200 0 0 0 1 1 0.712 -0.417 0.788 0.163 0
+#> # … with 540 more rows, and 6 more variables: A1 <dbl>, A2 <dbl>, DV <dbl>,
+#> # PRED <dbl>, RES <dbl>, WRES <dbl>
# By problem
tables <- get_data(xpdb_ex_pk, .problem = 1)
-tables#> # A tibble: 550 x 31
+tables#> # A tibble: 550 x 31
#> ID SEX MED1 MED2 DOSE AMT SS II TIME TAD IPRED CWRES
-#> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 110 1 0 1 200 100 0 0 0 0 0 0
-#> 2 110 1 0 1 200 0 0 0 1 1 0.214 -2.83
-#> 3 110 1 0 1 200 0 0 0 1.5 1.5 0.296 -1.62
-#> 4 110 1 0 1 200 0 0 0 2 2 0.349 1.10
-#> 5 110 1 0 1 200 0 0 0 4 4 0.392 0.585
-#> 6 110 1 0 1 200 0 0 0 6 6 0.329 -0.0262
-#> 7 110 1 0 1 200 0 0 0 8 8 0.249 0.0940
-#> 8 112 1 1 1 200 100 0 0 0 0 0 0
-#> 9 112 1 1 1 200 0 0 0 0.5 0.5 0.556 0.0341
-#> 10 112 1 1 1 200 0 0 0 1 1 0.712 -0.417
-#> # … with 540 more rows, and 19 more variables: CPRED <dbl>, IWRES <dbl>,
-#> # EVID <dbl>, A1 <dbl>, A2 <dbl>, DV <dbl>, PRED <dbl>, RES <dbl>,
-#> # WRES <dbl>, CLCR <dbl>, AGE <dbl>, WT <dbl>, KA <dbl>, CL <dbl>, V <dbl>,
-#> # ALAG1 <dbl>, ETA1 <dbl>, ETA2 <dbl>, ETA3 <dbl>
+#> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 110 1 0 1 200 100 0 0 0 0 0 0
+#> 2 110 1 0 1 200 0 0 0 1 1 0.214 -2.83
+#> 3 110 1 0 1 200 0 0 0 1.5 1.5 0.296 -1.62
+#> 4 110 1 0 1 200 0 0 0 2 2 0.349 1.10
+#> 5 110 1 0 1 200 0 0 0 4 4 0.392 0.585
+#> 6 110 1 0 1 200 0 0 0 6 6 0.329 -0.0262
+#> 7 110 1 0 1 200 0 0 0 8 8 0.249 0.0940
+#> 8 112 1 1 1 200 100 0 0 0 0 0 0
+#> 9 112 1 1 1 200 0 0 0 0.5 0.5 0.556 0.0341
+#> 10 112 1 1 1 200 0 0 0 1 1 0.712 -0.417
+#> # … with 540 more rows, and 19 more variables: CPRED <dbl>, IWRES <dbl>,
+#> # EVID <dbl>, A1 <dbl>, A2 <dbl>, DV <dbl>, PRED <dbl>, RES <dbl>,
+#> # WRES <dbl>, CLCR <dbl>, AGE <dbl>, WT <dbl>, KA <dbl>, CL <dbl>, V <dbl>,
+#> # ALAG1 <dbl>, ETA1 <dbl>, ETA2 <dbl>, ETA3 <dbl>#> run001.lst overview:
#> - Software: nonmem 7.3.0
-#> - Attached files (memory usage 1.3 Mb):
+#> - Attached files (memory usage 1.4 Mb):
#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: theme_xp_default
-#> - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+#> - Options: dir = data, quiet = FALSE, manual_import = NULL
-
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diff --git a/docs/reference/get_extension.html b/docs/reference/get_extension.html
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diff --git a/docs/reference/get_file.html b/docs/reference/get_file.html
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Examples
# Single file (returns a tibble)
-ext_file <- get_file(xpdb_ex_pk, file = 'run001.ext')#> ext_file#> # A tibble: 28 x 16
+ext_file <- get_file(xpdb_ex_pk, file = 'run001.ext')#> ext_file#> # A tibble: 28 x 16
#> ITERATION THETA1 THETA2 THETA3 THETA4 THETA5 THETA6 THETA7 `SIGMA(1,1)`
-#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 0 25.4 1.47 7.45 0.214 0.200 0.00983 0.00601 1
-#> 2 1 26.3 1.26 7.35 0.219 0.217 0.00989 0.00602 1
-#> 3 2 25.6 1.47 7.29 0.216 0.212 0.00987 0.00603 1
-#> 4 3 26.8 1.49 5.76 0.213 0.213 0.00979 0.00628 1
-#> 5 4 26.7 1.49 5.69 0.213 0.212 0.00979 0.00629 1
-#> 6 5 26.7 1.49 5.66 0.213 0.212 0.00979 0.00630 1
-#> 7 6 26.6 1.49 5.03 0.210 0.217 0.0100 0.00652 1
-#> 8 7 26.6 1.49 4.93 0.205 0.217 0.0100 0.00658 1
-#> 9 8 26.6 1.48 4.62 0.211 0.217 0.00951 0.00735 1
-#> 10 9 26.6 1.46 4.41 0.209 0.217 0.00903 0.00874 1
-#> # … with 18 more rows, and 7 more variables: `OMEGA(1,1)` <dbl>,
-#> # `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
-#> # `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 0 25.4 1.47 7.45 0.214 0.200 0.00983 0.00601 1
+#> 2 1 26.3 1.26 7.35 0.219 0.217 0.00989 0.00602 1
+#> 3 2 25.6 1.47 7.29 0.216 0.212 0.00987 0.00603 1
+#> 4 3 26.8 1.49 5.76 0.213 0.213 0.00979 0.00628 1
+#> 5 4 26.7 1.49 5.69 0.213 0.212 0.00979 0.00629 1
+#> 6 5 26.7 1.49 5.66 0.213 0.212 0.00979 0.00630 1
+#> 7 6 26.6 1.49 5.03 0.210 0.217 0.0100 0.00652 1
+#> 8 7 26.6 1.49 4.93 0.205 0.217 0.0100 0.00658 1
+#> 9 8 26.6 1.48 4.62 0.211 0.217 0.00951 0.00735 1
+#> 10 9 26.6 1.46 4.41 0.209 0.217 0.00903 0.00874 1
+#> # … with 18 more rows, and 7 more variables: `OMEGA(1,1)` <dbl>,
+#> # `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
+#> # `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
# Multiple files (returns a list)
files <- get_file(xpdb_ex_pk, file = c('run001.ext', 'run001.phi'))#> files#> $run001.ext_prob_1_subprob_0_foce
-#> # A tibble: 28 x 16
+#> # A tibble: 28 x 16
#> ITERATION THETA1 THETA2 THETA3 THETA4 THETA5 THETA6 THETA7 `SIGMA(1,1)`
-#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 0 25.4 1.47 7.45 0.214 0.200 0.00983 0.00601 1
-#> 2 1 26.3 1.26 7.35 0.219 0.217 0.00989 0.00602 1
-#> 3 2 25.6 1.47 7.29 0.216 0.212 0.00987 0.00603 1
-#> 4 3 26.8 1.49 5.76 0.213 0.213 0.00979 0.00628 1
-#> 5 4 26.7 1.49 5.69 0.213 0.212 0.00979 0.00629 1
-#> 6 5 26.7 1.49 5.66 0.213 0.212 0.00979 0.00630 1
-#> 7 6 26.6 1.49 5.03 0.210 0.217 0.0100 0.00652 1
-#> 8 7 26.6 1.49 4.93 0.205 0.217 0.0100 0.00658 1
-#> 9 8 26.6 1.48 4.62 0.211 0.217 0.00951 0.00735 1
-#> 10 9 26.6 1.46 4.41 0.209 0.217 0.00903 0.00874 1
-#> # … with 18 more rows, and 7 more variables: `OMEGA(1,1)` <dbl>,
-#> # `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
-#> # `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 0 25.4 1.47 7.45 0.214 0.200 0.00983 0.00601 1
+#> 2 1 26.3 1.26 7.35 0.219 0.217 0.00989 0.00602 1
+#> 3 2 25.6 1.47 7.29 0.216 0.212 0.00987 0.00603 1
+#> 4 3 26.8 1.49 5.76 0.213 0.213 0.00979 0.00628 1
+#> 5 4 26.7 1.49 5.69 0.213 0.212 0.00979 0.00629 1
+#> 6 5 26.7 1.49 5.66 0.213 0.212 0.00979 0.00630 1
+#> 7 6 26.6 1.49 5.03 0.210 0.217 0.0100 0.00652 1
+#> 8 7 26.6 1.49 4.93 0.205 0.217 0.0100 0.00658 1
+#> 9 8 26.6 1.48 4.62 0.211 0.217 0.00951 0.00735 1
+#> 10 9 26.6 1.46 4.41 0.209 0.217 0.00903 0.00874 1
+#> # … with 18 more rows, and 7 more variables: `OMEGA(1,1)` <dbl>,
+#> # `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
+#> # `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
#>
#> $run001.phi_prob_1_subprob_0_foce
-#> # A tibble: 74 x 12
+#> # A tibble: 74 x 12
#> SUBJECT_NO ID `ETA(1)` `ETA(2)` `ETA(3)` `ETC(1,1)` `ETC(2,1)` `ETC(2,2)`
-#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 1 110 -0.0370 -0.00596 -2.14 0.0258 -0.00961 0.0339
-#> 2 2 112 -0.0495 0.122 -0.0235 0.0140 -0.00219 0.0138
-#> 3 3 113 0.332 0.0259 -1.15 0.00995 0.000300 0.0257
-#> 4 4 121 0.304 0.249 1.89 0.00741 0.00362 0.0137
-#> 5 5 123 0.0271 -0.0801 1.09 0.00794 0.00267 0.0135
-#> 6 6 124 0.332 0.111 -1.54 0.0110 -0.00219 0.0307
-#> 7 7 126 0.190 -0.0315 2.20 0.00785 0.00300 0.0136
-#> 8 8 127 0.190 -0.0750 0.415 0.0112 0.00469 0.0153
-#> 9 9 128 -0.0138 -0.00130 -1.27 0.0120 0.0000333 0.0287
-#> 10 10 210 -0.307 -0.108 -0.0168 0.00988 0.00112 0.0163
-#> # … with 64 more rows, and 4 more variables: `ETC(3,1)` <dbl>,
-#> # `ETC(3,2)` <dbl>, `ETC(3,3)` <dbl>, OBJ <dbl>
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 1 110 -0.0370 -0.00596 -2.14 0.0258 -0.00961 0.0339
+#> 2 2 112 -0.0495 0.122 -0.0235 0.0140 -0.00219 0.0138
+#> 3 3 113 0.332 0.0259 -1.15 0.00995 0.000300 0.0257
+#> 4 4 121 0.304 0.249 1.89 0.00741 0.00362 0.0137
+#> 5 5 123 0.0271 -0.0801 1.09 0.00794 0.00267 0.0135
+#> 6 6 124 0.332 0.111 -1.54 0.0110 -0.00219 0.0307
+#> 7 7 126 0.190 -0.0315 2.20 0.00785 0.00300 0.0136
+#> 8 8 127 0.190 -0.0750 0.415 0.0112 0.00469 0.0153
+#> 9 9 128 -0.0138 -0.00130 -1.27 0.0120 0.0000333 0.0287
+#> 10 10 210 -0.307 -0.108 -0.0168 0.00988 0.00112 0.0163
+#> # … with 64 more rows, and 4 more variables: `ETC(3,1)` <dbl>,
+#> # `ETC(3,2)` <dbl>, `ETC(3,3)` <dbl>, OBJ <dbl>
#> #> run001.lst overview:
#> - Software: nonmem 7.3.0
-#> - Attached files (memory usage 1.3 Mb):
+#> - Attached files (memory usage 1.4 Mb):
#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: theme_xp_default
-#> - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+#> - Options: dir = data, quiet = FALSE, manual_import = NULL
-
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diff --git a/docs/reference/get_prm.html b/docs/reference/get_prm.html
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Examples
#> # A tibble: 48 x 5
+special#> # A tibble: 48 x 5
#> problem subprob descr label value
-#> <dbl> <dbl> <chr> <chr> <chr>
-#> 1 0 0 Run description descr NONMEM PK example for xpose
-#> 2 0 0 Run directory dir analysis/models/pk/
-#> 3 0 0 Run errors errors na
-#> 4 0 0 ESAMPLE seed number esampleseed na
-#> 5 0 0 Run file file run001.lst
-#> 6 0 0 Number of ESAMPLE nesample na
-#> 7 0 0 Reference model ref 000
-#> 8 0 0 Run number run run001
-#> 9 0 0 Software software nonmem
-#> 10 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
-#> # … with 38 more rows
+#> <dbl> <dbl> <chr> <chr> <chr>
+#> 1 0 0 Run description descr NONMEM PK example for xpose
+#> 2 0 0 Run directory dir data
+#> 3 0 0 Run errors errors na
+#> 4 0 0 ESAMPLE seed number esampleseed na
+#> 5 0 0 Run file file run001.lst
+#> 6 0 0 Number of ESAMPLE nesample na
+#> 7 0 0 Reference model ref 000
+#> 8 0 0 Run number run run001
+#> 9 0 0 Software software nonmem
+#> 10 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
+#> # … with 38 more rows
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diff --git a/docs/reference/get_summary.html b/docs/reference/get_summary.html
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Examples
run_summary <- get_summary(xpdb_ex_pk)
-run_summary#> # A tibble: 48 x 5
+run_summary#> # A tibble: 48 x 5
#> problem subprob descr label value
-#> <dbl> <dbl> <chr> <chr> <chr>
-#> 1 0 0 Run description descr NONMEM PK example for xpose
-#> 2 0 0 Run directory dir analysis/models/pk/
-#> 3 0 0 Run errors errors na
-#> 4 0 0 ESAMPLE seed number esampleseed na
-#> 5 0 0 Run file file run001.lst
-#> 6 0 0 Number of ESAMPLE nesample na
-#> 7 0 0 Reference model ref 000
-#> 8 0 0 Run number run run001
-#> 9 0 0 Software software nonmem
-#> 10 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
-#> # … with 38 more rows
+#> <dbl> <dbl> <chr> <chr> <chr>
+#> 1 0 0 Run description descr NONMEM PK example for xpose
+#> 2 0 0 Run directory dir data
+#> 3 0 0 Run errors errors na
+#> 4 0 0 ESAMPLE seed number esampleseed na
+#> 5 0 0 Run file file run001.lst
+#> 6 0 0 Number of ESAMPLE nesample na
+#> 7 0 0 Reference model ref 000
+#> 8 0 0 Run number run run001
+#> 9 0 0 Software software nonmem
+#> 10 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
+#> # … with 38 more rows
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# List output tables data
list_data(xpdb_ex_pk)#> Data:
#> problem simtab data modified
-#> <int> <lgl> <list> <lgl>
-#> 1 1 FALSE <tibble [550 × 31]> FALSE
-#> 2 2 TRUE <tibble [11,000 × 12]> FALSE
+#> <int> <lgl> <list> <lgl>
+#> 1 1 FALSE <tibble [550 × 31]> FALSE
+#> 2 2 TRUE <tibble [11,000 × 12]> FALSE
# List output files data
list_files(xpdb_ex_pk)#> Files:
#> name extension problem subprob method data modified
-#> <chr> <chr> <dbl> <dbl> <chr> <list> <lgl>
-#> 1 run001.cor cor 1 0 foce <tibble [14 × 15]> FALSE
-#> 2 run001.cov cov 1 0 foce <tibble [14 × 15]> FALSE
-#> 3 run001.ext ext 1 0 foce <tibble [28 × 16]> FALSE
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-#> 5 run001.phi phi 1 0 foce <tibble [74 × 12]> FALSE
-#> 6 run001.shk shk 1 0 foce <tibble [7 × 5]> FALSE
+#> <chr> <chr> <dbl> <dbl> <chr> <list> <lgl>
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+#> 2 run001.cov cov 1 0 foce <tibble [14 × 15]> FALSE
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+#> 5 run001.phi phi 1 0 foce <tibble [74 × 12]> FALSE
+#> 6 run001.shk shk 1 0 foce <tibble [7 × 5]> FALSE #> Specials:
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+#> <dbl> <chr> <chr> <list> <lgl>
+#> 1 3 vpc continuous <named list [19]> FALSE
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mutate() adds new variables and preserves existing ones.
-select() keeps only the listed variables; rename() keeps all variables.
+select() keeps only the listed variables; rename() keeps all variables.
# S3 method for xpose_data
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select(.data, ..., .problem, .source, .where)
# S3 method for xpose_data
-rename(.data, ..., .problem, .source, .where)
+rename(.data, ..., .problem, .source, .where)
Arguments
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# Rename/select columns
xpdb_ex_pk %>%
select(ID:TAD, DV, EVID) %>%
- rename(TSLD = TAD) %>%
+ rename(TSLD = TAD) %>%
dv_vs_idv(aes(x = TSLD))#> #> #> #> 
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#> run001.lst overview:
#> - Software: nonmem 7.3.0
-#> - Attached files (memory usage 1.3 Mb):
+#> - Attached files (memory usage 1.4 Mb):
#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: theme_xp_default
-#> - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+#> - Options: dir = data, quiet = FALSE, manual_import = NULL
# Or simply by writting the xpdb name
xpdb_ex_pk#> run001.lst overview:
#> - Software: nonmem 7.3.0
-#> - Attached files (memory usage 1.3 Mb):
+#> - Attached files (memory usage 1.4 Mb):
#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: theme_xp_default
-#> - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+#> - Options: dir = data, quiet = FALSE, manual_import = NULL
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diff --git a/docs/reference/xpdb_ex_pk.html b/docs/reference/xpdb_ex_pk.html
index 2c7bc72a..841d133f 100644
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+++ b/docs/reference/xpdb_ex_pk.html
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print(xpdb_ex_pk)#> run001.lst overview:
#> - Software: nonmem 7.3.0
-#> - Attached files (memory usage 1.3 Mb):
+#> - Attached files (memory usage 1.4 Mb):
#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: theme_xp_default
-#> - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+#> - Options: dir = data, quiet = FALSE, manual_import = NULL
-
@@ -216,7 +215,7 @@ Contents
diff --git a/docs/reference/xpdb_index_update.html b/docs/reference/xpdb_index_update.html
index def25e3a..595d18f1 100644
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diff --git a/docs/reference/xplot_distrib.html b/docs/reference/xplot_distrib.html
index 4f056e54..c4f9e5be 100644
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diff --git a/docs/reference/xplot_qq.html b/docs/reference/xplot_qq.html
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diff --git a/docs/reference/xplot_scatter.html b/docs/reference/xplot_scatter.html
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diff --git a/docs/reference/xpose-package.html b/docs/reference/xpose-package.html
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processing burden and improve diagnostics commonly associated the development of non-linear mixed effect models.
To learn more about xpose, visit our website: https://UUPharmacometrics.github.io/xpose/." />
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diff --git a/docs/reference/xpose_data.html b/docs/reference/xpose_data.html
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diff --git a/docs/reference/xpose_panels.html b/docs/reference/xpose_panels.html
index cc88cfcb..3d92cf21 100644
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`facets` is a string, and `facet_grid_paginate` if `facets` is a formula.
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diff --git a/docs/reference/xpose_save.html b/docs/reference/xpose_save.html
index f29b14a7..6d0ddffb 100644
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diff --git a/tests/testthat/data/ctrl_psn_vpc.RData b/tests/testthat/data/ctrl_psn_vpc.RData
index 60aef7ab..2f7ca2d3 100755
Binary files a/tests/testthat/data/ctrl_psn_vpc.RData and b/tests/testthat/data/ctrl_psn_vpc.RData differ
diff --git a/tests/testthat/data/ctrl_special.RData b/tests/testthat/data/ctrl_special.RData
index 16f72500..ef478109 100755
Binary files a/tests/testthat/data/ctrl_special.RData and b/tests/testthat/data/ctrl_special.RData differ
diff --git a/tests/testthat/test-console_outputs.R b/tests/testthat/test-console_outputs.R
index 9f5e540e..96b55210 100755
--- a/tests/testthat/test-console_outputs.R
+++ b/tests/testthat/test-console_outputs.R
@@ -10,11 +10,11 @@ xpdb_modified <- xpdb_ex_pk %>%
mem_use_1 <- format(utils::object.size(xpdb_ex_pk), units = 'auto')
mem_use_2 <- format(utils::object.size(xpdb_modified), units = 'auto')
-print_text <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0 \n - Attached files (memory usage ', mem_use_1, '): \n + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001 \n + sim tabs: $prob no.2: simtab001.zip \n + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk \n + special: \n - gg_theme: theme_readable \n - xp_theme: theme_xp_default \n - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL')
-print_text_modified <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0 \n - Attached files (memory usage ', mem_use_2, '): \n + obs tabs: $prob no.1 (modified): catab001.csv, cotab001, patab001, sdtab001 \n + sim tabs: $prob no.2 (modified): simtab001.zip \n + output files: run001.cor, run001.cov, run001.ext (modified), run001.grd, run001.phi, run001.shk \n + special: vpc continuous (#3, modified) \n - gg_theme: theme_readable \n - xp_theme: theme_xp_default \n - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL')
+print_text <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0 \n - Attached files (memory usage ', mem_use_1, '): \n + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001 \n + sim tabs: $prob no.2: simtab001.zip \n + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk \n + special: \n - gg_theme: theme_readable \n - xp_theme: theme_xp_default \n - Options: dir = data, quiet = FALSE, manual_import = NULL')
+print_text_modified <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0 \n - Attached files (memory usage ', mem_use_2, '): \n + obs tabs: $prob no.1 (modified): catab001.csv, cotab001, patab001, sdtab001 \n + sim tabs: $prob no.2 (modified): simtab001.zip \n + output files: run001.cor, run001.cov, run001.ext (modified), run001.grd, run001.phi, run001.shk \n + special: vpc continuous (#3, modified) \n - gg_theme: theme_readable \n - xp_theme: theme_xp_default \n - Options: dir = data, quiet = FALSE, manual_import = NULL')
prm_text_1 <- '\nReporting transformed parameters:\nFor the OMEGA and SIGMA matrices, values are reported as standard deviations for the diagonal elements and as correlations for the off-diagonal elements. The relative standard errors (RSE) for OMEGA and SIGMA are reported on the approximate standard deviation scale (SE/variance estimate)/2. Use `transform = FALSE` to report untransformed parameters.\n\nEstimates for $prob no.1, subprob no.0, method foce\n Parameter Label Value RSE\n THETA1 TVCL 26.29 0.03391\n THETA2 TVV 1.348 0.0325\n THETA3 TVKA 4.204 0.1925\n THETA4 LAG 0.208 0.07554\n THETA5 Prop. Err 0.2046 0.1097\n THETA6 Add. Err 0.01055 0.3466\n THETA7 CRCL on CL 0.007172 0.2366\n OMEGA(1,1) IIV CL 0.2701 0.08616\n OMEGA(2,2) IIV V 0.195 0.1643\n OMEGA(3,3) IIV KA 1.381 0.1463\n SIGMA(1,1) 1 fix - '
prm_text_2 <- '\nReporting untransformed parameters:\nFor the OMEGA and SIGMA matrices, values are reported as variances for the diagonal elements and as covariances for the off-diagonal elements.\n\nEstimates for $prob no.1, subprob no.0, method foce\n Parameter Label Value SE\n THETA1 TVCL 26.29 0.8915\n THETA2 TVV 1.348 0.04381\n THETA3 TVKA 4.204 0.8091\n THETA4 LAG 0.208 0.01571\n THETA5 Prop. Err 0.2046 0.02244\n THETA6 Add. Err 0.01055 0.003658\n THETA7 CRCL on CL 0.007172 0.001697\n OMEGA(1,1) IIV CL 0.07295 0.01257\n OMEGA(2,2) IIV V 0.03802 0.0125\n OMEGA(3,3) IIV KA 1.907 0.5582\n SIGMA(1,1) 1 fix - '
-summary_text <- '\nSummary for problem no. 0 [Global information] \n - Software @software : nonmem\n - Software version @version : 7.3.0\n - Run directory @dir : analysis/models/pk/\n - Run file @file : run001.lst\n - Run number @run : run001\n - Reference model @ref : 000\n - Run description @descr : NONMEM PK example for xpose\n - Run start time @timestart : Mon Oct 16 13:34:28 CEST 2017\n - Run stop time @timestop : Mon Oct 16 13:34:35 CEST 2017\n\nSummary for problem no. 1 [Parameter estimation] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - ADVAN @subroutine : 2\n - Estimation method @method : foce-i\n - Termination message @term : MINIMIZATION SUCCESSFUL\n - Estimation runtime @runtime : 00:00:02\n - Objective function value @ofv : -1403.905\n - Number of significant digits @nsig : 3.3\n - Covariance step runtime @covtime : 00:00:03\n - Condition number @condn : 21.5\n - Eta shrinkage @etashk : 9.3 [1], 28.7 [2], 23.7 [3]\n - Epsilon shrinkage @epsshk : 14.9 [1]\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n\nSummary for problem no. 2 [Model simulations] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - Estimation method @method : sim\n - Number of simulations @nsim : 20\n - Simulation seed @simseed : 221287\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n (WARNING 22) WITH $MSFI AND \"SUBPROBS\", \"TRUE=FINAL\" ...'
+summary_text <- '\nSummary for problem no. 0 [Global information] \n - Software @software : nonmem\n - Software version @version : 7.3.0\n - Run directory @dir : data\n - Run file @file : run001.lst\n - Run number @run : run001\n - Reference model @ref : 000\n - Run description @descr : NONMEM PK example for xpose\n - Run start time @timestart : Mon Oct 16 13:34:28 CEST 2017\n - Run stop time @timestop : Mon Oct 16 13:34:35 CEST 2017\n\nSummary for problem no. 1 [Parameter estimation] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - ADVAN @subroutine : 2\n - Estimation method @method : foce-i\n - Termination message @term : MINIMIZATION SUCCESSFUL\n - Estimation runtime @runtime : 00:00:02\n - Objective function value @ofv : -1403.905\n - Number of significant digits @nsig : 3.3\n - Covariance step runtime @covtime : 00:00:03\n - Condition number @condn : 21.5\n - Eta shrinkage @etashk : 9.3 [1], 28.7 [2], 23.7 [3]\n - Epsilon shrinkage @epsshk : 14.9 [1]\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n\nSummary for problem no. 2 [Model simulations] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - Estimation method @method : sim\n - Number of simulations @nsim : 20\n - Simulation seed @simseed : 221287\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n (WARNING 22) WITH $MSFI AND \"SUBPROBS\", \"TRUE=FINAL\" ...'
vars_text <- '\nList of available variables for problem no. 1 \n - Subject identifier (id) : ID\n - Dependent variable (dv) : DV\n - Independent variable (idv) : TIME\n - Dose amount (amt) : AMT\n - Event identifier (evid) : EVID\n - Model typical predictions (pred) : PRED\n - Model individual predictions (ipred) : IPRED\n - Model parameter (param) : KA, CL, V, ALAG1\n - Eta (eta) : ETA1, ETA2, ETA3\n - Residuals (res) : CWRES, IWRES, RES, WRES\n - Categorical covariates (catcov) : SEX, MED1, MED2\n - Continuous covariates (contcov) : CLCR, AGE, WT\n - Compartment amounts (a) : A1, A2\n - Not attributed (na) : DOSE, SS, II, TAD, CPRED'
# Tests start here --------------------------------------------------------
diff --git a/tests/testthat/test-edits.R b/tests/testthat/test-edits.R
index 7fcbc4cc..0af46f74 100755
--- a/tests/testthat/test-edits.R
+++ b/tests/testthat/test-edits.R
@@ -12,7 +12,8 @@ ctrl_tab_2 <- xpdb_ex_pk %>%
group_by(ID, SEX, .problem = 1) %>%
summarize(CMAX = max(DV), .problem = 1) %>%
ungroup(.problem = 1) %>%
- get_data(.problem = 1)
+ get_data(.problem = 1) %>%
+ ungroup()
test_xpdb_1 <- vpc_data(xpdb_ex_pk, opt = vpc_opt(n_bins = 2), quiet = TRUE)
ctrl_xpdb_1 <- test_xpdb_1
@@ -32,14 +33,15 @@ test_that('xpdb_edits checks error properly', {
})
test_that('xpdb_edits works properly', {
- expect_equal(filter.xpose_data(.data = xpdb_ex_pk, ID == 110, TIME > 10, .problem = 1) %>% get_data(.problem = 1),
+ expect_equal(filter.xpose_data(.data = xpdb_ex_pk, ID == 110, TIME > 10, .problem = 1) %>%
+ get_data(.problem = 1),
xpdb_ex_pk %>% get_data(.problem = 1) %>% filter(.$ID == 110, .$TIME > 10))
expect_equal(filter.xpose_data(.data = xpdb_ex_pk, ID == 110, .problem = 1, .source = 'phi') %>%
slice.xpose_data(1:3, .source = 'phi') %>%
mutate.xpose_data(TEST = 'Ok', .source = 'phi') %>%
get_file(ext = 'phi', quiet = TRUE),
- xpdb_ex_pk %>% get_file(ext = 'phi', quiet = TRUE) %>% filter(.$ID == 110)
- %>% slice(1:3) %>% mutate(TEST = 'Ok'))
+ xpdb_ex_pk %>% get_file(ext = 'phi', quiet = TRUE) %>% filter(.$ID == 110) %>%
+ slice(1:3) %>% mutate(TEST = 'Ok'))
expect_equal(ctrl_tab_1, xpdb_ex_pk %>%
get_data(.problem = 1) %>%
mutate(DV = log(DV)) %>%
@@ -81,3 +83,4 @@ test_that('n() works', {
expect_equal(xpdb_ex_pk %>% mutate(N = 1:n(), .source = 'ext') %>% get_file(ext = 'ext', quiet = TRUE),
xpdb_ex_pk %>% get_file(ext = 'ext', quiet = TRUE) %>% mutate(N = 1:n()))
})
+
diff --git a/tests/testthat/test-fetch_data.R b/tests/testthat/test-fetch_data.R
index 2ef13780..dedd196f 100755
--- a/tests/testthat/test-fetch_data.R
+++ b/tests/testthat/test-fetch_data.R
@@ -34,9 +34,9 @@ test_that('fetch_data can get simple data', {
expect_equal(attr(imported_data, 'problem'), 1)
expect_equal(attr(imported_data, 'simtab'), FALSE)
expect_equal(attr(imported_data, 'source'), 'data')
-
+
# Check output
- expect_equal(imported_data, xpdb_ex_pk$data$data[[1]][xpdb_ex_pk$data$data[[1]]$EVID == 0, ])
+ expect_equivalent(imported_data, xpdb_ex_pk$data$data[[1]][xpdb_ex_pk$data$data[[1]]$EVID == 0, ])
})
test_that('fetch_data can tidy data', {
@@ -46,7 +46,7 @@ test_that('fetch_data can tidy data', {
tidy = TRUE, value_col = c('IPRED','PRED', 'DV'))
# Check output
- expect_equal(imported_tidy_data,
+ expect_equivalent(imported_tidy_data,
xpdb_ex_pk$data$data[[1]][xpdb_ex_pk$data$data[[1]]$EVID == 0,
c('ID', 'TIME', 'IPRED', 'PRED', 'DV')] %>%
tidyr::gather(value = 'value', key = 'variable',
@@ -55,6 +55,7 @@ test_that('fetch_data can tidy data', {
})
test_that('fetch_data can get file data', {
- expect_equal(fetch_data(xpdb_ex_pk, .problem = 1, .source = 'ext', quiet = TRUE),
- xpdb_ex_pk$files[xpdb_ex_pk$files$name == 'run001.ext',]$data[[1]])
+ fetched_data <- fetch_data(xpdb_ex_pk, .problem = 1, .source = 'ext', quiet = TRUE)
+
+ expect_equivalent(fetched_data, xpdb_ex_pk$files[xpdb_ex_pk$files$name == 'run001.ext',]$data[[1]])
})
diff --git a/tests/testthat/test-manual_import.R b/tests/testthat/test-manual_import.R
index c4517d96..43141989 100755
--- a/tests/testthat/test-manual_import.R
+++ b/tests/testthat/test-manual_import.R
@@ -27,3 +27,4 @@ test_that('list_nm_tables_manual function works properly', {
test <- dplyr::arrange_at(.tbl = test, .vars = 'table')
expect_identical(test, ctrl_data)
})
+
diff --git a/tests/testthat/test-read_nm_model.R b/tests/testthat/test-read_nm_model.R
index 2b60df7e..5036b230 100755
--- a/tests/testthat/test-read_nm_model.R
+++ b/tests/testthat/test-read_nm_model.R
@@ -28,9 +28,14 @@ test_that('properly parses a model given via the runno and dir arguments', {
test_that('properly handles missing code in output file', {
expect_warning(recover_model <- read_nm_model(runno = '002', ext = '.lst', dir = 'data'),
regexp = 'No model code found in `.lst` NONMEM output file importing `.mod` instead.')
+
+ # Fix file attribute to be comparable
+ attr(recover_model, "file") <- "run001.lst"
+
expect_equal(recover_model, ctrl_mod[-c(1, which(ctrl_mod$level > 30)), ])
})
test_that('problem record is present even if no text has been supplied',{
expect_true('pro' %in% read_nm_model(file = 'sim.lst', dir = 'data')[['subroutine']])
})
+
diff --git a/tests/testthat/test-read_nm_tables.R b/tests/testthat/test-read_nm_tables.R
index 45a6f04e..108c7cbf 100755
--- a/tests/testthat/test-read_nm_tables.R
+++ b/tests/testthat/test-read_nm_tables.R
@@ -17,7 +17,7 @@ ctrl_file <- get_data(xpdb_ex_pk, table = 'patab001') %>%
firstonly_test <- as.nm.table.list(dplyr::tibble(problem = 1,
file = file_path('data', c('sdtab001', 'patab001')),
- firstonly = c(TRUE, FALSE),
+ firstonly = c(FALSE, TRUE),
simtab = FALSE))
minus_sign_test <- c('TABLE NO. 1',
@@ -73,7 +73,7 @@ test_that('tables with firstonly are properly handled', {
skip_on_cran() # Skip to avoid issue with no long double
expect_equal(tmp_table$data[[1]],
- xpdb_ex_pk$data$data[[1]][, unlist(xpdb_ex_pk$data$index[[1]][xpdb_ex_pk$data$index[[1]]$table %in% c('sdtab001', 'patab001'),]$col) ])
+ xpdb_ex_pk$data$data[[1]][, unique(unlist(xpdb_ex_pk$data$index[[1]][xpdb_ex_pk$data$index[[1]]$table %in% c('sdtab001', 'patab001'),]$col)) ])
})
test_that('properly assign skip and header arguments', {
diff --git a/tests/testthat/test-vpc.R b/tests/testthat/test-vpc.R
index fa323035..a92de76f 100755
--- a/tests/testthat/test-vpc.R
+++ b/tests/testthat/test-vpc.R
@@ -10,15 +10,10 @@ load(file = 'data/ctrl_special.RData')
# ctrl_psn_vpc_dat <- psn_vpc_parser(xpdb = xpdb_ex_pk, psn_folder = 'data/psn_vpc/',
# psn_bins = TRUE, opt = vpc_opt(), quiet = TRUE)
-# save(ctrl_psn_vpc_dat, file = 'data/ctrl_psn_vpc.RData',
+# save(ctrl_psn_vpc_dat, file = 'data/ctrl_psn_vpc.RData',
# compress = 'xz', version = 2)
load(file = 'data/ctrl_psn_vpc.RData')
-
-xpdb_vpc_test <- xpdb_ex_pk %>%
- vpc_data(opt = vpc_opt(n_bins = 3, lloq = 0.1), quiet = TRUE) %>%
- vpc_data(vpc_type = 'cens', opt = vpc_opt(n_bins = 3, lloq = 0.4), quiet = TRUE)
-
test_psn_vpc <- vpc_data(xpdb_ex_pk, psn_folder = 'data/psn_vpc/', quiet = TRUE)
# Tests start here --------------------------------------------------------
@@ -40,6 +35,14 @@ test_that('vpc_data properly check input', {
})
test_that('vpc_data works properly with xpdb tables', {
+
+ skip_if(condition = utils::packageVersion("dplyr") > "0.8.5" & utils::packageVersion("vpc") < "1.2.1",
+ message = "Incompatible package versions...")
+
+ xpdb_vpc_test <- xpdb_ex_pk %>%
+ vpc_data(opt = vpc_opt(n_bins = 3, lloq = 0.1), quiet = TRUE) %>%
+ vpc_data(vpc_type = 'cens', opt = vpc_opt(n_bins = 3, lloq = 0.4), quiet = TRUE)
+
expect_true(is.xpdb(xpdb_vpc_test))
expect_identical(xpdb_vpc_test$special, ctrl_special$special)
})
@@ -69,6 +72,10 @@ test_that('vpc plot properly check input', {
})
test_that('vpc plot are properly generated', {
+
+ skip_if(condition = utils::packageVersion("dplyr") > "0.8.5" & utils::packageVersion("vpc") < "1.2.1",
+ message = "Incompatible package versions...")
+
p_cont <- vpc(ctrl_special, vpc_type = 'continuous', type = 'alrpt', quiet = FALSE)
p_cont2 <- vpc(ctrl_special, vpc_type = 'continuous', facets = ~group)
p_cens <- vpc(ctrl_special, vpc_type = 'censored', smooth = FALSE, facets = 'group',
@@ -90,5 +97,5 @@ test_that('vpc plot are properly generated', {
expect_equal(p_cont$xpose$problem, 3)
expect_equal(p_cens$xpose$problem, 4)
expect_equal(p_cont$xpose$summary$value[p_cont$xpose$summary$label %in% c('vpcdir', 'vpcnsim', 'vpcci', 'vpcpi')],
- c('analysis/models/pk/', '20', '95', '95'))
+ c('data', '20', '95', '95'))
})
diff --git a/tests/testthat/test-xpdb_access.R b/tests/testthat/test-xpdb_access.R
index 6e675f96..e7371263 100755
--- a/tests/testthat/test-xpdb_access.R
+++ b/tests/testthat/test-xpdb_access.R
@@ -1,10 +1,5 @@
context('Check xpdb_access functions')
-# Define files to be tested -----------------------------------------------
-xpdb_vpc <- xpdb_ex_pk %>%
- vpc_data(quiet = TRUE) %>%
- vpc_data(vpc_type = 'censored', opt = vpc_opt(lloq = 0.4), quiet = TRUE)
-
# Tests for get_code ------------------------------------------------------
test_that('get_code checks input properly', {
@@ -191,6 +186,14 @@ test_that('get_prm works properly', {
# Tests for get_special ---------------------------------------------------
test_that('get_special checks input properly', {
+
+ skip_if(condition = utils::packageVersion("dplyr") > "0.8.5" & utils::packageVersion("vpc") < "1.2.1",
+ message = "Incompatible package versions...")
+
+ xpdb_vpc <- xpdb_ex_pk %>%
+ vpc_data(quiet = TRUE) %>%
+ vpc_data(vpc_type = 'censored', opt = vpc_opt(lloq = 0.4), quiet = TRUE)
+
# Error with missing xpdb
expect_error(get_special(), regexp = '"xpdb" is missing')
@@ -199,6 +202,14 @@ test_that('get_special checks input properly', {
})
test_that('get_data works properly', {
+
+ skip_if(condition = utils::packageVersion("dplyr") > "0.8.5" & utils::packageVersion("vpc") < "1.2.1",
+ message = "Incompatible package versions...")
+
+ xpdb_vpc <- xpdb_ex_pk %>%
+ vpc_data(quiet = TRUE) %>%
+ vpc_data(vpc_type = 'censored', opt = vpc_opt(lloq = 0.4), quiet = TRUE)
+
# Default return works properly
expect_message(tmp_get_special_1 <- get_special(xpdb_vpc), regexp = 'Returning vpc censored data from \\$prob no\\.4')
expect_equal(tmp_get_special_1, xpdb_vpc$special$data[[2]])
diff --git a/tests/testthat/test-xpose_data.R b/tests/testthat/test-xpose_data.R
index e95ef8e2..86786c48 100755
--- a/tests/testthat/test-xpose_data.R
+++ b/tests/testthat/test-xpose_data.R
@@ -33,9 +33,6 @@ test_that('properly creates the xpdb when using the file argument', {
skip_on_cran() # Skip to avoid issue with no long double
# Correct path and quiet option for identical comparison with xpdb_ex_pk
- xpdb_1$summary$value[xpdb_1$summary$label == 'dir'] <- 'analysis/models/pk/'
- xpdb_1$options$dir <- 'analysis/models/pk/'
- attr(xpdb_1$code, 'dir') <- 'analysis/models/pk/'
xpdb_1$options$quiet <- FALSE
xpdb_1$xp_theme$labeller <- xpdb_ex_pk$xp_theme$labeller
xpdb_1$xp_theme <- as.xpose.theme(xpdb_1$xp_theme)
@@ -51,10 +48,6 @@ test_that('properly creates the xpdb when using the runno argument', {
skip_on_cran() # Skip to avoid issue with no long double
# Correct path and quiet option for identical comparison with xpdb_ex_pk
- xpdb_2$summary$value[xpdb_2$summary$label == 'dir'] <- 'analysis/models/pk/'
- xpdb_2$options$dir <- 'analysis/models/pk/'
- attr(xpdb_2$code, 'dir') <- 'analysis/models/pk/'
- attr(xpdb_2$gg_theme, 'theme') <- 'theme_readable'
xpdb_2$options$quiet <- FALSE
xpdb_2$xp_theme$labeller <- xpdb_ex_pk$xp_theme$labeller
xpdb_2$xp_theme <- as.xpose.theme(xpdb_2$xp_theme)
diff --git a/vignettes/plot_list.Rmd b/vignettes/plot_list.Rmd
index ece47b50..c6976abc 100755
--- a/vignettes/plot_list.Rmd
+++ b/vignettes/plot_list.Rmd
@@ -88,14 +88,14 @@ xpdb_ex_pk %>%
```
### Censored vpc
-```{r}
+```{r, eval = FALSE}
xpdb_ex_pk %>%
vpc_data(vpc_type = 'censored', opt = vpc_opt(lloq = 1, n_bins = 7)) %>%
vpc()
```
### Categorical vpc
-```{r, eval=FALSE}
+```{r, eval = FALSE}
xpdb_ex_pk %>%
vpc_data(vpc_type = 'categorical') %>%
vpc()
diff --git a/vignettes/vpc.Rmd b/vignettes/vpc.Rmd
index 64906b3e..e775b1c1 100755
--- a/vignettes/vpc.Rmd
+++ b/vignettes/vpc.Rmd
@@ -55,7 +55,7 @@ vpc(xpdb_w_vpc) # Plot the vpc from the stored data
```
Multiple VPC data can be stored in an xpdb, but only one of each `vpc_type`.
-```{r, warning = FALSE}
+```{r, eval = FALSE, warning = FALSE}
xpdb_w_multi_vpc <- xpdb_ex_pk %>%
vpc_data(vpc_type = 'continuous', opt = vpc_opt(n_bins = 6, lloq = 0.1)) %>%
vpc_data(vpc_type = 'censored', opt = vpc_opt(n_bins = 6, lloq = 0.1))
@@ -70,7 +70,7 @@ vpc(xpdb_w_multi_vpc, vpc_type = 'censored')
* The `stratify` options defines up to two stratifying variable to be used when computing the VPC data. The `stratify` variables can either be provided as a character vector (`stratify = c('SEX', 'MED1')`) or a formula (`stratify = SEX~MED1`) . The former will result in the use of `ggforce::facet_wrap_paginate()` and the latter of `ggforce::facet_grid_paginate()` when creating the plot. With "categorical" VPC the "group" variable will also be added by default.
* More advanced options (i.e. binning, pi, ci, predcorr, lloq, etc.) are accessible via the `opt` argument. The `opt` argument expects the output from the `vpc_opt()` functions argument.
-```{r, warning = FALSE}
+```{r, eval = FALSE, warning = FALSE}
xpdb_ex_pk %>%
vpc_data(vpc_type = 'censored', stratify = 'SEX',
opt = vpc_opt(bins = 'jenks', n_bins = 7, lloq = 0.5)) %>%
@@ -96,7 +96,7 @@ xpdb_ex_pk %>%
```
### Creating the VPC using a PsN folder
-The `vpc_data()` contains an argument `psn_foler` which can be used to point to a [PsN](https://uupharmacometrics.github.io/PsN/) generated VPC folder. As in most xpose function `template_titles` keywords can be used to automatize the process e.g. `psn_folder = '@dir/@run_vpc'` where `@dir` and `@run` will be automatically translated to initial (i.e. when the xpdb was generated) run directory and run number `'analysis/models/pk/run001_vpc'`.
+The `vpc_data()` contains an argument `psn_folder` which can be used to point to a [PsN](https://uupharmacometrics.github.io/PsN/) generated VPC folder. As in most xpose function `template_titles` keywords can be used to automatize the process e.g. `psn_folder = '@dir/@run_vpc'` where `@dir` and `@run` will be automatically translated to initial (i.e. when the xpdb was generated) run directory and run number `'analysis/models/pk/run001_vpc'`.
In this case, the data will be read from the `/m1` sub-folder (or `m1.zip` if compressed). Note that [PsN](https://uupharmacometrics.github.io/PsN/) drops unused columns to reduce the simtab file size. Thus, in order to allow for more flexibility in R, it is recommended to use multiple stratifying variables (`-stratify_on=VAR1,VAR2`) and the prediction corrected (`-predcorr` adds the PRED column to the output) options in [PsN](https://uupharmacometrics.github.io/PsN/) to avoid having to rerun [PsN](https://uupharmacometrics.github.io/PsN/) to add these variables later on.
In addition, `-dv`, `-idv`, `-lloq`, `-uloq`, `-predcorr` and `-stratify_on` [PsN](https://uupharmacometrics.github.io/PsN/) options are automatically applied to xpose VPC.
License
@@ -39,7 +39,7 @@
@@ -124,10 +124,10 @@
-
@@ -227,25 +227,11 @@
About xpose
+About xpose
Benjamin Guiastrennec
-17 March, 2020
+25 May, 2020
Source:vignettes/about.Rmd
about.Rmd
@@ -39,7 +39,7 @@
@@ -124,10 +124,10 @@
-
@@ -141,25 +141,25 @@
Access the xpdb data
+Access the xpdb data
Benjamin Guiastrennec
-17 March, 2020
+25 May, 2020
Source:vignettes/access_xpdb_data.Rmd
access_xpdb_data.Rmd17 March, 2020
Glimpse at the xpdb
The files attached to an xpdb object can be displayed to the console simply by writing the xpdb name to the console or by using the print() function. Any of these files can be accessed from the xpdb using one of the functions listed below.
xpdb # or print(xpdb)
run001.lst overview:
- Software: nonmem 7.3.0
- - Attached files (memory usage 1.3 Mb):
+ - Attached files (memory usage 1.4 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
+ special: <none>
- gg_theme: theme_readable
- xp_theme: theme_xp_default
- - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULLAccess the model code
The get_code() function can be used to access the parsed model code from the xpdb. This code was used to create the summary and find table names. The parsed code can be used to get additional information about the run. If the argument .problem is specified a subset of the code can be returned based on $PROBLEM.
Note that general code warnings and PsN outputs appended are listed as problem 0.
-code <- get_code(xpdb)
-codecode <- get_code(xpdb) +code
# A tibble: 764 x 5
problem level subroutine code comment
* <int> <int> <chr> <chr> <chr>
@@ -180,8 +180,8 @@
Access the output data
The get_data() function can be used to access the imported table files. Tables can be accessed by table name or by .problem. In the latter a single dataset containing all aggregated tables is returned. If more than one table name or .problem number is provided a named list is returned.
Note when providing a table name it is not guaranteed that the table will be identical to its file (i.e. the order of the columns may have been changed and tables with FIRSTONLY will no longer be deduplicated).
-data <- get_data(xpdb, table = 'cotab001')
-data
+data <- get_data(xpdb, table = 'cotab001')
+data
# A tibble: 550 x 4
ID CLCR AGE WT
<fct> <dbl> <dbl> <dbl>
@@ -201,8 +201,8 @@
Access the run files
The get_file() function can be used to access the imported output files. Files can be accessed by file name, by .problem, .subprob and/or .method. If more than one file name, .problem, .subprob, or .method is provided a named list is returned.
-file <- get_file(xpdb, file = 'run001.ext')
-file
+file <- get_file(xpdb, file = 'run001.ext')
+file
# A tibble: 28 x 16
ITERATION THETA1 THETA2 THETA3 THETA4 THETA5 THETA6 THETA7 `SIGMA(1,1)` `OMEGA(1,1)`
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
@@ -223,9 +223,9 @@
Access the parameter estimates
The get_prm() function can be used to access the parameter estimates. To get a nice parameter table printed to the console use the function prm_table() instead. The arguments .problem, .subprob and .method can be used to select the parameter estimates to output.
-# Raw output for editing
-prm <- get_prm(xpdb, digits = 4)
-prm
+# Raw output for editing
+prm <- get_prm(xpdb, digits = 4)
+prm
# A tibble: 11 x 10
type name label value se rse fixed diagonal m n
* <chr> <chr> <chr> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl>
@@ -240,8 +240,8 @@
9 ome OMEGA(2,2) "IIV V" 0.195 0.0320 0.164 FALSE TRUE 2 2
10 ome OMEGA(3,3) "IIV KA" 1.38 0.202 0.146 FALSE TRUE 3 3
11 sig SIGMA(1,1) "" 1 NA NA TRUE TRUE 1 1
-# Nicely formated table
-prm_table(xpdb, digits = 4)
+# Nicely formated table
+prm_table(xpdb, digits = 4)
Reporting transformed parameters:
For the OMEGA and SIGMA matrices, values are reported as standard deviations for the diagonal elements and as correlations for the off-diagonal elements. The relative standard errors (RSE) for OMEGA and SIGMA are reported on the approximate standard deviation scale (SE/variance estimate)/2. Use `transform = FALSE` to report untransformed parameters.
@@ -265,13 +265,13 @@
Access the run summary
The get_summary() function can be used to access the generated run summary from which the template_titles. If the argument .problem is specified a subset of the summary can be returned based on $PROBLEM.
Note that general summary information are listed as problem 0.
-run_sum <- get_summary(xpdb, .problem = 0)
-run_sum
+run_sum <- get_summary(xpdb, .problem = 0)
+run_sum
# A tibble: 12 x 5
problem subprob descr label value
<dbl> <dbl> <chr> <chr> <chr>
1 0 0 Run description descr NONMEM PK example for xpose
- 2 0 0 Run directory dir analysis/models/pk/
+ 2 0 0 Run directory dir data
3 0 0 Run errors errors na
4 0 0 ESAMPLE seed number esampleseed na
5 0 0 Run file file run001.lst
@@ -285,7 +285,7 @@
@@ -39,7 +39,7 @@
@@ -124,10 +124,10 @@
-
@@ -167,33 +167,33 @@
+
+
Customize plots
+Customize plots
Benjamin Guiastrennec
-17 March, 2020
+25 May, 2020
Source:vignettes/customize_plots.Rmd
customize_plots.Rmd
Labels
-All xpose plots have by default an informative title, subtitle and caption. For example all plots using individual model predictions (IPRED) will display the epsilons’ shrinkage. These titles can easily be edited as templates using @keywords which will be replaced by their actual value stored in the summary level of the xpdb object when rendering the plots. Keywords are defined by a word preceded by a @ e.g. '@ofv'. A list of all available keyword can be accessed via help('template_titles'). The title, subtitle or caption can be disabled by setting them to NULL. Suffix can be automatically added to title, subtitle and caption of all plots. The suffixes can be defined in the xp_theme.
+All xpose plots have by default an informative title, subtitle and caption. For example all plots using individual model predictions (IPRED) will display the epsilons’ shrinkage. These titles can easily be edited as templates using @keywords which will be replaced by their actual value stored in the summary level of the xpdb object when rendering the plots. Keywords are defined by a word preceded by a @ e.g. '@ofv'. A list of all available keyword can be accessed via help('template_titles'). The title, subtitle or caption can be disabled by setting them to NULL. Suffix can be automatically added to title, subtitle and caption of all plots. The suffixes can be defined in the xp_theme.
From the plot functions
-# Using template titles
-dv_vs_ipred(xpdb,
- title = '@y vs. @x (@run, obj: @ofv)',
- subtitle = 'Based on: @nind subjects and @nobs records',
- caption = 'Dir: @dir')
+# Using template titles
+dv_vs_ipred(xpdb,
+ title = '@y vs. @x (@run, obj: @ofv)',
+ subtitle = 'Based on: @nind subjects and @nobs records',
+ caption = 'Dir: @dir')
-# Disabling all titles
-dv_vs_ipred(xpdb, title = NULL, subtitle = NULL, caption = NULL)
+# Disabling all titles
+dv_vs_ipred(xpdb, title = NULL, subtitle = NULL, caption = NULL)
-# Edit title suffix from the xp_theme for a specific plot
-dv_vs_ipred(xpdb, title = 'A title', xp_theme = list(title_suffix = ' | a suffix for @run'))
+# Edit title suffix from the xp_theme for a specific plot
+dv_vs_ipred(xpdb, title = 'A title', xp_theme = list(title_suffix = ' | a suffix for @run'))
Using the labs function
Labels can also modified later on by using the ggplot2::labs() function in combination with the ggplot2 + operator.
-dv_vs_ipred(xpdb) +
- labs(title = '@descr',
- subtitle = NULL,
- caption = 'Based on @nobs observations',
- x = 'Individual model predictions (@x)',
- y = 'Observations (@y) for @nind subjects')
+dv_vs_ipred(xpdb) +
+ labs(title = '@descr',
+ subtitle = NULL,
+ caption = 'Based on @nobs observations',
+ x = 'Individual model predictions (@x)',
+ y = 'Observations (@y) for @nind subjects')
@@ -202,18 +202,18 @@
Modify aesthetics
Labels
-All xpose plots have by default an informative title, subtitle and caption. For example all plots using individual model predictions (IPRED) will display the epsilons’ shrinkage. These titles can easily be edited as templates using @keywords which will be replaced by their actual value stored in the summary level of the xpdb object when rendering the plots. Keywords are defined by a word preceded by a @ e.g. '@ofv'. A list of all available keyword can be accessed via help('template_titles'). The title, subtitle or caption can be disabled by setting them to NULL. Suffix can be automatically added to title, subtitle and caption of all plots. The suffixes can be defined in the xp_theme.
All xpose plots have by default an informative title, subtitle and caption. For example all plots using individual model predictions (IPRED) will display the epsilons’ shrinkage. These titles can easily be edited as templates using @keywords which will be replaced by their actual value stored in the summary level of the xpdb object when rendering the plots. Keywords are defined by a word preceded by a @ e.g. '@ofv'. A list of all available keyword can be accessed via help('template_titles'). The title, subtitle or caption can be disabled by setting them to NULL. Suffix can be automatically added to title, subtitle and caption of all plots. The suffixes can be defined in the xp_theme.
From the plot functions
-# Using template titles
-dv_vs_ipred(xpdb,
- title = '@y vs. @x (@run, obj: @ofv)',
- subtitle = 'Based on: @nind subjects and @nobs records',
- caption = 'Dir: @dir')# Using template titles +dv_vs_ipred(xpdb, + title = '@y vs. @x (@run, obj: @ofv)', + subtitle = 'Based on: @nind subjects and @nobs records', + caption = 'Dir: @dir')
# Disabling all titles
-dv_vs_ipred(xpdb, title = NULL, subtitle = NULL, caption = NULL)# Disabling all titles +dv_vs_ipred(xpdb, title = NULL, subtitle = NULL, caption = NULL)
# Edit title suffix from the xp_theme for a specific plot
-dv_vs_ipred(xpdb, title = 'A title', xp_theme = list(title_suffix = ' | a suffix for @run'))# Edit title suffix from the xp_theme for a specific plot +dv_vs_ipred(xpdb, title = 'A title', xp_theme = list(title_suffix = ' | a suffix for @run'))
Using the labs function
Labels can also modified later on by using the ggplot2::labs() function in combination with the ggplot2 + operator.
dv_vs_ipred(xpdb) +
- labs(title = '@descr',
- subtitle = NULL,
- caption = 'Based on @nobs observations',
- x = 'Individual model predictions (@x)',
- y = 'Observations (@y) for @nind subjects')dv_vs_ipred(xpdb) + + labs(title = '@descr', + subtitle = NULL, + caption = 'Based on @nobs observations', + x = 'Individual model predictions (@x)', + y = 'Observations (@y) for @nind subjects')
By default the aesthetics are read from the xp_theme level in the xpdb object but these can be modified in any plot function. xpose makes use of the ggplot2 functions mapping for any layer (e.g. points, lines, etc.) however to direct the mapping to a specific layer, a prefix appealing to the targeted layer should be used. The format is defined as layer_aesthetic = value. Hence to change the color of points in ggplot2 the argument color = 'green' could be used in geom_point(), while in xpose the same could be achieved with point_color = 'green'.
In basic goodness-of-fit plots, the layers have been named as: point_xxx, line_xxx, smooth_xxx, guide_xxx, xscale_xxx, yscale_xxx where xxx can be any option available in the ggplot2 layers: geom_point, geom_line, geom_smooth, geom_abline, scale_x_continuous, etc.
dv_vs_ipred(xpdb,
- # Change points aesthetics
- point_color = 'blue', point_alpha = 0.5,
- point_stroke = 0, point_size = 1.5,
- # Change lines aesthetics
- line_alpha = 0.5, line_size = 0.5,
- line_color = 'orange', line_linetype = 'solid',
- # Change smooth aesthetics
- smooth_method = 'lm')dv_vs_ipred(xpdb, + # Change points aesthetics + point_color = 'blue', point_alpha = 0.5, + point_stroke = 0, point_size = 1.5, + # Change lines aesthetics + line_alpha = 0.5, line_size = 0.5, + line_color = 'orange', line_linetype = 'solid', + # Change smooth aesthetics + smooth_method = 'lm')
Aesthetics can also be defined mapped to a variable using the ggplot2 aes() function.
dv_vs_ipred(xpdb, type = 'p', aes(point_color = SEX))dv_vs_ipred(xpdb, type = 'p', aes(point_color = SEX))
@@ -229,11 +229,11 @@
If
If
-
+
+
+
facets is a string e.g facets = "SEX", ggforce::facet_wrap_paginate will be usedfacets is a formula e.g facets = SEX~MED1, ggforce::facet_grid_paginate will be used# Example with a string
-dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 1)# Example with a string +dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 1)
# Example with a formula
-dv_vs_ipred(xpdb, facets = SEX~MED1, margins = TRUE)# Example with a formula +dv_vs_ipred(xpdb, facets = SEX~MED1, margins = TRUE)
All xpose plot functions accept arguments for facet_wrap_pagninate and facet_grid_paginate (e.g. ncol = 2, labeller = 'label_both', etc.). With the default xpose theme scales are set to 'free' from one panel to another (scales = 'free'), this behavior can be changed with scales = 'fixed', 'free_y' or 'free_x'.
For more information on the available faceting options read the help of facet_wrap_paginate and facet_grid_paginate from the package ggforce.
Additional layers
xpose offers the opportunity to add any additional layers from ggplot2. Hence, a ggplot2::geom_rug() layer could be added to the dv_vs_ipred() plot along with some annotations (ggplot2::annotate()). Note: the additional layers do not inherit from the xpose aesthetic mapping (i.e. colors or other options need to be defined in each layer as shown below).
Layers can also be used to modify the aesthetics scales for example ggplot2::scale_color_manual(), or remove a legend ggplot2::scale_fill_identity().
dv_vs_ipred(xpdb) +
- geom_rug(alpha = 0.2, color = 'grey50',
- sides = 'lb', size = 0.4) +
- annotate(geom = 'text',
- fontface = 'bold',
- color = 'darkred',
- size = 3,
- label = 'Toxic concentrations', x = 1.35, y = 1.75) +
- annotate(geom = 'rect',
- alpha = 0.2, fill = 'red',
- xmin = 1, xmax = Inf,
- ymin = 1, ymax = Inf)dv_vs_ipred(xpdb) + + geom_rug(alpha = 0.2, color = 'grey50', + sides = 'lb', size = 0.4) + + annotate(geom = 'text', + fontface = 'bold', + color = 'darkred', + size = 3, + label = 'Toxic concentrations', x = 1.35, y = 1.75) + + annotate(geom = 'rect', + alpha = 0.2, fill = 'red', + xmin = 1, xmax = Inf, + ymin = 1, ymax = Inf)
Scales options
The argument log allows to log-transform the axes. Accepted values are x, y or xy.
dv_vs_ipred(xpdb, log = 'xy', subtitle = 'Plot on log scale')dv_vs_ipred(xpdb, log = 'xy', subtitle = 'Plot on log scale')
Additional arguments can be provided to the scales via the mapping by using the naming convention xscale_xxx or yscale_xxx where xxx is the name of a ggplot2 scale argument such as name, breaks, labels, expand.
dv_vs_ipred(xpdb,
- xscale_breaks = c(0.3, 0.8, 1.3),
- xscale_labels = c('Low', 'Med', 'High'),
- xscale_expand = c(0.2, 0),
- xscale_name = 'Individual model predictions')dv_vs_ipred(xpdb, + xscale_breaks = c(0.3, 0.8, 1.3), + xscale_labels = c('Low', 'Med', 'High'), + xscale_expand = c(0.2, 0), + xscale_name = 'Individual model predictions')
@@ -281,16 +281,16 @@
An
+
-
diff --git a/docs/articles/customize_plots_files/figure-html/demo aesthetics-1.png b/docs/articles/customize_plots_files/figure-html/demo aesthetics-1.png
index 49e3f8df..a44f050b 100644
Binary files a/docs/articles/customize_plots_files/figure-html/demo aesthetics-1.png and b/docs/articles/customize_plots_files/figure-html/demo aesthetics-1.png differ
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diff --git a/docs/articles/faq.html b/docs/articles/faq.html
index bc6ffb8f..1bfff156 100644
--- a/docs/articles/faq.html
+++ b/docs/articles/faq.html
@@ -12,21 +12,21 @@
-
-
-
-
+
+
+
+
+
-
+
-
-
+
xpose (xp_theme) theme controlling the aspect of the layers such as points or linesThemes can be attached to an xpdb when importing the data with xpose_data() or using the function update_themes() on an xpdb object.
# While creating the xpdb
-xpdb <- xpose_data(runno = '001',
- gg_theme = theme_minimal(),
- xp_theme = theme_xp_xpose4())
-
-# Update a pre-existing xpdb
-xpdb <- update_themes(xpdb = xpdb,
- gg_theme = theme_bw2(),
- xp_theme = list(point_color = 'dodgerblue4',
- line_color = 'dodgerblue4')) # While creating the xpdb +xpdb <- xpose_data(runno = '001', + gg_theme = theme_minimal(), + xp_theme = theme_xp_xpose4()) + +# Update a pre-existing xpdb +xpdb <- update_themes(xpdb = xpdb, + gg_theme = theme_bw2(), + xp_theme = list(point_color = 'dodgerblue4', + line_color = 'dodgerblue4'))
@@ -39,7 +39,7 @@
@@ -124,10 +124,10 @@
-
@@ -189,20 +189,11 @@
Frequently Asked Questions
+Frequently Asked Questions
Benjamin Guiastrennec
-17 March, 2020
+25 May, 2020
Source:vignettes/faq.Rmd
faq.Rmd
@@ -39,7 +39,7 @@
@@ -121,15 +121,15 @@
-
+
-
@@ -148,23 +148,23 @@
+
Creating interactive plots
+Creating interactive plots
Benjamin Guiastrennec
-17 March, 2020
+25 May, 2020
Source:vignettes/interactive_plots.Rmd
interactive_plots.Rmd
Install plotly
This command is only required for the first use.
-install.packages("plotly")install.packages("plotly")
Generate a plot with xpose
Then generate any plot in xpose and store it in an object.
-gof_plot <- dv_vs_ipred(xpdb_ex_pk, type = 'pl',
- aes(point_color = SEX,
- line_color = SEX)) +
- labs(color = 'Sex', title = 'DV vs IPRED')gof_plot <- dv_vs_ipred(xpdb_ex_pk, type = 'pl', + aes(point_color = SEX, + line_color = SEX)) + + labs(color = 'Sex', title = 'DV vs IPRED')
## Using data from $prob no.1## Filtering data by EVID == 0
Convert the plot from ggplot2 to plotly
The ggplot2 object is now automatically converted to a plotly graph. The scales can now be changed interactively, groups can be hidden by clicking on the legend
-plotly::ggplotly(gof_plot)plotly::ggplotly(gof_plot)
@@ -182,26 +182,26 @@
A distribution
-xpdb_ex_pk %>%
- eta_distrib(title = 'Eta distribution') %>%
- plotly::ggplotly()
-
-
+xpdb_ex_pk %>%
+ eta_distrib(title = 'Eta distribution') %>%
+ plotly::ggplotly()
+
+
A distribution
-xpdb_ex_pk %>%
- eta_distrib(title = 'Eta distribution') %>%
- plotly::ggplotly()xpdb_ex_pk %>% + eta_distrib(title = 'Eta distribution') %>% + plotly::ggplotly()
"); + trace.legendgroup = trace.legendgroup || keys.join("
"); + + // keep track of mapping between this new trace and the trace it targets + // (necessary for updating frames to reflect the selection traces) + trace._originalIndex = i; + trace._newIndex = this.gd._fullData.length + traces.length; + trace._isCrosstalkTrace = true; + traces.push(trace); + } + } + + if (traces.length > 0) { + + Plotly.addTraces(this.gd, traces).then(function(gd) { + // incrementally add selection traces to frames + // (this is heavily inspired by Plotly.Plots.modifyFrames() + // in src/plots/plots.js) + var _hash = gd._transitionData._frameHash; + var _frames = gd._transitionData._frames || []; + + for (var i = 0; i < _frames.length; i++) { + + // add to _frames[i].traces *if* this frame references selected trace(s) + var newIndices = []; + for (var j = 0; j < traces.length; j++) { + var tr = traces[j]; + if (_frames[i].traces.indexOf(tr._originalIndex) > -1) { + newIndices.push(tr._newIndex); + _frames[i].traces.push(tr._newIndex); + } + } + + // nothing to do... + if (newIndices.length === 0) { + continue; + } + + var ctr = 0; + var nFrameTraces = _frames[i].data.length; + + for (var j = 0; j < nFrameTraces; j++) { + var frameTrace = _frames[i].data[j]; + if (!frameTrace.key || frameTrace.set !== group) { + continue; + } + + var matchFunc = getMatchFunc(frameTrace); + var matches = matchFunc(frameTrace.key, keys); + + if (matches.length > 0) { + if (!trace._isSimpleKey) { + frameTrace = subsetArrayAttrs(frameTrace, matches); + } + var d = gd._fullData[newIndices[ctr]]; + if (d.marker) { + frameTrace.marker = d.marker; + } + if (d.line) { + frameTrace.line = d.line; + } + if (d.textfont) { + frameTrace.textfont = d.textfont; + } + ctr = ctr + 1; + _frames[i].data.push(frameTrace); + } + } + + // update gd._transitionData._frameHash + _hash[_frames[i].name] = _frames[i]; + } + + }); + + // dim traces that have a set matching the set of selection sets + var tracesToDim = [], + opacities = [], + sets = Object.keys(this.groupSelections), + n = this.origData.length; + + for (var i = 0; i < n; i++) { + var opacity = this.origOpacity[i] || 1; + // have we already dimmed this trace? Or is this even worth doing? + if (opacity !== this.gd._fullData[i].opacity || this.highlight.opacityDim === 1) { + continue; + } + // is this set an element of the set of selection sets? + var matches = findMatches(sets, [this.gd.data[i].set]); + if (matches.length) { + tracesToDim.push(i); + opacities.push(opacity * this.highlight.opacityDim); + } + } + + if (tracesToDim.length > 0) { + Plotly.restyle(this.gd, {"opacity": opacities}, tracesToDim); + // turn off the selected/unselected API + Plotly.restyle(this.gd, {"selectedpoints": null}); + } + + } + + } +}; + +/* +Note: in all of these match functions, we assume needleSet (i.e. the selected keys) +is a 1D (or flat) array. The real difference is the meaning of haystack. +findMatches() does the usual thing you'd expect for +linked brushing on a scatterplot matrix. findSimpleMatches() returns a match iff +haystack is a subset of the needleSet. findNestedMatches() returns +*/ + +function getMatchFunc(trace) { + return (trace._isNestedKey) ? findNestedMatches : + (trace._isSimpleKey) ? findSimpleMatches : findMatches; +} + +// find matches for "flat" keys +function findMatches(haystack, needleSet) { + var matches = []; + haystack.forEach(function(obj, i) { + if (obj === null || needleSet.indexOf(obj) >= 0) { + matches.push(i); + } + }); + return matches; +} + +// find matches for "simple" keys +function findSimpleMatches(haystack, needleSet) { + var match = haystack.every(function(val) { + return val === null || needleSet.indexOf(val) >= 0; + }); + // yes, this doesn't make much sense other than conforming + // to the output type of the other match functions + return (match) ? [0] : [] +} + +// find matches for a "nested" haystack (2D arrays) +function findNestedMatches(haystack, needleSet) { + var matches = []; + for (var i = 0; i < haystack.length; i++) { + var hay = haystack[i]; + var match = hay.every(function(val) { + return val === null || needleSet.indexOf(val) >= 0; + }); + if (match) { + matches.push(i); + } + } + return matches; +} + +function isPlainObject(obj) { + return ( + Object.prototype.toString.call(obj) === '[object Object]' && + Object.getPrototypeOf(obj) === Object.prototype + ); +} + +function subsetArrayAttrs(obj, indices) { + var newObj = {}; + Object.keys(obj).forEach(function(k) { + var val = obj[k]; + + if (k.charAt(0) === "_") { + newObj[k] = val; + } else if (k === "transforms" && Array.isArray(val)) { + newObj[k] = val.map(function(transform) { + return subsetArrayAttrs(transform, indices); + }); + } else if (k === "colorscale" && Array.isArray(val)) { + newObj[k] = val; + } else if (isPlainObject(val)) { + newObj[k] = subsetArrayAttrs(val, indices); + } else if (Array.isArray(val)) { + newObj[k] = subsetArray(val, indices); + } else { + newObj[k] = val; + } + }); + return newObj; +} + +function subsetArray(arr, indices) { + var result = []; + for (var i = 0; i < indices.length; i++) { + result.push(arr[indices[i]]); + } + return result; +} + +// Convenience function for removing plotly's brush +function removeBrush(el) { + var outlines = el.querySelectorAll(".select-outline"); + for (var i = 0; i < outlines.length; i++) { + outlines[i].remove(); + } +} + + +// https://davidwalsh.name/javascript-debounce-function + +// Returns a function, that, as long as it continues to be invoked, will not +// be triggered. The function will be called after it stops being called for +// N milliseconds. If `immediate` is passed, trigger the function on the +// leading edge, instead of the trailing. +function debounce(func, wait, immediate) { + var timeout; + return function() { + var context = this, args = arguments; + var later = function() { + timeout = null; + if (!immediate) func.apply(context, args); + }; + var callNow = immediate && !timeout; + clearTimeout(timeout); + timeout = setTimeout(later, wait); + if (callNow) func.apply(context, args); + }; +}; diff --git a/docs/articles/introduction.html b/docs/articles/introduction.html index 00ea8d31..f9f1094d 100644 --- a/docs/articles/introduction.html +++ b/docs/articles/introduction.html @@ -12,21 +12,21 @@ - - - - + + + + + - + - - +
@@ -39,7 +39,7 @@
@@ -124,10 +124,10 @@
-
@@ -140,40 +140,40 @@
+
Introduction
+Introduction
Benjamin Guiastrennec
-17 March, 2020
+25 May, 2020
Source:vignettes/introduction.Rmd
introduction.Rmd17 March, 2020
Load xpose
The first step is to load xpose in R with the following command:
-library(xpose)library(xpose)
Import model output
The function xpose_data() collects all model output files and table and organizes them into an R object commonly called xpdb which stands for “xpose database”.
xpdb <- xpose_data(runno = '001', dir = 'analysis/model/pk/')xpdb <- xpose_data(runno = '001', dir = 'analysis/model/pk/')
Glimpse at the xpdb
The files attached to an xpdb object can be displayed to the console simply by writing its name to the console or by using the print() function.
xpdb # or print(xpdb)
run001.lst overview:
- Software: nonmem 7.3.0
- - Attached files (memory usage 1.3 Mb):
+ - Attached files (memory usage 1.4 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
+ special: <none>
- gg_theme: theme_readable
- xp_theme: theme_xp_default
- - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULLModel summary
A summary of a model run can be displayed to the console by using the summary() function on an xpdb object.
summary(xpdb)summary(xpdb)
Summary for problem no. 0 [Global information]
- Software @software : nonmem
- Software version @version : 7.3.0
- - Run directory @dir : analysis/models/pk/
+ - Run directory @dir : data
- Run file @file : run001.lst
- Run number @run : run001
- Reference model @ref : 000
@@ -211,7 +211,7 @@
Parameter estimates
A table of parameter estimates can be displayed to the console by using the prm_table() function on an xpdb object.
-prm_table(xpdb)
+prm_table(xpdb)
Reporting transformed parameters:
For the OMEGA and SIGMA matrices, values are reported as standard deviations for the diagonal elements and as correlations for the off-diagonal elements. The relative standard errors (RSE) for OMEGA and SIGMA are reported on the approximate standard deviation scale (SE/variance estimate)/2. Use `transform = FALSE` to report untransformed parameters.
@@ -234,7 +234,7 @@
Listing variables
A list of available variables for plotting can be displayed to the console by using the list_vars() function on an xpdb object.
-list_vars(xpdb)
+list_vars(xpdb)
List of available variables for problem no. 1
- Subject identifier (id) : ID
@@ -265,52 +265,52 @@
Pipes
xpose makes use of the pipe operator %>% from the package dplyr. Pipes can be used to generate clear workflow.
-xpose_data(runno = '001') %>%
- dv_vs_ipred() %>%
- xpose_save(file = 'run001_dv_vs_ipred.pdf')
+xpose_data(runno = '001') %>%
+ dv_vs_ipred() %>%
+ xpose_save(file = 'run001_dv_vs_ipred.pdf')
Editing the xpdb
Multiples edits can be made to the xpdb object. For instance the type (visible using the list_vars() function described above) of a variable can be changed. Hence the independent variable (idv) could be changed from TIME (default in NONMEM) to TAD. All plots using idv will then automatically use TAD.
# With the TIME default
-xpdb %>%
- dv_vs_idv()# With the TIME default +xpdb %>% + dv_vs_idv()
# After IDV reassignment
-xpdb %>%
- set_var_types(idv = 'TAD') %>%
- dv_vs_idv()# After IDV reassignment +xpdb %>% + set_var_types(idv = 'TAD') %>% + dv_vs_idv()
Generating plots
Plotting functions are used as follows:
-# DV vs. IPRED plot
-dv_vs_ipred(xpdb)# DV vs. IPRED plot +dv_vs_ipred(xpdb)
# CWRES vs. PRED plot
-res_vs_pred(xpdb, res = 'CWRES')# CWRES vs. PRED plot +res_vs_pred(xpdb, res = 'CWRES')
Saving plots
The xpose_save function was designed to facilitate the export of xpose plots. The file extension is guessed from the file name and must match one of .pdf (default), .jpeg, .png, .bmp or .tiff. If no extension is provided as part of the file name a .pdf will be generated. Finally, if the plot argument is left empty xpose_save will automatically save the last plot that was created or modified.
The xpose_save() function is compatible with templates titles and keywords such as @run for the run number and @plotfun for the name of the plotting function can be used to automatically name files. Learn more about the template titles keywords using help('template_titles').
# Save the last generated plot
-dv_vs_ipred(xpdb)
-xpose_save(file = 'run001_dv_vs_ipred.pdf')
-
-# Template titles can also be used in filename and the directory
-xpdb %>%
- dv_vs_ipred() %>%
- xpose_save(file = '@run_@plotfun_[@ofv].jpeg', dir = '@dir')The xpose_save() function is compatible with templates titles and keywords such as @run for the run number and @plotfun for the name of the plotting function can be used to automatically name files. Learn more about the template titles keywords using help('template_titles').
# Save the last generated plot +dv_vs_ipred(xpdb) +xpose_save(file = 'run001_dv_vs_ipred.pdf') + +# Template titles can also be used in filename and the directory +xpdb %>% + dv_vs_ipred() %>% + xpose_save(file = '@run_@plotfun_[@ofv].jpeg', dir = '@dir')
@@ -39,7 +39,7 @@
@@ -124,10 +124,10 @@
-
@@ -146,11 +146,11 @@
If
-
+
+
@@ -161,32 +161,32 @@ Plots with multiple pages
+Plots with multiple pages
Benjamin Guiastrennec
-17 March, 2020
+25 May, 2020
Source:vignettes/multiple_pages.Rmd
multiple_pages.Rmdfacets is a formula e.g facets = SEX~MED1, facets will be created using ggforce::facet_grid_paginate
# Example with a string
-dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'))# Example with a string +dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'))
# Example with a formula
-dv_vs_ipred(xpdb, facets = SEX~MED1, margins = TRUE)# Example with a formula +dv_vs_ipred(xpdb, facets = SEX~MED1, margins = TRUE)
All xpose plot functions accept arguments for facet_wrap_pagninate and facet_grid_paginate (e.g. ncol = 2, labeller = 'label_both', etc.). With the default xpose theme scales are set to 'free' from one panel to another (scales = 'free'), this behavior can be changed with scales = 'fixed', 'free_y' or 'free_x'.
Setting the layout
When the arguments ncol and nrow are set and under the condition that they are more panels to be drawn that can fit on a single page given the selected layout, the multiple page functionality will automatically be enabled.
dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1)dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1)
Selecting pages
By default all pages will be shown but this can be time consuming. To select only specific pages to be drawn, use the argument page.
dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 1)dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 1)
It is also possible to change the page(s) to be drawn from an already existing xpose_plot object via the print() function.
# Create an xpose_plot, by default page = 1
-p1 <- dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1)
-
-# Change the page to be drawn
-print(p1, page = 2)# Create an xpose_plot, by default page = 1 +p1 <- dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1) + +# Change the page to be drawn +print(p1, page = 2)
Numbering pages
To number pages the keywords @page and @lastpage can be used to respectively indicate the current page and the total number of pages.
dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 1, caption = 'Page @page of @lastpage')dv_vs_ipred(xpdb, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 1, caption = 'Page @page of @lastpage')
To systematically number all generated plots a caption suffix can be defined in the xp_theme.
xpdb_numbered <- update_themes(xpdb, xp_theme = list(caption_suffix = ', page @page of @lastpage'))
-
-dv_vs_ipred(xpdb_numbered, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 2)xpdb_numbered <- update_themes(xpdb, xp_theme = list(caption_suffix = ', page @page of @lastpage')) + +dv_vs_ipred(xpdb_numbered, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1, page = 2)
Saving multiple pages plots
Multiple pages graphs can simply be saved by using the xpose_save() function.
dv_vs_ipred(xpdb_numbered, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1) %>%
-xpose_save(file = 'dv_vs_ipred_multiple.pdf')dv_vs_ipred(xpdb_numbered, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1) %>% +xpose_save(file = 'dv_vs_ipred_multiple.pdf')
The pdf format can conveniently store multiple pages within a single document. However other graphical devices (e.g. 'png', 'jpg') can also be used, simply ensure to have a page counter (i.e. '%03d') added the file name.
dv_vs_ipred(xpdb_numbered, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1) %>%
-xpose_save(file = '@run_@plotfun_%03d.png')dv_vs_ipred(xpdb_numbered, facets = c('SEX', 'MED1'), ncol = 2, nrow = 1) %>% +xpose_save(file = '@run_@plotfun_%03d.png')
@@ -39,7 +39,7 @@
@@ -124,10 +124,10 @@
-
@@ -142,48 +142,48 @@
The xpose plots
+The xpose plots
Benjamin Guiastrennec
-17 March, 2020
+25 May, 2020
Source:vignettes/plot_list.Rmd
plot_list.Rmd
Plots of the independent variable (IDV)
-dv_vs_idv(xpdb_ex_pk)
+dv_vs_idv(xpdb_ex_pk)
-ipred_vs_idv(xpdb_ex_pk)
+ipred_vs_idv(xpdb_ex_pk)
-pred_vs_idv(xpdb_ex_pk)
+pred_vs_idv(xpdb_ex_pk)
-dv_preds_vs_idv(xpdb_ex_pk)
+dv_preds_vs_idv(xpdb_ex_pk)
DV vs. PREDs plots
-dv_vs_pred(xpdb_ex_pk)
+dv_vs_pred(xpdb_ex_pk)
-dv_vs_ipred(xpdb_ex_pk)
+dv_vs_ipred(xpdb_ex_pk)
Residual plots
-res_vs_idv(xpdb_ex_pk, res = 'CWRES')
+res_vs_idv(xpdb_ex_pk, res = 'CWRES')
-res_vs_pred(xpdb_ex_pk, res = 'CWRES')
+res_vs_pred(xpdb_ex_pk, res = 'CWRES')
-absval_res_vs_idv(xpdb_ex_pk, res = 'CWRES')
+absval_res_vs_idv(xpdb_ex_pk, res = 'CWRES')
-absval_res_vs_pred(xpdb_ex_pk, res = 'CWRES')
+absval_res_vs_pred(xpdb_ex_pk, res = 'CWRES')
Plots of the independent variable (IDV)
-dv_vs_idv(xpdb_ex_pk)dv_vs_idv(xpdb_ex_pk)
ipred_vs_idv(xpdb_ex_pk)ipred_vs_idv(xpdb_ex_pk)
pred_vs_idv(xpdb_ex_pk)pred_vs_idv(xpdb_ex_pk)
dv_preds_vs_idv(xpdb_ex_pk)dv_preds_vs_idv(xpdb_ex_pk)
DV vs. PREDs plots
-dv_vs_pred(xpdb_ex_pk)dv_vs_pred(xpdb_ex_pk)
dv_vs_ipred(xpdb_ex_pk)dv_vs_ipred(xpdb_ex_pk)
Residual plots
-res_vs_idv(xpdb_ex_pk, res = 'CWRES')res_vs_idv(xpdb_ex_pk, res = 'CWRES')
res_vs_pred(xpdb_ex_pk, res = 'CWRES')res_vs_pred(xpdb_ex_pk, res = 'CWRES')
absval_res_vs_idv(xpdb_ex_pk, res = 'CWRES')absval_res_vs_idv(xpdb_ex_pk, res = 'CWRES')
absval_res_vs_pred(xpdb_ex_pk, res = 'CWRES')absval_res_vs_pred(xpdb_ex_pk, res = 'CWRES')
Minimization plots
-prm_vs_iteration(xpdb_ex_pk, labeller = 'label_value')prm_vs_iteration(xpdb_ex_pk, labeller = 'label_value')
grd_vs_iteration(xpdb_ex_pk, labeller = 'label_value')grd_vs_iteration(xpdb_ex_pk, labeller = 'label_value')
@@ -192,25 +192,25 @@
@@ -220,7 +220,7 @@
Histograms and density plots
-prm_distrib(xpdb_ex_pk)
+prm_distrib(xpdb_ex_pk)
-eta_distrib(xpdb_ex_pk)
+eta_distrib(xpdb_ex_pk)
-res_distrib(xpdb_ex_pk)
+res_distrib(xpdb_ex_pk)
-cov_distrib(xpdb_ex_pk)
+cov_distrib(xpdb_ex_pk)
Histograms and density plots
-prm_distrib(xpdb_ex_pk)prm_distrib(xpdb_ex_pk)
eta_distrib(xpdb_ex_pk)eta_distrib(xpdb_ex_pk)
res_distrib(xpdb_ex_pk)res_distrib(xpdb_ex_pk)
cov_distrib(xpdb_ex_pk)cov_distrib(xpdb_ex_pk)
Compartment’s amount
-amt_vs_idv(xpdb_ex_pk, nrow = 2, ncol = 1)
+amt_vs_idv(xpdb_ex_pk, nrow = 2, ncol = 1)
Compartment’s amount
-amt_vs_idv(xpdb_ex_pk, nrow = 2, ncol = 1)amt_vs_idv(xpdb_ex_pk, nrow = 2, ncol = 1)
@@ -39,7 +39,7 @@
@@ -124,10 +124,10 @@
-
@@ -160,14 +160,14 @@
Adjust the VPC data (e.g. remove panels or factor labels) before plotting
Facilitate error debugging
-
+
+
+ - Options: dir = data, quiet = FALSE, manual_import = NULL
+
-
-
Visual Predictive Checks (VPC)
+Visual Predictive Checks (VPC)
Benjamin Guiastrennec
-17 March, 2020
+25 May, 2020
Source:vignettes/vpc.Rmd
vpc.Rmd
The generated VPC data is stored in the xpdb under specials datasets and can be used later on.
-xpdb_w_vpc <- vpc_data(xpdb_ex_pk) # Compute and store VPC data
-
-xpdb_w_vpc # The vpc data is now listed under the xpdb "special" dataxpdb_w_vpc <- vpc_data(xpdb_ex_pk) # Compute and store VPC data + +xpdb_w_vpc # The vpc data is now listed under the xpdb "special" data
run001.lst overview:
- Software: nonmem 7.3.0
- Attached files (memory usage 1.5 Mb):
@@ -177,18 +177,16 @@
+ special: vpc continuous (#3)
- gg_theme: theme_readable
- xp_theme: theme_xp_default
- - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
vpc(xpdb_w_vpc) # Plot the vpc from the stored datavpc(xpdb_w_vpc) # Plot the vpc from the stored data
Multiple VPC data can be stored in an xpdb, but only one of each vpc_type.
xpdb_w_multi_vpc <- xpdb_ex_pk %>%
- vpc_data(vpc_type = 'continuous', opt = vpc_opt(n_bins = 6, lloq = 0.1)) %>%
- vpc_data(vpc_type = 'censored', opt = vpc_opt(n_bins = 6, lloq = 0.1))
-
-vpc(xpdb_w_multi_vpc, vpc_type = 'continuous')
vpc(xpdb_w_multi_vpc, vpc_type = 'censored')
@@ -202,11 +200,10 @@
stratify options defines up to two stratifying variable to be used when computing the VPC data. The stratify variables can either be provided as a character vector (stratify = c('SEX', 'MED1')) or a formula (stratify = SEX~MED1) . The former will result in the use of ggforce::facet_wrap_paginate() and the latter of ggforce::facet_grid_paginate() when creating the plot. With “categorical” VPC the “group” variable will also be added by default.opt argument. The opt argument expects the output from the vpc_opt() functions argument.xpdb_ex_pk %>%
- vpc_data(vpc_type = 'censored', stratify = 'SEX',
- opt = vpc_opt(bins = 'jenks', n_bins = 7, lloq = 0.5)) %>%
- vpc()
@@ -223,37 +220,37 @@
Creating VPC using the xpdb data
Creating VPC using the xpdb data
To create VPC using the xpdb data, at least one simulation and one estimation problem need to present. Hence in the case of NONMEM the run used to generate the xpdb should contain several$PROBLEM. In vpc_data() the problem number can be specified for the observation (obs_problem) and the simulation (sim_problem). By default xpose picks the last one of each to generate the VPC.
# View the xpdb content and data problems +xpdb_ex_pk
run001.lst overview:
- Software: nonmem 7.3.0
- - Attached files (memory usage 1.3 Mb):
+ - Attached files (memory usage 1.4 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
+ special: <none>
- gg_theme: theme_readable
- xp_theme: theme_xp_default
- - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL# Generate the vpc
-xpdb_ex_pk %>%
- vpc_data(vpc_type = 'continuous', obs_problem = 1, sim_problem = 2) %>%
- vpc()# Generate the vpc +xpdb_ex_pk %>% + vpc_data(vpc_type = 'continuous', obs_problem = 1, sim_problem = 2) %>% + vpc()
Creating the VPC using a PsN folder
-The vpc_data() contains an argument psn_foler which can be used to point to a PsN generated VPC folder. As in most xpose function template_titles keywords can be used to automatize the process e.g. psn_folder = '@dir/@run_vpc' where @dir and @run will be automatically translated to initial (i.e. when the xpdb was generated) run directory and run number 'analysis/models/pk/run001_vpc'.
The vpc_data() contains an argument psn_folder which can be used to point to a PsN generated VPC folder. As in most xpose function template_titles keywords can be used to automatize the process e.g. psn_folder = '@dir/@run_vpc' where @dir and @run will be automatically translated to initial (i.e. when the xpdb was generated) run directory and run number 'analysis/models/pk/run001_vpc'.
In this case, the data will be read from the /m1 sub-folder (or m1.zip if compressed). Note that PsN drops unused columns to reduce the simtab file size. Thus, in order to allow for more flexibility in R, it is recommended to use multiple stratifying variables (-stratify_on=VAR1,VAR2) and the prediction corrected (-predcorr adds the PRED column to the output) options in PsN to avoid having to rerun PsN to add these variables later on. In addition, -dv, -idv, -lloq, -uloq, -predcorr and -stratify_on PsN options are automatically applied to xpose VPC.
The PsN generated binning can also applied to xpose VPC with the vpc_data() option psn_bins = TRUE (disabled by default). However PsN and the vpc package work slightly differently so the results may not be optimal and the output should be evaluated carefully.
`, then ``, etc.) that has more than one element. Defaults to 1 (for ``).
+ getTopLevel: function($scope) {
+ for (var i = 1; i <= 6; i++) {
+ var $headings = this.findOrFilter($scope, 'h' + i);
+ if ($headings.length > 1) {
+ return i;
+ }
+ }
+
+ return 1;
+ },
+
+ // returns the elements for the top level, and the next below it
+ getHeadings: function($scope, topLevel) {
+ var topSelector = 'h' + topLevel;
+
+ var secondaryLevel = topLevel + 1;
+ var secondarySelector = 'h' + secondaryLevel;
+
+ return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
+ },
+
+ getNavLevel: function(el) {
+ return parseInt(el.tagName.charAt(1), 10);
+ },
+
+ populateNav: function($topContext, topLevel, $headings) {
+ var $context = $topContext;
+ var $prevNav;
+
+ var helpers = this;
+ $headings.each(function(i, el) {
+ var $newNav = helpers.generateNavItem(el);
+ var navLevel = helpers.getNavLevel(el);
+
+ // determine the proper $context
+ if (navLevel === topLevel) {
+ // use top level
+ $context = $topContext;
+ } else if ($prevNav && $context === $topContext) {
+ // create a new level of the tree and switch to it
+ $context = helpers.createChildNavList($prevNav);
+ } // else use the current $context
+
+ $context.append($newNav);
+
+ $prevNav = $newNav;
+ });
+ },
+
+ parseOps: function(arg) {
+ var opts;
+ if (arg.jquery) {
+ opts = {
+ $nav: arg
+ };
+ } else {
+ opts = arg;
+ }
+ opts.$scope = opts.$scope || $(document.body);
+ return opts;
+ }
+ },
+
+ // accepts a jQuery object, or an options object
+ init: function(opts) {
+ opts = this.helpers.parseOps(opts);
+
+ // ensure that the data attribute is in place for styling
+ opts.$nav.attr('data-toggle', 'toc');
+
+ var $topContext = this.helpers.createChildNavList(opts.$nav);
+ var topLevel = this.helpers.getTopLevel(opts.$scope);
+ var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
+ this.helpers.populateNav($topContext, topLevel, $headings);
+ }
+ };
+
+ $(function() {
+ $('nav[data-toggle="toc"]').each(function(i, el) {
+ var $nav = $(el);
+ Toc.init($nav);
+ });
+ });
+})();
diff --git a/docs/index.html b/docs/index.html
index c78bfe3d..8bd0426e 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -12,10 +12,11 @@
-
-
-
-
+
+
+
+
+
-
-
+
@@ -43,7 +43,7 @@
@@ -140,12 +140,12 @@
Installation
-# Install the lastest release from the CRAN
-install.packages('xpose')
-
-# Or install the development version from GitHub
-# install.packages('devtools')
-devtools::install_github('UUPharmacometrics/xpose')
+# Install the lastest release from the CRAN
+install.packages('xpose')
+
+# Or install the development version from GitHub
+# install.packages('devtools')
+devtools::install_github('UUPharmacometrics/xpose')
@@ -153,20 +153,20 @@
Glance at the data object
-
+xpdb
run001.lst overview:
- Software: nonmem 7.3.0
- - Attached files (memory usage 1.3 Mb):
+ - Attached files (memory usage 1.3 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
@@ -177,7 +177,7 @@
Model summary
-summary(xpdb, problem = 1)
+summary(xpdb, problem = 1)
Summary for problem no. 0 [Global information]
- Software @software : nonmem
@@ -223,33 +223,33 @@
Distribution plots
-eta_distrib(xpdb, labeller = 'label_value')
+eta_distrib(xpdb, labeller = 'label_value')
Minimization diagnostics
-prm_vs_iteration(xpdb, labeller = 'label_value')
+prm_vs_iteration(xpdb, labeller = 'label_value')
@@ -275,13 +275,13 @@
-
diff --git a/docs/reference/all_data_problem.html b/docs/reference/all_data_problem.html
index 6c3aab20..68a424e4 100644
--- a/docs/reference/all_data_problem.html
+++ b/docs/reference/all_data_problem.html
@@ -17,23 +17,27 @@
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diff --git a/docs/reference/all_file_problem.html b/docs/reference/all_file_problem.html
index a2c97f7b..83a32fb9 100644
--- a/docs/reference/all_file_problem.html
+++ b/docs/reference/all_file_problem.html
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diff --git a/docs/reference/amt_vs_idv-1.png b/docs/reference/amt_vs_idv-1.png
index d62d10eb..0be4f2fa 100644
Binary files a/docs/reference/amt_vs_idv-1.png and b/docs/reference/amt_vs_idv-1.png differ
diff --git a/docs/reference/amt_vs_idv.html b/docs/reference/amt_vs_idv.html
index 2daef739..10e470c5 100644
--- a/docs/reference/amt_vs_idv.html
+++ b/docs/reference/amt_vs_idv.html
@@ -17,23 +17,27 @@
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diff --git a/docs/reference/append_suffix.html b/docs/reference/append_suffix.html
index b69da160..80c3a037 100644
--- a/docs/reference/append_suffix.html
+++ b/docs/reference/append_suffix.html
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@@ -47,7 +51,6 @@
-
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-
+
-
@@ -215,7 +215,7 @@ Contents
`).
+ getTopLevel: function($scope) {
+ for (var i = 1; i <= 6; i++) {
+ var $headings = this.findOrFilter($scope, 'h' + i);
+ if ($headings.length > 1) {
+ return i;
+ }
+ }
+
+ return 1;
+ },
+
+ // returns the elements for the top level, and the next below it
+ getHeadings: function($scope, topLevel) {
+ var topSelector = 'h' + topLevel;
+
+ var secondaryLevel = topLevel + 1;
+ var secondarySelector = 'h' + secondaryLevel;
+
+ return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
+ },
+
+ getNavLevel: function(el) {
+ return parseInt(el.tagName.charAt(1), 10);
+ },
+
+ populateNav: function($topContext, topLevel, $headings) {
+ var $context = $topContext;
+ var $prevNav;
+
+ var helpers = this;
+ $headings.each(function(i, el) {
+ var $newNav = helpers.generateNavItem(el);
+ var navLevel = helpers.getNavLevel(el);
+
+ // determine the proper $context
+ if (navLevel === topLevel) {
+ // use top level
+ $context = $topContext;
+ } else if ($prevNav && $context === $topContext) {
+ // create a new level of the tree and switch to it
+ $context = helpers.createChildNavList($prevNav);
+ } // else use the current $context
+
+ $context.append($newNav);
+
+ $prevNav = $newNav;
+ });
+ },
+
+ parseOps: function(arg) {
+ var opts;
+ if (arg.jquery) {
+ opts = {
+ $nav: arg
+ };
+ } else {
+ opts = arg;
+ }
+ opts.$scope = opts.$scope || $(document.body);
+ return opts;
+ }
+ },
+
+ // accepts a jQuery object, or an options object
+ init: function(opts) {
+ opts = this.helpers.parseOps(opts);
+
+ // ensure that the data attribute is in place for styling
+ opts.$nav.attr('data-toggle', 'toc');
+
+ var $topContext = this.helpers.createChildNavList(opts.$nav);
+ var topLevel = this.helpers.getTopLevel(opts.$scope);
+ var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
+ this.helpers.populateNav($topContext, topLevel, $headings);
+ }
+ };
+
+ $(function() {
+ $('nav[data-toggle="toc"]').each(function(i, el) {
+ var $nav = $(el);
+ Toc.init($nav);
+ });
+ });
+})();
diff --git a/docs/index.html b/docs/index.html
index c78bfe3d..8bd0426e 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -12,10 +12,11 @@
-
-
-
-
+
+
+
+
+
-
-
+
@@ -43,7 +43,7 @@
@@ -140,12 +140,12 @@
Installation
-# Install the lastest release from the CRAN
-install.packages('xpose')
-
-# Or install the development version from GitHub
-# install.packages('devtools')
-devtools::install_github('UUPharmacometrics/xpose')
+# Install the lastest release from the CRAN
+install.packages('xpose')
+
+# Or install the development version from GitHub
+# install.packages('devtools')
+devtools::install_github('UUPharmacometrics/xpose')
@@ -153,20 +153,20 @@
Glance at the data object
-
+xpdb
run001.lst overview:
- Software: nonmem 7.3.0
- - Attached files (memory usage 1.3 Mb):
+ - Attached files (memory usage 1.3 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
@@ -177,7 +177,7 @@
Model summary
-summary(xpdb, problem = 1)
+summary(xpdb, problem = 1)
Summary for problem no. 0 [Global information]
- Software @software : nonmem
@@ -223,33 +223,33 @@
Distribution plots
-eta_distrib(xpdb, labeller = 'label_value')
+eta_distrib(xpdb, labeller = 'label_value')
Minimization diagnostics
-prm_vs_iteration(xpdb, labeller = 'label_value')
+prm_vs_iteration(xpdb, labeller = 'label_value')
@@ -275,13 +275,13 @@
-
diff --git a/docs/reference/all_data_problem.html b/docs/reference/all_data_problem.html
index 6c3aab20..68a424e4 100644
--- a/docs/reference/all_data_problem.html
+++ b/docs/reference/all_data_problem.html
@@ -17,23 +17,27 @@
-
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@@ -65,7 +68,7 @@
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+
-
@@ -207,7 +207,7 @@ Contents
diff --git a/docs/reference/all_file_problem.html b/docs/reference/all_file_problem.html
index a2c97f7b..83a32fb9 100644
--- a/docs/reference/all_file_problem.html
+++ b/docs/reference/all_file_problem.html
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-
+
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diff --git a/docs/reference/amt_vs_idv-1.png b/docs/reference/amt_vs_idv-1.png
index d62d10eb..0be4f2fa 100644
Binary files a/docs/reference/amt_vs_idv-1.png and b/docs/reference/amt_vs_idv-1.png differ
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index 2daef739..10e470c5 100644
--- a/docs/reference/amt_vs_idv.html
+++ b/docs/reference/amt_vs_idv.html
@@ -17,23 +17,27 @@
-
+
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+
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+
+
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+
+
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+
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-
+
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diff --git a/docs/reference/append_suffix.html b/docs/reference/append_suffix.html
index b69da160..80c3a037 100644
--- a/docs/reference/append_suffix.html
+++ b/docs/reference/append_suffix.html
@@ -17,23 +17,27 @@
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+
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+
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@@ -65,7 +68,7 @@
-
+
-
@@ -215,7 +215,7 @@ Contents
@@ -43,7 +43,7 @@
@@ -140,12 +140,12 @@
Installation
-# Install the lastest release from the CRAN
-install.packages('xpose')
-
-# Or install the development version from GitHub
-# install.packages('devtools')
-devtools::install_github('UUPharmacometrics/xpose')
+# Install the lastest release from the CRAN
+install.packages('xpose')
+
+# Or install the development version from GitHub
+# install.packages('devtools')
+devtools::install_github('UUPharmacometrics/xpose')
@@ -153,20 +153,20 @@
Glance at the data object
-
+xpdb
run001.lst overview:
- Software: nonmem 7.3.0
- - Attached files (memory usage 1.3 Mb):
+ - Attached files (memory usage 1.3 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
@@ -177,7 +177,7 @@
Model summary
-summary(xpdb, problem = 1)
+summary(xpdb, problem = 1)
Summary for problem no. 0 [Global information]
- Software @software : nonmem
@@ -223,33 +223,33 @@
Distribution plots
-eta_distrib(xpdb, labeller = 'label_value')
+eta_distrib(xpdb, labeller = 'label_value')
Minimization diagnostics
-prm_vs_iteration(xpdb, labeller = 'label_value')
+prm_vs_iteration(xpdb, labeller = 'label_value')
@@ -275,13 +275,13 @@
-
diff --git a/docs/reference/all_data_problem.html b/docs/reference/all_data_problem.html
index 6c3aab20..68a424e4 100644
--- a/docs/reference/all_data_problem.html
+++ b/docs/reference/all_data_problem.html
@@ -17,23 +17,27 @@
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diff --git a/docs/reference/all_file_problem.html b/docs/reference/all_file_problem.html
index a2c97f7b..83a32fb9 100644
--- a/docs/reference/all_file_problem.html
+++ b/docs/reference/all_file_problem.html
@@ -17,23 +17,27 @@
-
+
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@@ -65,7 +68,7 @@
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+
-
@@ -211,7 +211,7 @@ Contents
Installation
-# Install the lastest release from the CRAN
-install.packages('xpose')
-
-# Or install the development version from GitHub
-# install.packages('devtools')
-devtools::install_github('UUPharmacometrics/xpose')# Install the lastest release from the CRAN +install.packages('xpose') + +# Or install the development version from GitHub +# install.packages('devtools') +devtools::install_github('UUPharmacometrics/xpose')
@@ -153,20 +153,20 @@
Glance at the data object
-
+xpdb
run001.lst overview:
- Software: nonmem 7.3.0
- - Attached files (memory usage 1.3 Mb):
+ - Attached files (memory usage 1.3 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
@@ -177,7 +177,7 @@
Model summary
-summary(xpdb, problem = 1)
+summary(xpdb, problem = 1)
Summary for problem no. 0 [Global information]
- Software @software : nonmem
@@ -223,33 +223,33 @@
Distribution plots
-eta_distrib(xpdb, labeller = 'label_value')
+eta_distrib(xpdb, labeller = 'label_value')
Minimization diagnostics
-prm_vs_iteration(xpdb, labeller = 'label_value')
+prm_vs_iteration(xpdb, labeller = 'label_value')
@@ -275,13 +275,13 @@
-
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+++ b/docs/reference/all_data_problem.html
@@ -17,23 +17,27 @@
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@@ -47,7 +51,6 @@
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@@ -65,7 +68,7 @@
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@@ -207,7 +207,7 @@ Contents
Glance at the data object
- +xpdbrun001.lst overview:
- Software: nonmem 7.3.0
- - Attached files (memory usage 1.3 Mb):
+ - Attached files (memory usage 1.3 Mb):
+ obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
+ sim tabs: $prob no.2: simtab001.zip
+ output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
@@ -177,7 +177,7 @@
Model summary
-summary(xpdb, problem = 1)
+summary(xpdb, problem = 1)
Summary for problem no. 0 [Global information]
- Software @software : nonmem
@@ -223,33 +223,33 @@
Distribution plots
-eta_distrib(xpdb, labeller = 'label_value')
+eta_distrib(xpdb, labeller = 'label_value')
Minimization diagnostics
-prm_vs_iteration(xpdb, labeller = 'label_value')
+prm_vs_iteration(xpdb, labeller = 'label_value')
@@ -275,13 +275,13 @@
Contents
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Examp # Density plot of continuous covariates cov_distrib(xpdb_ex_pk, type = 'd')
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Contents
Contents
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See a
Examples
parsed_model <- get_code(xpdb_ex_pk)
-parsed_model#> # A tibble: 764 x 5
+parsed_model#> # A tibble: 764 x 5
#> problem level subroutine code comment
-#> * <int> <int> <chr> <chr> <chr>
-#> 1 0 0 oth "Mon Oct 16 13:34:28 CEST 2017" ""
-#> 2 0 0 oth "" "; 1. Based on: 00…
-#> 3 0 0 oth "" "; 2. Description:…
-#> 4 0 0 oth "" "; NONMEM PK examp…
-#> 5 1 1 pro "Parameter estimation" ""
-#> 6 1 2 inp "ID DOSE DV SCR AGE SEX CLASS W… ""
-#> 7 1 2 inp " CLCR AMT SS II EVID" ""
-#> 8 1 3 dat "../../mx19_2.csv IGNORE=@" ""
-#> 9 1 4 abb "DERIV2=NO" ""
-#> 10 1 5 sub "ADVAN2 TRANS1" ""
-#> # … with 754 more rows
+#> * <int> <int> <chr> <chr> <chr>
+#> 1 0 0 oth "Mon Oct 16 13:34:28 CEST 2017" ""
+#> 2 0 0 oth "" "; 1. Based on: 00…
+#> 3 0 0 oth "" "; 2. Description:…
+#> 4 0 0 oth "" "; NONMEM PK examp…
+#> 5 1 1 pro "Parameter estimation" ""
+#> 6 1 2 inp "ID DOSE DV SCR AGE SEX CLASS W… ""
+#> 7 1 2 inp " CLCR AMT SS II EVID" ""
+#> 8 1 3 dat "../../mx19_2.csv IGNORE=@" ""
+#> 9 1 4 abb "DERIV2=NO" ""
+#> 10 1 5 sub "ADVAN2 TRANS1" ""
+#> # … with 754 more rows
parsed_model <- get_code(xpdb_ex_pk)
-parsed_model
#> # A tibble: 764 x 5
+parsed_model
#> # A tibble: 764 x 5
#> problem level subroutine code comment
-#> * <int> <int> <chr> <chr> <chr>
-#> 1 0 0 oth "Mon Oct 16 13:34:28 CEST 2017" ""
-#> 2 0 0 oth "" "; 1. Based on: 00…
-#> 3 0 0 oth "" "; 2. Description:…
-#> 4 0 0 oth "" "; NONMEM PK examp…
-#> 5 1 1 pro "Parameter estimation" ""
-#> 6 1 2 inp "ID DOSE DV SCR AGE SEX CLASS W… ""
-#> 7 1 2 inp " CLCR AMT SS II EVID" ""
-#> 8 1 3 dat "../../mx19_2.csv IGNORE=@" ""
-#> 9 1 4 abb "DERIV2=NO" ""
-#> 10 1 5 sub "ADVAN2 TRANS1" ""
-#> # … with 754 more rows
+#> * <int> <int> <chr> <chr> <chr>
+#> 1 0 0 oth "Mon Oct 16 13:34:28 CEST 2017" ""
+#> 2 0 0 oth "" "; 1. Based on: 00…
+#> 3 0 0 oth "" "; 2. Description:…
+#> 4 0 0 oth "" "; NONMEM PK examp…
+#> 5 1 1 pro "Parameter estimation" ""
+#> 6 1 2 inp "ID DOSE DV SCR AGE SEX CLASS W… ""
+#> 7 1 2 inp " CLCR AMT SS II EVID" ""
+#> 8 1 3 dat "../../mx19_2.csv IGNORE=@" ""
+#> 9 1 4 abb "DERIV2=NO" ""
+#> 10 1 5 sub "ADVAN2 TRANS1" ""
+#> # … with 754 more rows
See a
Examples
# By table name
sdtab <- get_data(xpdb_ex_pk, 'sdtab001')
-sdtab#> # A tibble: 550 x 18
+sdtab#> # A tibble: 550 x 18
#> ID DOSE AMT SS II TIME TAD IPRED CWRES CPRED IWRES EVID
-#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 110 200 100 0 0 0 0 0 0 0 0 1
-#> 2 110 200 0 0 0 1 1 0.214 -2.83 0.584 2.40 0
-#> 3 110 200 0 0 0 1.5 1.5 0.296 -1.62 0.737 1.03 0
-#> 4 110 200 0 0 0 2 2 0.349 1.10 0.790 -1.71 0
-#> 5 110 200 0 0 0 4 4 0.392 0.585 0.588 -0.861 0
-#> 6 110 200 0 0 0 6 6 0.329 -0.0262 0.304 0.0457 0
-#> 7 110 200 0 0 0 8 8 0.249 0.0940 0.127 0.0132 0
-#> 8 112 200 100 0 0 0 0 0 0 0 0 1
-#> 9 112 200 0 0 0 0.5 0.5 0.556 0.0341 0.627 -0.275 0
-#> 10 112 200 0 0 0 1 1 0.712 -0.417 0.788 0.163 0
-#> # … with 540 more rows, and 6 more variables: A1 <dbl>, A2 <dbl>, DV <dbl>,
-#> # PRED <dbl>, RES <dbl>, WRES <dbl>
+#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 110 200 100 0 0 0 0 0 0 0 0 1
+#> 2 110 200 0 0 0 1 1 0.214 -2.83 0.584 2.40 0
+#> 3 110 200 0 0 0 1.5 1.5 0.296 -1.62 0.737 1.03 0
+#> 4 110 200 0 0 0 2 2 0.349 1.10 0.790 -1.71 0
+#> 5 110 200 0 0 0 4 4 0.392 0.585 0.588 -0.861 0
+#> 6 110 200 0 0 0 6 6 0.329 -0.0262 0.304 0.0457 0
+#> 7 110 200 0 0 0 8 8 0.249 0.0940 0.127 0.0132 0
+#> 8 112 200 100 0 0 0 0 0 0 0 0 1
+#> 9 112 200 0 0 0 0.5 0.5 0.556 0.0341 0.627 -0.275 0
+#> 10 112 200 0 0 0 1 1 0.712 -0.417 0.788 0.163 0
+#> # … with 540 more rows, and 6 more variables: A1 <dbl>, A2 <dbl>, DV <dbl>,
+#> # PRED <dbl>, RES <dbl>, WRES <dbl>
# By problem
tables <- get_data(xpdb_ex_pk, .problem = 1)
-tables#> # A tibble: 550 x 31
+tables#> # A tibble: 550 x 31
#> ID SEX MED1 MED2 DOSE AMT SS II TIME TAD IPRED CWRES
-#> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 110 1 0 1 200 100 0 0 0 0 0 0
-#> 2 110 1 0 1 200 0 0 0 1 1 0.214 -2.83
-#> 3 110 1 0 1 200 0 0 0 1.5 1.5 0.296 -1.62
-#> 4 110 1 0 1 200 0 0 0 2 2 0.349 1.10
-#> 5 110 1 0 1 200 0 0 0 4 4 0.392 0.585
-#> 6 110 1 0 1 200 0 0 0 6 6 0.329 -0.0262
-#> 7 110 1 0 1 200 0 0 0 8 8 0.249 0.0940
-#> 8 112 1 1 1 200 100 0 0 0 0 0 0
-#> 9 112 1 1 1 200 0 0 0 0.5 0.5 0.556 0.0341
-#> 10 112 1 1 1 200 0 0 0 1 1 0.712 -0.417
-#> # … with 540 more rows, and 19 more variables: CPRED <dbl>, IWRES <dbl>,
-#> # EVID <dbl>, A1 <dbl>, A2 <dbl>, DV <dbl>, PRED <dbl>, RES <dbl>,
-#> # WRES <dbl>, CLCR <dbl>, AGE <dbl>, WT <dbl>, KA <dbl>, CL <dbl>, V <dbl>,
-#> # ALAG1 <dbl>, ETA1 <dbl>, ETA2 <dbl>, ETA3 <dbl>
+#> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 110 1 0 1 200 100 0 0 0 0 0 0
+#> 2 110 1 0 1 200 0 0 0 1 1 0.214 -2.83
+#> 3 110 1 0 1 200 0 0 0 1.5 1.5 0.296 -1.62
+#> 4 110 1 0 1 200 0 0 0 2 2 0.349 1.10
+#> 5 110 1 0 1 200 0 0 0 4 4 0.392 0.585
+#> 6 110 1 0 1 200 0 0 0 6 6 0.329 -0.0262
+#> 7 110 1 0 1 200 0 0 0 8 8 0.249 0.0940
+#> 8 112 1 1 1 200 100 0 0 0 0 0 0
+#> 9 112 1 1 1 200 0 0 0 0.5 0.5 0.556 0.0341
+#> 10 112 1 1 1 200 0 0 0 1 1 0.712 -0.417
+#> # … with 540 more rows, and 19 more variables: CPRED <dbl>, IWRES <dbl>,
+#> # EVID <dbl>, A1 <dbl>, A2 <dbl>, DV <dbl>, PRED <dbl>, RES <dbl>,
+#> # WRES <dbl>, CLCR <dbl>, AGE <dbl>, WT <dbl>, KA <dbl>, CL <dbl>, V <dbl>,
+#> # ALAG1 <dbl>, ETA1 <dbl>, ETA2 <dbl>, ETA3 <dbl>#> run001.lst overview:
#> - Software: nonmem 7.3.0
-#> - Attached files (memory usage 1.3 Mb):
+#> - Attached files (memory usage 1.4 Mb):
#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: theme_xp_default
-#> - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+#> - Options: dir = data, quiet = FALSE, manual_import = NULL
# By table name
sdtab <- get_data(xpdb_ex_pk, 'sdtab001')
-sdtab
#> # A tibble: 550 x 18
+sdtab
#> # A tibble: 550 x 18
#> ID DOSE AMT SS II TIME TAD IPRED CWRES CPRED IWRES EVID
-#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 110 200 100 0 0 0 0 0 0 0 0 1
-#> 2 110 200 0 0 0 1 1 0.214 -2.83 0.584 2.40 0
-#> 3 110 200 0 0 0 1.5 1.5 0.296 -1.62 0.737 1.03 0
-#> 4 110 200 0 0 0 2 2 0.349 1.10 0.790 -1.71 0
-#> 5 110 200 0 0 0 4 4 0.392 0.585 0.588 -0.861 0
-#> 6 110 200 0 0 0 6 6 0.329 -0.0262 0.304 0.0457 0
-#> 7 110 200 0 0 0 8 8 0.249 0.0940 0.127 0.0132 0
-#> 8 112 200 100 0 0 0 0 0 0 0 0 1
-#> 9 112 200 0 0 0 0.5 0.5 0.556 0.0341 0.627 -0.275 0
-#> 10 112 200 0 0 0 1 1 0.712 -0.417 0.788 0.163 0
-#> # … with 540 more rows, and 6 more variables: A1 <dbl>, A2 <dbl>, DV <dbl>,
-#> # PRED <dbl>, RES <dbl>, WRES <dbl>
+#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 110 200 100 0 0 0 0 0 0 0 0 1
+#> 2 110 200 0 0 0 1 1 0.214 -2.83 0.584 2.40 0
+#> 3 110 200 0 0 0 1.5 1.5 0.296 -1.62 0.737 1.03 0
+#> 4 110 200 0 0 0 2 2 0.349 1.10 0.790 -1.71 0
+#> 5 110 200 0 0 0 4 4 0.392 0.585 0.588 -0.861 0
+#> 6 110 200 0 0 0 6 6 0.329 -0.0262 0.304 0.0457 0
+#> 7 110 200 0 0 0 8 8 0.249 0.0940 0.127 0.0132 0
+#> 8 112 200 100 0 0 0 0 0 0 0 0 1
+#> 9 112 200 0 0 0 0.5 0.5 0.556 0.0341 0.627 -0.275 0
+#> 10 112 200 0 0 0 1 1 0.712 -0.417 0.788 0.163 0
+#> # … with 540 more rows, and 6 more variables: A1 <dbl>, A2 <dbl>, DV <dbl>,
+#> # PRED <dbl>, RES <dbl>, WRES <dbl>
# By problem
tables <- get_data(xpdb_ex_pk, .problem = 1)
-tables
#> # A tibble: 550 x 31
+tables
#> # A tibble: 550 x 31
#> ID SEX MED1 MED2 DOSE AMT SS II TIME TAD IPRED CWRES
-#> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 110 1 0 1 200 100 0 0 0 0 0 0
-#> 2 110 1 0 1 200 0 0 0 1 1 0.214 -2.83
-#> 3 110 1 0 1 200 0 0 0 1.5 1.5 0.296 -1.62
-#> 4 110 1 0 1 200 0 0 0 2 2 0.349 1.10
-#> 5 110 1 0 1 200 0 0 0 4 4 0.392 0.585
-#> 6 110 1 0 1 200 0 0 0 6 6 0.329 -0.0262
-#> 7 110 1 0 1 200 0 0 0 8 8 0.249 0.0940
-#> 8 112 1 1 1 200 100 0 0 0 0 0 0
-#> 9 112 1 1 1 200 0 0 0 0.5 0.5 0.556 0.0341
-#> 10 112 1 1 1 200 0 0 0 1 1 0.712 -0.417
-#> # … with 540 more rows, and 19 more variables: CPRED <dbl>, IWRES <dbl>,
-#> # EVID <dbl>, A1 <dbl>, A2 <dbl>, DV <dbl>, PRED <dbl>, RES <dbl>,
-#> # WRES <dbl>, CLCR <dbl>, AGE <dbl>, WT <dbl>, KA <dbl>, CL <dbl>, V <dbl>,
-#> # ALAG1 <dbl>, ETA1 <dbl>, ETA2 <dbl>, ETA3 <dbl>
+#> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 110 1 0 1 200 100 0 0 0 0 0 0
+#> 2 110 1 0 1 200 0 0 0 1 1 0.214 -2.83
+#> 3 110 1 0 1 200 0 0 0 1.5 1.5 0.296 -1.62
+#> 4 110 1 0 1 200 0 0 0 2 2 0.349 1.10
+#> 5 110 1 0 1 200 0 0 0 4 4 0.392 0.585
+#> 6 110 1 0 1 200 0 0 0 6 6 0.329 -0.0262
+#> 7 110 1 0 1 200 0 0 0 8 8 0.249 0.0940
+#> 8 112 1 1 1 200 100 0 0 0 0 0 0
+#> 9 112 1 1 1 200 0 0 0 0.5 0.5 0.556 0.0341
+#> 10 112 1 1 1 200 0 0 0 1 1 0.712 -0.417
+#> # … with 540 more rows, and 19 more variables: CPRED <dbl>, IWRES <dbl>,
+#> # EVID <dbl>, A1 <dbl>, A2 <dbl>, DV <dbl>, PRED <dbl>, RES <dbl>,
+#> # WRES <dbl>, CLCR <dbl>, AGE <dbl>, WT <dbl>, KA <dbl>, CL <dbl>, V <dbl>,
+#> # ALAG1 <dbl>, ETA1 <dbl>, ETA2 <dbl>, ETA3 <dbl>
#> run001.lst overview:
#> - Software: nonmem 7.3.0
-#> - Attached files (memory usage 1.3 Mb):
+#> - Attached files (memory usage 1.4 Mb):
#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: theme_xp_default
-#> - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+#> - Options: dir = data, quiet = FALSE, manual_import = NULL
See a
Examples
# Single file (returns a tibble)
-ext_file <- get_file(xpdb_ex_pk, file = 'run001.ext')#> ext_file#> # A tibble: 28 x 16
+ext_file <- get_file(xpdb_ex_pk, file = 'run001.ext')#> ext_file#> # A tibble: 28 x 16
#> ITERATION THETA1 THETA2 THETA3 THETA4 THETA5 THETA6 THETA7 `SIGMA(1,1)`
-#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 0 25.4 1.47 7.45 0.214 0.200 0.00983 0.00601 1
-#> 2 1 26.3 1.26 7.35 0.219 0.217 0.00989 0.00602 1
-#> 3 2 25.6 1.47 7.29 0.216 0.212 0.00987 0.00603 1
-#> 4 3 26.8 1.49 5.76 0.213 0.213 0.00979 0.00628 1
-#> 5 4 26.7 1.49 5.69 0.213 0.212 0.00979 0.00629 1
-#> 6 5 26.7 1.49 5.66 0.213 0.212 0.00979 0.00630 1
-#> 7 6 26.6 1.49 5.03 0.210 0.217 0.0100 0.00652 1
-#> 8 7 26.6 1.49 4.93 0.205 0.217 0.0100 0.00658 1
-#> 9 8 26.6 1.48 4.62 0.211 0.217 0.00951 0.00735 1
-#> 10 9 26.6 1.46 4.41 0.209 0.217 0.00903 0.00874 1
-#> # … with 18 more rows, and 7 more variables: `OMEGA(1,1)` <dbl>,
-#> # `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
-#> # `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 0 25.4 1.47 7.45 0.214 0.200 0.00983 0.00601 1
+#> 2 1 26.3 1.26 7.35 0.219 0.217 0.00989 0.00602 1
+#> 3 2 25.6 1.47 7.29 0.216 0.212 0.00987 0.00603 1
+#> 4 3 26.8 1.49 5.76 0.213 0.213 0.00979 0.00628 1
+#> 5 4 26.7 1.49 5.69 0.213 0.212 0.00979 0.00629 1
+#> 6 5 26.7 1.49 5.66 0.213 0.212 0.00979 0.00630 1
+#> 7 6 26.6 1.49 5.03 0.210 0.217 0.0100 0.00652 1
+#> 8 7 26.6 1.49 4.93 0.205 0.217 0.0100 0.00658 1
+#> 9 8 26.6 1.48 4.62 0.211 0.217 0.00951 0.00735 1
+#> 10 9 26.6 1.46 4.41 0.209 0.217 0.00903 0.00874 1
+#> # … with 18 more rows, and 7 more variables: `OMEGA(1,1)` <dbl>,
+#> # `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
+#> # `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
# Multiple files (returns a list)
files <- get_file(xpdb_ex_pk, file = c('run001.ext', 'run001.phi'))#> files#> $run001.ext_prob_1_subprob_0_foce
-#> # A tibble: 28 x 16
+#> # A tibble: 28 x 16
#> ITERATION THETA1 THETA2 THETA3 THETA4 THETA5 THETA6 THETA7 `SIGMA(1,1)`
-#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 0 25.4 1.47 7.45 0.214 0.200 0.00983 0.00601 1
-#> 2 1 26.3 1.26 7.35 0.219 0.217 0.00989 0.00602 1
-#> 3 2 25.6 1.47 7.29 0.216 0.212 0.00987 0.00603 1
-#> 4 3 26.8 1.49 5.76 0.213 0.213 0.00979 0.00628 1
-#> 5 4 26.7 1.49 5.69 0.213 0.212 0.00979 0.00629 1
-#> 6 5 26.7 1.49 5.66 0.213 0.212 0.00979 0.00630 1
-#> 7 6 26.6 1.49 5.03 0.210 0.217 0.0100 0.00652 1
-#> 8 7 26.6 1.49 4.93 0.205 0.217 0.0100 0.00658 1
-#> 9 8 26.6 1.48 4.62 0.211 0.217 0.00951 0.00735 1
-#> 10 9 26.6 1.46 4.41 0.209 0.217 0.00903 0.00874 1
-#> # … with 18 more rows, and 7 more variables: `OMEGA(1,1)` <dbl>,
-#> # `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
-#> # `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 0 25.4 1.47 7.45 0.214 0.200 0.00983 0.00601 1
+#> 2 1 26.3 1.26 7.35 0.219 0.217 0.00989 0.00602 1
+#> 3 2 25.6 1.47 7.29 0.216 0.212 0.00987 0.00603 1
+#> 4 3 26.8 1.49 5.76 0.213 0.213 0.00979 0.00628 1
+#> 5 4 26.7 1.49 5.69 0.213 0.212 0.00979 0.00629 1
+#> 6 5 26.7 1.49 5.66 0.213 0.212 0.00979 0.00630 1
+#> 7 6 26.6 1.49 5.03 0.210 0.217 0.0100 0.00652 1
+#> 8 7 26.6 1.49 4.93 0.205 0.217 0.0100 0.00658 1
+#> 9 8 26.6 1.48 4.62 0.211 0.217 0.00951 0.00735 1
+#> 10 9 26.6 1.46 4.41 0.209 0.217 0.00903 0.00874 1
+#> # … with 18 more rows, and 7 more variables: `OMEGA(1,1)` <dbl>,
+#> # `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
+#> # `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
#>
#> $run001.phi_prob_1_subprob_0_foce
-#> # A tibble: 74 x 12
+#> # A tibble: 74 x 12
#> SUBJECT_NO ID `ETA(1)` `ETA(2)` `ETA(3)` `ETC(1,1)` `ETC(2,1)` `ETC(2,2)`
-#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 1 110 -0.0370 -0.00596 -2.14 0.0258 -0.00961 0.0339
-#> 2 2 112 -0.0495 0.122 -0.0235 0.0140 -0.00219 0.0138
-#> 3 3 113 0.332 0.0259 -1.15 0.00995 0.000300 0.0257
-#> 4 4 121 0.304 0.249 1.89 0.00741 0.00362 0.0137
-#> 5 5 123 0.0271 -0.0801 1.09 0.00794 0.00267 0.0135
-#> 6 6 124 0.332 0.111 -1.54 0.0110 -0.00219 0.0307
-#> 7 7 126 0.190 -0.0315 2.20 0.00785 0.00300 0.0136
-#> 8 8 127 0.190 -0.0750 0.415 0.0112 0.00469 0.0153
-#> 9 9 128 -0.0138 -0.00130 -1.27 0.0120 0.0000333 0.0287
-#> 10 10 210 -0.307 -0.108 -0.0168 0.00988 0.00112 0.0163
-#> # … with 64 more rows, and 4 more variables: `ETC(3,1)` <dbl>,
-#> # `ETC(3,2)` <dbl>, `ETC(3,3)` <dbl>, OBJ <dbl>
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 1 110 -0.0370 -0.00596 -2.14 0.0258 -0.00961 0.0339
+#> 2 2 112 -0.0495 0.122 -0.0235 0.0140 -0.00219 0.0138
+#> 3 3 113 0.332 0.0259 -1.15 0.00995 0.000300 0.0257
+#> 4 4 121 0.304 0.249 1.89 0.00741 0.00362 0.0137
+#> 5 5 123 0.0271 -0.0801 1.09 0.00794 0.00267 0.0135
+#> 6 6 124 0.332 0.111 -1.54 0.0110 -0.00219 0.0307
+#> 7 7 126 0.190 -0.0315 2.20 0.00785 0.00300 0.0136
+#> 8 8 127 0.190 -0.0750 0.415 0.0112 0.00469 0.0153
+#> 9 9 128 -0.0138 -0.00130 -1.27 0.0120 0.0000333 0.0287
+#> 10 10 210 -0.307 -0.108 -0.0168 0.00988 0.00112 0.0163
+#> # … with 64 more rows, and 4 more variables: `ETC(3,1)` <dbl>,
+#> # `ETC(3,2)` <dbl>, `ETC(3,3)` <dbl>, OBJ <dbl>
#> #> run001.lst overview:
#> - Software: nonmem 7.3.0
-#> - Attached files (memory usage 1.3 Mb):
+#> - Attached files (memory usage 1.4 Mb):
#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: theme_xp_default
-#> - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+#> - Options: dir = data, quiet = FALSE, manual_import = NULL
# Single file (returns a tibble)
-ext_file <- get_file(xpdb_ex_pk, file = 'run001.ext')
#>
ext_file
#> # A tibble: 28 x 16
+ext_file <- get_file(xpdb_ex_pk, file = 'run001.ext')
#>
ext_file
#> # A tibble: 28 x 16
#> ITERATION THETA1 THETA2 THETA3 THETA4 THETA5 THETA6 THETA7 `SIGMA(1,1)`
-#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 0 25.4 1.47 7.45 0.214 0.200 0.00983 0.00601 1
-#> 2 1 26.3 1.26 7.35 0.219 0.217 0.00989 0.00602 1
-#> 3 2 25.6 1.47 7.29 0.216 0.212 0.00987 0.00603 1
-#> 4 3 26.8 1.49 5.76 0.213 0.213 0.00979 0.00628 1
-#> 5 4 26.7 1.49 5.69 0.213 0.212 0.00979 0.00629 1
-#> 6 5 26.7 1.49 5.66 0.213 0.212 0.00979 0.00630 1
-#> 7 6 26.6 1.49 5.03 0.210 0.217 0.0100 0.00652 1
-#> 8 7 26.6 1.49 4.93 0.205 0.217 0.0100 0.00658 1
-#> 9 8 26.6 1.48 4.62 0.211 0.217 0.00951 0.00735 1
-#> 10 9 26.6 1.46 4.41 0.209 0.217 0.00903 0.00874 1
-#> # … with 18 more rows, and 7 more variables: `OMEGA(1,1)` <dbl>,
-#> # `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
-#> # `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 0 25.4 1.47 7.45 0.214 0.200 0.00983 0.00601 1
+#> 2 1 26.3 1.26 7.35 0.219 0.217 0.00989 0.00602 1
+#> 3 2 25.6 1.47 7.29 0.216 0.212 0.00987 0.00603 1
+#> 4 3 26.8 1.49 5.76 0.213 0.213 0.00979 0.00628 1
+#> 5 4 26.7 1.49 5.69 0.213 0.212 0.00979 0.00629 1
+#> 6 5 26.7 1.49 5.66 0.213 0.212 0.00979 0.00630 1
+#> 7 6 26.6 1.49 5.03 0.210 0.217 0.0100 0.00652 1
+#> 8 7 26.6 1.49 4.93 0.205 0.217 0.0100 0.00658 1
+#> 9 8 26.6 1.48 4.62 0.211 0.217 0.00951 0.00735 1
+#> 10 9 26.6 1.46 4.41 0.209 0.217 0.00903 0.00874 1
+#> # … with 18 more rows, and 7 more variables: `OMEGA(1,1)` <dbl>,
+#> # `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
+#> # `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
# Multiple files (returns a list)
files <- get_file(xpdb_ex_pk, file = c('run001.ext', 'run001.phi'))
#>
files
#> $run001.ext_prob_1_subprob_0_foce
-#> # A tibble: 28 x 16
+#> # A tibble: 28 x 16
#> ITERATION THETA1 THETA2 THETA3 THETA4 THETA5 THETA6 THETA7 `SIGMA(1,1)`
-#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 0 25.4 1.47 7.45 0.214 0.200 0.00983 0.00601 1
-#> 2 1 26.3 1.26 7.35 0.219 0.217 0.00989 0.00602 1
-#> 3 2 25.6 1.47 7.29 0.216 0.212 0.00987 0.00603 1
-#> 4 3 26.8 1.49 5.76 0.213 0.213 0.00979 0.00628 1
-#> 5 4 26.7 1.49 5.69 0.213 0.212 0.00979 0.00629 1
-#> 6 5 26.7 1.49 5.66 0.213 0.212 0.00979 0.00630 1
-#> 7 6 26.6 1.49 5.03 0.210 0.217 0.0100 0.00652 1
-#> 8 7 26.6 1.49 4.93 0.205 0.217 0.0100 0.00658 1
-#> 9 8 26.6 1.48 4.62 0.211 0.217 0.00951 0.00735 1
-#> 10 9 26.6 1.46 4.41 0.209 0.217 0.00903 0.00874 1
-#> # … with 18 more rows, and 7 more variables: `OMEGA(1,1)` <dbl>,
-#> # `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
-#> # `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 0 25.4 1.47 7.45 0.214 0.200 0.00983 0.00601 1
+#> 2 1 26.3 1.26 7.35 0.219 0.217 0.00989 0.00602 1
+#> 3 2 25.6 1.47 7.29 0.216 0.212 0.00987 0.00603 1
+#> 4 3 26.8 1.49 5.76 0.213 0.213 0.00979 0.00628 1
+#> 5 4 26.7 1.49 5.69 0.213 0.212 0.00979 0.00629 1
+#> 6 5 26.7 1.49 5.66 0.213 0.212 0.00979 0.00630 1
+#> 7 6 26.6 1.49 5.03 0.210 0.217 0.0100 0.00652 1
+#> 8 7 26.6 1.49 4.93 0.205 0.217 0.0100 0.00658 1
+#> 9 8 26.6 1.48 4.62 0.211 0.217 0.00951 0.00735 1
+#> 10 9 26.6 1.46 4.41 0.209 0.217 0.00903 0.00874 1
+#> # … with 18 more rows, and 7 more variables: `OMEGA(1,1)` <dbl>,
+#> # `OMEGA(2,1)` <dbl>, `OMEGA(2,2)` <dbl>, `OMEGA(3,1)` <dbl>,
+#> # `OMEGA(3,2)` <dbl>, `OMEGA(3,3)` <dbl>, OBJ <dbl>
#>
#> $run001.phi_prob_1_subprob_0_foce
-#> # A tibble: 74 x 12
+#> # A tibble: 74 x 12
#> SUBJECT_NO ID `ETA(1)` `ETA(2)` `ETA(3)` `ETC(1,1)` `ETC(2,1)` `ETC(2,2)`
-#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 1 110 -0.0370 -0.00596 -2.14 0.0258 -0.00961 0.0339
-#> 2 2 112 -0.0495 0.122 -0.0235 0.0140 -0.00219 0.0138
-#> 3 3 113 0.332 0.0259 -1.15 0.00995 0.000300 0.0257
-#> 4 4 121 0.304 0.249 1.89 0.00741 0.00362 0.0137
-#> 5 5 123 0.0271 -0.0801 1.09 0.00794 0.00267 0.0135
-#> 6 6 124 0.332 0.111 -1.54 0.0110 -0.00219 0.0307
-#> 7 7 126 0.190 -0.0315 2.20 0.00785 0.00300 0.0136
-#> 8 8 127 0.190 -0.0750 0.415 0.0112 0.00469 0.0153
-#> 9 9 128 -0.0138 -0.00130 -1.27 0.0120 0.0000333 0.0287
-#> 10 10 210 -0.307 -0.108 -0.0168 0.00988 0.00112 0.0163
-#> # … with 64 more rows, and 4 more variables: `ETC(3,1)` <dbl>,
-#> # `ETC(3,2)` <dbl>, `ETC(3,3)` <dbl>, OBJ <dbl>
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 1 110 -0.0370 -0.00596 -2.14 0.0258 -0.00961 0.0339
+#> 2 2 112 -0.0495 0.122 -0.0235 0.0140 -0.00219 0.0138
+#> 3 3 113 0.332 0.0259 -1.15 0.00995 0.000300 0.0257
+#> 4 4 121 0.304 0.249 1.89 0.00741 0.00362 0.0137
+#> 5 5 123 0.0271 -0.0801 1.09 0.00794 0.00267 0.0135
+#> 6 6 124 0.332 0.111 -1.54 0.0110 -0.00219 0.0307
+#> 7 7 126 0.190 -0.0315 2.20 0.00785 0.00300 0.0136
+#> 8 8 127 0.190 -0.0750 0.415 0.0112 0.00469 0.0153
+#> 9 9 128 -0.0138 -0.00130 -1.27 0.0120 0.0000333 0.0287
+#> 10 10 210 -0.307 -0.108 -0.0168 0.00988 0.00112 0.0163
+#> # … with 64 more rows, and 4 more variables: `ETC(3,1)` <dbl>,
+#> # `ETC(3,2)` <dbl>, `ETC(3,3)` <dbl>, OBJ <dbl>
#>
#> run001.lst overview:
#> - Software: nonmem 7.3.0
-#> - Attached files (memory usage 1.3 Mb):
+#> - Attached files (memory usage 1.4 Mb):
#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: theme_xp_default
-#> - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+#> - Options: dir = data, quiet = FALSE, manual_import = NULL
See a
Examples
#> # A tibble: 48 x 5
+special#> # A tibble: 48 x 5
#> problem subprob descr label value
-#> <dbl> <dbl> <chr> <chr> <chr>
-#> 1 0 0 Run description descr NONMEM PK example for xpose
-#> 2 0 0 Run directory dir analysis/models/pk/
-#> 3 0 0 Run errors errors na
-#> 4 0 0 ESAMPLE seed number esampleseed na
-#> 5 0 0 Run file file run001.lst
-#> 6 0 0 Number of ESAMPLE nesample na
-#> 7 0 0 Reference model ref 000
-#> 8 0 0 Run number run run001
-#> 9 0 0 Software software nonmem
-#> 10 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
-#> # … with 38 more rows
+#> <dbl> <dbl> <chr> <chr> <chr>
+#> 1 0 0 Run description descr NONMEM PK example for xpose
+#> 2 0 0 Run directory dir data
+#> 3 0 0 Run errors errors na
+#> 4 0 0 ESAMPLE seed number esampleseed na
+#> 5 0 0 Run file file run001.lst
+#> 6 0 0 Number of ESAMPLE nesample na
+#> 7 0 0 Reference model ref 000
+#> 8 0 0 Run number run run001
+#> 9 0 0 Software software nonmem
+#> 10 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
+#> # … with 38 more rows
#> # A tibble: 48 x 5
+special
#> # A tibble: 48 x 5
#> problem subprob descr label value
-#> <dbl> <dbl> <chr> <chr> <chr>
-#> 1 0 0 Run description descr NONMEM PK example for xpose
-#> 2 0 0 Run directory dir analysis/models/pk/
-#> 3 0 0 Run errors errors na
-#> 4 0 0 ESAMPLE seed number esampleseed na
-#> 5 0 0 Run file file run001.lst
-#> 6 0 0 Number of ESAMPLE nesample na
-#> 7 0 0 Reference model ref 000
-#> 8 0 0 Run number run run001
-#> 9 0 0 Software software nonmem
-#> 10 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
-#> # … with 38 more rows
+#> <dbl> <dbl> <chr> <chr> <chr>
+#> 1 0 0 Run description descr NONMEM PK example for xpose
+#> 2 0 0 Run directory dir data
+#> 3 0 0 Run errors errors na
+#> 4 0 0 ESAMPLE seed number esampleseed na
+#> 5 0 0 Run file file run001.lst
+#> 6 0 0 Number of ESAMPLE nesample na
+#> 7 0 0 Reference model ref 000
+#> 8 0 0 Run number run run001
+#> 9 0 0 Software software nonmem
+#> 10 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
+#> # … with 38 more rows
See a
Examples
run_summary <- get_summary(xpdb_ex_pk)
-run_summary#> # A tibble: 48 x 5
+run_summary#> # A tibble: 48 x 5
#> problem subprob descr label value
-#> <dbl> <dbl> <chr> <chr> <chr>
-#> 1 0 0 Run description descr NONMEM PK example for xpose
-#> 2 0 0 Run directory dir analysis/models/pk/
-#> 3 0 0 Run errors errors na
-#> 4 0 0 ESAMPLE seed number esampleseed na
-#> 5 0 0 Run file file run001.lst
-#> 6 0 0 Number of ESAMPLE nesample na
-#> 7 0 0 Reference model ref 000
-#> 8 0 0 Run number run run001
-#> 9 0 0 Software software nonmem
-#> 10 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
-#> # … with 38 more rows
+#> <dbl> <dbl> <chr> <chr> <chr>
+#> 1 0 0 Run description descr NONMEM PK example for xpose
+#> 2 0 0 Run directory dir data
+#> 3 0 0 Run errors errors na
+#> 4 0 0 ESAMPLE seed number esampleseed na
+#> 5 0 0 Run file file run001.lst
+#> 6 0 0 Number of ESAMPLE nesample na
+#> 7 0 0 Reference model ref 000
+#> 8 0 0 Run number run run001
+#> 9 0 0 Software software nonmem
+#> 10 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
+#> # … with 38 more rows
run_summary <- get_summary(xpdb_ex_pk)
-run_summary
#> # A tibble: 48 x 5
+run_summary
#> # A tibble: 48 x 5
#> problem subprob descr label value
-#> <dbl> <dbl> <chr> <chr> <chr>
-#> 1 0 0 Run description descr NONMEM PK example for xpose
-#> 2 0 0 Run directory dir analysis/models/pk/
-#> 3 0 0 Run errors errors na
-#> 4 0 0 ESAMPLE seed number esampleseed na
-#> 5 0 0 Run file file run001.lst
-#> 6 0 0 Number of ESAMPLE nesample na
-#> 7 0 0 Reference model ref 000
-#> 8 0 0 Run number run run001
-#> 9 0 0 Software software nonmem
-#> 10 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
-#> # … with 38 more rows
+#> <dbl> <dbl> <chr> <chr> <chr>
+#> 1 0 0 Run description descr NONMEM PK example for xpose
+#> 2 0 0 Run directory dir data
+#> 3 0 0 Run errors errors na
+#> 4 0 0 ESAMPLE seed number esampleseed na
+#> 5 0 0 Run file file run001.lst
+#> 6 0 0 Number of ESAMPLE nesample na
+#> 7 0 0 Reference model ref 000
+#> 8 0 0 Run number run run001
+#> 9 0 0 Software software nonmem
+#> 10 0 0 Run start time timestart Mon Oct 16 13:34:28 CEST 2017
+#> # … with 38 more rows
Examp dv_vs_ipred(facets = 'SEX')
Import tables and output files from a NLME run to R.
+
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diff --git a/docs/reference/index_table.html b/docs/reference/index_table.html
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diff --git a/docs/reference/irep.html b/docs/reference/irep.html
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diff --git a/docs/reference/is.formula.html b/docs/reference/is.formula.html
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# List output tables data
list_data(xpdb_ex_pk)#> Data:
#> problem simtab data modified
-#> <int> <lgl> <list> <lgl>
-#> 1 1 FALSE <tibble [550 × 31]> FALSE
-#> 2 2 TRUE <tibble [11,000 × 12]> FALSE
+#> <int> <lgl> <list> <lgl>
+#> 1 1 FALSE <tibble [550 × 31]> FALSE
+#> 2 2 TRUE <tibble [11,000 × 12]> FALSE
# List output files data
list_files(xpdb_ex_pk)#> Files:
#> name extension problem subprob method data modified
-#> <chr> <chr> <dbl> <dbl> <chr> <list> <lgl>
-#> 1 run001.cor cor 1 0 foce <tibble [14 × 15]> FALSE
-#> 2 run001.cov cov 1 0 foce <tibble [14 × 15]> FALSE
-#> 3 run001.ext ext 1 0 foce <tibble [28 × 16]> FALSE
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-#> 5 run001.phi phi 1 0 foce <tibble [74 × 12]> FALSE
-#> 6 run001.shk shk 1 0 foce <tibble [7 × 5]> FALSE
+#> <chr> <chr> <dbl> <dbl> <chr> <list> <lgl>
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+#> 2 run001.cov cov 1 0 foce <tibble [14 × 15]> FALSE
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+#> 5 run001.phi phi 1 0 foce <tibble [74 × 12]> FALSE
+#> 6 run001.shk shk 1 0 foce <tibble [7 × 5]> FALSE #> Specials:
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-#> 1 3 vpc continuous <named list [19]> FALSE
+#> <dbl> <chr> <chr> <list> <lgl>
+#> 1 3 vpc continuous <named list [19]> FALSE
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mutate() adds new variables and preserves existing ones.
-select() keeps only the listed variables; rename() keeps all variables.
+select() keeps only the listed variables; rename() keeps all variables.
# S3 method for xpose_data
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select(.data, ..., .problem, .source, .where)
# S3 method for xpose_data
-rename(.data, ..., .problem, .source, .where)
+rename(.data, ..., .problem, .source, .where)
Arguments
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# Rename/select columns
xpdb_ex_pk %>%
select(ID:TAD, DV, EVID) %>%
- rename(TSLD = TAD) %>%
+ rename(TSLD = TAD) %>%
dv_vs_idv(aes(x = TSLD))#> #> #> #>
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#> run001.lst overview:
#> - Software: nonmem 7.3.0
-#> - Attached files (memory usage 1.3 Mb):
+#> - Attached files (memory usage 1.4 Mb):
#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: theme_xp_default
-#> - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+#> - Options: dir = data, quiet = FALSE, manual_import = NULL
# Or simply by writting the xpdb name
xpdb_ex_pk#> run001.lst overview:
#> - Software: nonmem 7.3.0
-#> - Attached files (memory usage 1.3 Mb):
+#> - Attached files (memory usage 1.4 Mb):
#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: theme_xp_default
-#> - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+#> - Options: dir = data, quiet = FALSE, manual_import = NULL
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print(xpdb_ex_pk)#> run001.lst overview:
#> - Software: nonmem 7.3.0
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#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
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#> - xp_theme: theme_xp_default
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diff --git a/docs/reference/xpose-package.html b/docs/reference/xpose-package.html
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processing burden and improve diagnostics commonly associated the development of non-linear mixed effect models.
To learn more about xpose, visit our website: https://UUPharmacometrics.github.io/xpose/." />
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diff --git a/docs/reference/xpose_data.html b/docs/reference/xpose_data.html
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diff --git a/docs/reference/xpose_panels.html b/docs/reference/xpose_panels.html
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`facets` is a string, and `facet_grid_paginate` if `facets` is a formula.
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diff --git a/docs/reference/xpose_save.html b/docs/reference/xpose_save.html
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diff --git a/tests/testthat/data/ctrl_psn_vpc.RData b/tests/testthat/data/ctrl_psn_vpc.RData
index 60aef7ab..2f7ca2d3 100755
Binary files a/tests/testthat/data/ctrl_psn_vpc.RData and b/tests/testthat/data/ctrl_psn_vpc.RData differ
diff --git a/tests/testthat/data/ctrl_special.RData b/tests/testthat/data/ctrl_special.RData
index 16f72500..ef478109 100755
Binary files a/tests/testthat/data/ctrl_special.RData and b/tests/testthat/data/ctrl_special.RData differ
diff --git a/tests/testthat/test-console_outputs.R b/tests/testthat/test-console_outputs.R
index 9f5e540e..96b55210 100755
--- a/tests/testthat/test-console_outputs.R
+++ b/tests/testthat/test-console_outputs.R
@@ -10,11 +10,11 @@ xpdb_modified <- xpdb_ex_pk %>%
mem_use_1 <- format(utils::object.size(xpdb_ex_pk), units = 'auto')
mem_use_2 <- format(utils::object.size(xpdb_modified), units = 'auto')
-print_text <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0 \n - Attached files (memory usage ', mem_use_1, '): \n + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001 \n + sim tabs: $prob no.2: simtab001.zip \n + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk \n + special: \n - gg_theme: theme_readable \n - xp_theme: theme_xp_default \n - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL')
-print_text_modified <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0 \n - Attached files (memory usage ', mem_use_2, '): \n + obs tabs: $prob no.1 (modified): catab001.csv, cotab001, patab001, sdtab001 \n + sim tabs: $prob no.2 (modified): simtab001.zip \n + output files: run001.cor, run001.cov, run001.ext (modified), run001.grd, run001.phi, run001.shk \n + special: vpc continuous (#3, modified) \n - gg_theme: theme_readable \n - xp_theme: theme_xp_default \n - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL')
+print_text <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0 \n - Attached files (memory usage ', mem_use_1, '): \n + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001 \n + sim tabs: $prob no.2: simtab001.zip \n + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk \n + special: \n - gg_theme: theme_readable \n - xp_theme: theme_xp_default \n - Options: dir = data, quiet = FALSE, manual_import = NULL')
+print_text_modified <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0 \n - Attached files (memory usage ', mem_use_2, '): \n + obs tabs: $prob no.1 (modified): catab001.csv, cotab001, patab001, sdtab001 \n + sim tabs: $prob no.2 (modified): simtab001.zip \n + output files: run001.cor, run001.cov, run001.ext (modified), run001.grd, run001.phi, run001.shk \n + special: vpc continuous (#3, modified) \n - gg_theme: theme_readable \n - xp_theme: theme_xp_default \n - Options: dir = data, quiet = FALSE, manual_import = NULL')
prm_text_1 <- '\nReporting transformed parameters:\nFor the OMEGA and SIGMA matrices, values are reported as standard deviations for the diagonal elements and as correlations for the off-diagonal elements. The relative standard errors (RSE) for OMEGA and SIGMA are reported on the approximate standard deviation scale (SE/variance estimate)/2. Use `transform = FALSE` to report untransformed parameters.\n\nEstimates for $prob no.1, subprob no.0, method foce\n Parameter Label Value RSE\n THETA1 TVCL 26.29 0.03391\n THETA2 TVV 1.348 0.0325\n THETA3 TVKA 4.204 0.1925\n THETA4 LAG 0.208 0.07554\n THETA5 Prop. Err 0.2046 0.1097\n THETA6 Add. Err 0.01055 0.3466\n THETA7 CRCL on CL 0.007172 0.2366\n OMEGA(1,1) IIV CL 0.2701 0.08616\n OMEGA(2,2) IIV V 0.195 0.1643\n OMEGA(3,3) IIV KA 1.381 0.1463\n SIGMA(1,1) 1 fix - '
prm_text_2 <- '\nReporting untransformed parameters:\nFor the OMEGA and SIGMA matrices, values are reported as variances for the diagonal elements and as covariances for the off-diagonal elements.\n\nEstimates for $prob no.1, subprob no.0, method foce\n Parameter Label Value SE\n THETA1 TVCL 26.29 0.8915\n THETA2 TVV 1.348 0.04381\n THETA3 TVKA 4.204 0.8091\n THETA4 LAG 0.208 0.01571\n THETA5 Prop. Err 0.2046 0.02244\n THETA6 Add. Err 0.01055 0.003658\n THETA7 CRCL on CL 0.007172 0.001697\n OMEGA(1,1) IIV CL 0.07295 0.01257\n OMEGA(2,2) IIV V 0.03802 0.0125\n OMEGA(3,3) IIV KA 1.907 0.5582\n SIGMA(1,1) 1 fix - '
-summary_text <- '\nSummary for problem no. 0 [Global information] \n - Software @software : nonmem\n - Software version @version : 7.3.0\n - Run directory @dir : analysis/models/pk/\n - Run file @file : run001.lst\n - Run number @run : run001\n - Reference model @ref : 000\n - Run description @descr : NONMEM PK example for xpose\n - Run start time @timestart : Mon Oct 16 13:34:28 CEST 2017\n - Run stop time @timestop : Mon Oct 16 13:34:35 CEST 2017\n\nSummary for problem no. 1 [Parameter estimation] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - ADVAN @subroutine : 2\n - Estimation method @method : foce-i\n - Termination message @term : MINIMIZATION SUCCESSFUL\n - Estimation runtime @runtime : 00:00:02\n - Objective function value @ofv : -1403.905\n - Number of significant digits @nsig : 3.3\n - Covariance step runtime @covtime : 00:00:03\n - Condition number @condn : 21.5\n - Eta shrinkage @etashk : 9.3 [1], 28.7 [2], 23.7 [3]\n - Epsilon shrinkage @epsshk : 14.9 [1]\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n\nSummary for problem no. 2 [Model simulations] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - Estimation method @method : sim\n - Number of simulations @nsim : 20\n - Simulation seed @simseed : 221287\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n (WARNING 22) WITH $MSFI AND \"SUBPROBS\", \"TRUE=FINAL\" ...'
+summary_text <- '\nSummary for problem no. 0 [Global information] \n - Software @software : nonmem\n - Software version @version : 7.3.0\n - Run directory @dir : data\n - Run file @file : run001.lst\n - Run number @run : run001\n - Reference model @ref : 000\n - Run description @descr : NONMEM PK example for xpose\n - Run start time @timestart : Mon Oct 16 13:34:28 CEST 2017\n - Run stop time @timestop : Mon Oct 16 13:34:35 CEST 2017\n\nSummary for problem no. 1 [Parameter estimation] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - ADVAN @subroutine : 2\n - Estimation method @method : foce-i\n - Termination message @term : MINIMIZATION SUCCESSFUL\n - Estimation runtime @runtime : 00:00:02\n - Objective function value @ofv : -1403.905\n - Number of significant digits @nsig : 3.3\n - Covariance step runtime @covtime : 00:00:03\n - Condition number @condn : 21.5\n - Eta shrinkage @etashk : 9.3 [1], 28.7 [2], 23.7 [3]\n - Epsilon shrinkage @epsshk : 14.9 [1]\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n\nSummary for problem no. 2 [Model simulations] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - Estimation method @method : sim\n - Number of simulations @nsim : 20\n - Simulation seed @simseed : 221287\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n (WARNING 22) WITH $MSFI AND \"SUBPROBS\", \"TRUE=FINAL\" ...'
vars_text <- '\nList of available variables for problem no. 1 \n - Subject identifier (id) : ID\n - Dependent variable (dv) : DV\n - Independent variable (idv) : TIME\n - Dose amount (amt) : AMT\n - Event identifier (evid) : EVID\n - Model typical predictions (pred) : PRED\n - Model individual predictions (ipred) : IPRED\n - Model parameter (param) : KA, CL, V, ALAG1\n - Eta (eta) : ETA1, ETA2, ETA3\n - Residuals (res) : CWRES, IWRES, RES, WRES\n - Categorical covariates (catcov) : SEX, MED1, MED2\n - Continuous covariates (contcov) : CLCR, AGE, WT\n - Compartment amounts (a) : A1, A2\n - Not attributed (na) : DOSE, SS, II, TAD, CPRED'
# Tests start here --------------------------------------------------------
diff --git a/tests/testthat/test-edits.R b/tests/testthat/test-edits.R
index 7fcbc4cc..0af46f74 100755
--- a/tests/testthat/test-edits.R
+++ b/tests/testthat/test-edits.R
@@ -12,7 +12,8 @@ ctrl_tab_2 <- xpdb_ex_pk %>%
group_by(ID, SEX, .problem = 1) %>%
summarize(CMAX = max(DV), .problem = 1) %>%
ungroup(.problem = 1) %>%
- get_data(.problem = 1)
+ get_data(.problem = 1) %>%
+ ungroup()
test_xpdb_1 <- vpc_data(xpdb_ex_pk, opt = vpc_opt(n_bins = 2), quiet = TRUE)
ctrl_xpdb_1 <- test_xpdb_1
@@ -32,14 +33,15 @@ test_that('xpdb_edits checks error properly', {
})
test_that('xpdb_edits works properly', {
- expect_equal(filter.xpose_data(.data = xpdb_ex_pk, ID == 110, TIME > 10, .problem = 1) %>% get_data(.problem = 1),
+ expect_equal(filter.xpose_data(.data = xpdb_ex_pk, ID == 110, TIME > 10, .problem = 1) %>%
+ get_data(.problem = 1),
xpdb_ex_pk %>% get_data(.problem = 1) %>% filter(.$ID == 110, .$TIME > 10))
expect_equal(filter.xpose_data(.data = xpdb_ex_pk, ID == 110, .problem = 1, .source = 'phi') %>%
slice.xpose_data(1:3, .source = 'phi') %>%
mutate.xpose_data(TEST = 'Ok', .source = 'phi') %>%
get_file(ext = 'phi', quiet = TRUE),
- xpdb_ex_pk %>% get_file(ext = 'phi', quiet = TRUE) %>% filter(.$ID == 110)
- %>% slice(1:3) %>% mutate(TEST = 'Ok'))
+ xpdb_ex_pk %>% get_file(ext = 'phi', quiet = TRUE) %>% filter(.$ID == 110) %>%
+ slice(1:3) %>% mutate(TEST = 'Ok'))
expect_equal(ctrl_tab_1, xpdb_ex_pk %>%
get_data(.problem = 1) %>%
mutate(DV = log(DV)) %>%
@@ -81,3 +83,4 @@ test_that('n() works', {
expect_equal(xpdb_ex_pk %>% mutate(N = 1:n(), .source = 'ext') %>% get_file(ext = 'ext', quiet = TRUE),
xpdb_ex_pk %>% get_file(ext = 'ext', quiet = TRUE) %>% mutate(N = 1:n()))
})
+
diff --git a/tests/testthat/test-fetch_data.R b/tests/testthat/test-fetch_data.R
index 2ef13780..dedd196f 100755
--- a/tests/testthat/test-fetch_data.R
+++ b/tests/testthat/test-fetch_data.R
@@ -34,9 +34,9 @@ test_that('fetch_data can get simple data', {
expect_equal(attr(imported_data, 'problem'), 1)
expect_equal(attr(imported_data, 'simtab'), FALSE)
expect_equal(attr(imported_data, 'source'), 'data')
-
+
# Check output
- expect_equal(imported_data, xpdb_ex_pk$data$data[[1]][xpdb_ex_pk$data$data[[1]]$EVID == 0, ])
+ expect_equivalent(imported_data, xpdb_ex_pk$data$data[[1]][xpdb_ex_pk$data$data[[1]]$EVID == 0, ])
})
test_that('fetch_data can tidy data', {
@@ -46,7 +46,7 @@ test_that('fetch_data can tidy data', {
tidy = TRUE, value_col = c('IPRED','PRED', 'DV'))
# Check output
- expect_equal(imported_tidy_data,
+ expect_equivalent(imported_tidy_data,
xpdb_ex_pk$data$data[[1]][xpdb_ex_pk$data$data[[1]]$EVID == 0,
c('ID', 'TIME', 'IPRED', 'PRED', 'DV')] %>%
tidyr::gather(value = 'value', key = 'variable',
@@ -55,6 +55,7 @@ test_that('fetch_data can tidy data', {
})
test_that('fetch_data can get file data', {
- expect_equal(fetch_data(xpdb_ex_pk, .problem = 1, .source = 'ext', quiet = TRUE),
- xpdb_ex_pk$files[xpdb_ex_pk$files$name == 'run001.ext',]$data[[1]])
+ fetched_data <- fetch_data(xpdb_ex_pk, .problem = 1, .source = 'ext', quiet = TRUE)
+
+ expect_equivalent(fetched_data, xpdb_ex_pk$files[xpdb_ex_pk$files$name == 'run001.ext',]$data[[1]])
})
diff --git a/tests/testthat/test-manual_import.R b/tests/testthat/test-manual_import.R
index c4517d96..43141989 100755
--- a/tests/testthat/test-manual_import.R
+++ b/tests/testthat/test-manual_import.R
@@ -27,3 +27,4 @@ test_that('list_nm_tables_manual function works properly', {
test <- dplyr::arrange_at(.tbl = test, .vars = 'table')
expect_identical(test, ctrl_data)
})
+
diff --git a/tests/testthat/test-read_nm_model.R b/tests/testthat/test-read_nm_model.R
index 2b60df7e..5036b230 100755
--- a/tests/testthat/test-read_nm_model.R
+++ b/tests/testthat/test-read_nm_model.R
@@ -28,9 +28,14 @@ test_that('properly parses a model given via the runno and dir arguments', {
test_that('properly handles missing code in output file', {
expect_warning(recover_model <- read_nm_model(runno = '002', ext = '.lst', dir = 'data'),
regexp = 'No model code found in `.lst` NONMEM output file importing `.mod` instead.')
+
+ # Fix file attribute to be comparable
+ attr(recover_model, "file") <- "run001.lst"
+
expect_equal(recover_model, ctrl_mod[-c(1, which(ctrl_mod$level > 30)), ])
})
test_that('problem record is present even if no text has been supplied',{
expect_true('pro' %in% read_nm_model(file = 'sim.lst', dir = 'data')[['subroutine']])
})
+
diff --git a/tests/testthat/test-read_nm_tables.R b/tests/testthat/test-read_nm_tables.R
index 45a6f04e..108c7cbf 100755
--- a/tests/testthat/test-read_nm_tables.R
+++ b/tests/testthat/test-read_nm_tables.R
@@ -17,7 +17,7 @@ ctrl_file <- get_data(xpdb_ex_pk, table = 'patab001') %>%
firstonly_test <- as.nm.table.list(dplyr::tibble(problem = 1,
file = file_path('data', c('sdtab001', 'patab001')),
- firstonly = c(TRUE, FALSE),
+ firstonly = c(FALSE, TRUE),
simtab = FALSE))
minus_sign_test <- c('TABLE NO. 1',
@@ -73,7 +73,7 @@ test_that('tables with firstonly are properly handled', {
skip_on_cran() # Skip to avoid issue with no long double
expect_equal(tmp_table$data[[1]],
- xpdb_ex_pk$data$data[[1]][, unlist(xpdb_ex_pk$data$index[[1]][xpdb_ex_pk$data$index[[1]]$table %in% c('sdtab001', 'patab001'),]$col) ])
+ xpdb_ex_pk$data$data[[1]][, unique(unlist(xpdb_ex_pk$data$index[[1]][xpdb_ex_pk$data$index[[1]]$table %in% c('sdtab001', 'patab001'),]$col)) ])
})
test_that('properly assign skip and header arguments', {
diff --git a/tests/testthat/test-vpc.R b/tests/testthat/test-vpc.R
index fa323035..a92de76f 100755
--- a/tests/testthat/test-vpc.R
+++ b/tests/testthat/test-vpc.R
@@ -10,15 +10,10 @@ load(file = 'data/ctrl_special.RData')
# ctrl_psn_vpc_dat <- psn_vpc_parser(xpdb = xpdb_ex_pk, psn_folder = 'data/psn_vpc/',
# psn_bins = TRUE, opt = vpc_opt(), quiet = TRUE)
-# save(ctrl_psn_vpc_dat, file = 'data/ctrl_psn_vpc.RData',
+# save(ctrl_psn_vpc_dat, file = 'data/ctrl_psn_vpc.RData',
# compress = 'xz', version = 2)
load(file = 'data/ctrl_psn_vpc.RData')
-
-xpdb_vpc_test <- xpdb_ex_pk %>%
- vpc_data(opt = vpc_opt(n_bins = 3, lloq = 0.1), quiet = TRUE) %>%
- vpc_data(vpc_type = 'cens', opt = vpc_opt(n_bins = 3, lloq = 0.4), quiet = TRUE)
-
test_psn_vpc <- vpc_data(xpdb_ex_pk, psn_folder = 'data/psn_vpc/', quiet = TRUE)
# Tests start here --------------------------------------------------------
@@ -40,6 +35,14 @@ test_that('vpc_data properly check input', {
})
test_that('vpc_data works properly with xpdb tables', {
+
+ skip_if(condition = utils::packageVersion("dplyr") > "0.8.5" & utils::packageVersion("vpc") < "1.2.1",
+ message = "Incompatible package versions...")
+
+ xpdb_vpc_test <- xpdb_ex_pk %>%
+ vpc_data(opt = vpc_opt(n_bins = 3, lloq = 0.1), quiet = TRUE) %>%
+ vpc_data(vpc_type = 'cens', opt = vpc_opt(n_bins = 3, lloq = 0.4), quiet = TRUE)
+
expect_true(is.xpdb(xpdb_vpc_test))
expect_identical(xpdb_vpc_test$special, ctrl_special$special)
})
@@ -69,6 +72,10 @@ test_that('vpc plot properly check input', {
})
test_that('vpc plot are properly generated', {
+
+ skip_if(condition = utils::packageVersion("dplyr") > "0.8.5" & utils::packageVersion("vpc") < "1.2.1",
+ message = "Incompatible package versions...")
+
p_cont <- vpc(ctrl_special, vpc_type = 'continuous', type = 'alrpt', quiet = FALSE)
p_cont2 <- vpc(ctrl_special, vpc_type = 'continuous', facets = ~group)
p_cens <- vpc(ctrl_special, vpc_type = 'censored', smooth = FALSE, facets = 'group',
@@ -90,5 +97,5 @@ test_that('vpc plot are properly generated', {
expect_equal(p_cont$xpose$problem, 3)
expect_equal(p_cens$xpose$problem, 4)
expect_equal(p_cont$xpose$summary$value[p_cont$xpose$summary$label %in% c('vpcdir', 'vpcnsim', 'vpcci', 'vpcpi')],
- c('analysis/models/pk/', '20', '95', '95'))
+ c('data', '20', '95', '95'))
})
diff --git a/tests/testthat/test-xpdb_access.R b/tests/testthat/test-xpdb_access.R
index 6e675f96..e7371263 100755
--- a/tests/testthat/test-xpdb_access.R
+++ b/tests/testthat/test-xpdb_access.R
@@ -1,10 +1,5 @@
context('Check xpdb_access functions')
-# Define files to be tested -----------------------------------------------
-xpdb_vpc <- xpdb_ex_pk %>%
- vpc_data(quiet = TRUE) %>%
- vpc_data(vpc_type = 'censored', opt = vpc_opt(lloq = 0.4), quiet = TRUE)
-
# Tests for get_code ------------------------------------------------------
test_that('get_code checks input properly', {
@@ -191,6 +186,14 @@ test_that('get_prm works properly', {
# Tests for get_special ---------------------------------------------------
test_that('get_special checks input properly', {
+
+ skip_if(condition = utils::packageVersion("dplyr") > "0.8.5" & utils::packageVersion("vpc") < "1.2.1",
+ message = "Incompatible package versions...")
+
+ xpdb_vpc <- xpdb_ex_pk %>%
+ vpc_data(quiet = TRUE) %>%
+ vpc_data(vpc_type = 'censored', opt = vpc_opt(lloq = 0.4), quiet = TRUE)
+
# Error with missing xpdb
expect_error(get_special(), regexp = '"xpdb" is missing')
@@ -199,6 +202,14 @@ test_that('get_special checks input properly', {
})
test_that('get_data works properly', {
+
+ skip_if(condition = utils::packageVersion("dplyr") > "0.8.5" & utils::packageVersion("vpc") < "1.2.1",
+ message = "Incompatible package versions...")
+
+ xpdb_vpc <- xpdb_ex_pk %>%
+ vpc_data(quiet = TRUE) %>%
+ vpc_data(vpc_type = 'censored', opt = vpc_opt(lloq = 0.4), quiet = TRUE)
+
# Default return works properly
expect_message(tmp_get_special_1 <- get_special(xpdb_vpc), regexp = 'Returning vpc censored data from \\$prob no\\.4')
expect_equal(tmp_get_special_1, xpdb_vpc$special$data[[2]])
diff --git a/tests/testthat/test-xpose_data.R b/tests/testthat/test-xpose_data.R
index e95ef8e2..86786c48 100755
--- a/tests/testthat/test-xpose_data.R
+++ b/tests/testthat/test-xpose_data.R
@@ -33,9 +33,6 @@ test_that('properly creates the xpdb when using the file argument', {
skip_on_cran() # Skip to avoid issue with no long double
# Correct path and quiet option for identical comparison with xpdb_ex_pk
- xpdb_1$summary$value[xpdb_1$summary$label == 'dir'] <- 'analysis/models/pk/'
- xpdb_1$options$dir <- 'analysis/models/pk/'
- attr(xpdb_1$code, 'dir') <- 'analysis/models/pk/'
xpdb_1$options$quiet <- FALSE
xpdb_1$xp_theme$labeller <- xpdb_ex_pk$xp_theme$labeller
xpdb_1$xp_theme <- as.xpose.theme(xpdb_1$xp_theme)
@@ -51,10 +48,6 @@ test_that('properly creates the xpdb when using the runno argument', {
skip_on_cran() # Skip to avoid issue with no long double
# Correct path and quiet option for identical comparison with xpdb_ex_pk
- xpdb_2$summary$value[xpdb_2$summary$label == 'dir'] <- 'analysis/models/pk/'
- xpdb_2$options$dir <- 'analysis/models/pk/'
- attr(xpdb_2$code, 'dir') <- 'analysis/models/pk/'
- attr(xpdb_2$gg_theme, 'theme') <- 'theme_readable'
xpdb_2$options$quiet <- FALSE
xpdb_2$xp_theme$labeller <- xpdb_ex_pk$xp_theme$labeller
xpdb_2$xp_theme <- as.xpose.theme(xpdb_2$xp_theme)
diff --git a/vignettes/plot_list.Rmd b/vignettes/plot_list.Rmd
index ece47b50..c6976abc 100755
--- a/vignettes/plot_list.Rmd
+++ b/vignettes/plot_list.Rmd
@@ -88,14 +88,14 @@ xpdb_ex_pk %>%
```
### Censored vpc
-```{r}
+```{r, eval = FALSE}
xpdb_ex_pk %>%
vpc_data(vpc_type = 'censored', opt = vpc_opt(lloq = 1, n_bins = 7)) %>%
vpc()
```
### Categorical vpc
-```{r, eval=FALSE}
+```{r, eval = FALSE}
xpdb_ex_pk %>%
vpc_data(vpc_type = 'categorical') %>%
vpc()
diff --git a/vignettes/vpc.Rmd b/vignettes/vpc.Rmd
index 64906b3e..e775b1c1 100755
--- a/vignettes/vpc.Rmd
+++ b/vignettes/vpc.Rmd
@@ -55,7 +55,7 @@ vpc(xpdb_w_vpc) # Plot the vpc from the stored data
```
Multiple VPC data can be stored in an xpdb, but only one of each `vpc_type`.
-```{r, warning = FALSE}
+```{r, eval = FALSE, warning = FALSE}
xpdb_w_multi_vpc <- xpdb_ex_pk %>%
vpc_data(vpc_type = 'continuous', opt = vpc_opt(n_bins = 6, lloq = 0.1)) %>%
vpc_data(vpc_type = 'censored', opt = vpc_opt(n_bins = 6, lloq = 0.1))
@@ -70,7 +70,7 @@ vpc(xpdb_w_multi_vpc, vpc_type = 'censored')
* The `stratify` options defines up to two stratifying variable to be used when computing the VPC data. The `stratify` variables can either be provided as a character vector (`stratify = c('SEX', 'MED1')`) or a formula (`stratify = SEX~MED1`) . The former will result in the use of `ggforce::facet_wrap_paginate()` and the latter of `ggforce::facet_grid_paginate()` when creating the plot. With "categorical" VPC the "group" variable will also be added by default.
* More advanced options (i.e. binning, pi, ci, predcorr, lloq, etc.) are accessible via the `opt` argument. The `opt` argument expects the output from the `vpc_opt()` functions argument.
-```{r, warning = FALSE}
+```{r, eval = FALSE, warning = FALSE}
xpdb_ex_pk %>%
vpc_data(vpc_type = 'censored', stratify = 'SEX',
opt = vpc_opt(bins = 'jenks', n_bins = 7, lloq = 0.5)) %>%
@@ -96,7 +96,7 @@ xpdb_ex_pk %>%
```
### Creating the VPC using a PsN folder
-The `vpc_data()` contains an argument `psn_foler` which can be used to point to a [PsN](https://uupharmacometrics.github.io/PsN/) generated VPC folder. As in most xpose function `template_titles` keywords can be used to automatize the process e.g. `psn_folder = '@dir/@run_vpc'` where `@dir` and `@run` will be automatically translated to initial (i.e. when the xpdb was generated) run directory and run number `'analysis/models/pk/run001_vpc'`.
+The `vpc_data()` contains an argument `psn_folder` which can be used to point to a [PsN](https://uupharmacometrics.github.io/PsN/) generated VPC folder. As in most xpose function `template_titles` keywords can be used to automatize the process e.g. `psn_folder = '@dir/@run_vpc'` where `@dir` and `@run` will be automatically translated to initial (i.e. when the xpdb was generated) run directory and run number `'analysis/models/pk/run001_vpc'`.
In this case, the data will be read from the `/m1` sub-folder (or `m1.zip` if compressed). Note that [PsN](https://uupharmacometrics.github.io/PsN/) drops unused columns to reduce the simtab file size. Thus, in order to allow for more flexibility in R, it is recommended to use multiple stratifying variables (`-stratify_on=VAR1,VAR2`) and the prediction corrected (`-predcorr` adds the PRED column to the output) options in [PsN](https://uupharmacometrics.github.io/PsN/) to avoid having to rerun [PsN](https://uupharmacometrics.github.io/PsN/) to add these variables later on.
In addition, `-dv`, `-idv`, `-lloq`, `-uloq`, `-predcorr` and `-stratify_on` [PsN](https://uupharmacometrics.github.io/PsN/) options are automatically applied to xpose VPC.
Manually import tables and output files from a NLME run to R.
Example of xpose_data
Summarize the xpdb content or the run
Edit data, index or files within an xpdb
Access code, data, files or summary from an xpdb object
Basic goodness-of-fit plots.
Indiviudal observations and model predictions profiles by the independent variable
Visual predictive checks data and plot generation
Covariate, parameters, etas and residuals distribution plots
Covariate, parameters, etas and residuals QQ plots
Plots of compartments’ amount
Graphics to evaluate the estimation step
Functions to manually create customized plots from an xpose_data object.
Add, customize themes or template titles.
Print plots to the console or save them into a file.
- Contents
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# List output tables data
list_data(xpdb_ex_pk)#> Data:
#> problem simtab data modified
-#> <int> <lgl> <list> <lgl>
-#> 1 1 FALSE <tibble [550 × 31]> FALSE
-#> 2 2 TRUE <tibble [11,000 × 12]> FALSE
+#> <int> <lgl> <list> <lgl>
+#> 1 1 FALSE <tibble [550 × 31]> FALSE
+#> 2 2 TRUE <tibble [11,000 × 12]> FALSE
# List output files data
list_files(xpdb_ex_pk)#> Files:
#> name extension problem subprob method data modified
-#> <chr> <chr> <dbl> <dbl> <chr> <list> <lgl>
-#> 1 run001.cor cor 1 0 foce <tibble [14 × 15]> FALSE
-#> 2 run001.cov cov 1 0 foce <tibble [14 × 15]> FALSE
-#> 3 run001.ext ext 1 0 foce <tibble [28 × 16]> FALSE
-#> 4 run001.grd grd 1 0 foce <tibble [21 × 11]> FALSE
-#> 5 run001.phi phi 1 0 foce <tibble [74 × 12]> FALSE
-#> 6 run001.shk shk 1 0 foce <tibble [7 × 5]> FALSE
+#> <chr> <chr> <dbl> <dbl> <chr> <list> <lgl>
+#> 1 run001.cor cor 1 0 foce <tibble [14 × 15]> FALSE
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+#> 6 run001.shk shk 1 0 foce <tibble [7 × 5]> FALSE #> Specials:
#> problem method type data modified
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-#> 1 3 vpc continuous <named list [19]> FALSE
+#> <dbl> <chr> <chr> <list> <lgl>
+#> 1 3 vpc continuous <named list [19]> FALSE
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# S3 method for xpose_data
@@ -182,7 +185,7 @@ Add, remove or rename variables in an xpdb
select(.data, ..., .problem, .source, .where)
# S3 method for xpose_data
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Arguments
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# Rename/select columns
xpdb_ex_pk %>%
select(ID:TAD, DV, EVID) %>%
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print(xpdb_ex_pk)#> run001.lst overview:
#> - Software: nonmem 7.3.0
-#> - Attached files (memory usage 1.3 Mb):
+#> - Attached files (memory usage 1.4 Mb):
#> + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001
#> + sim tabs: $prob no.2: simtab001.zip
#> + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: theme_xp_default
-#> - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL
+#> - Options: dir = data, quiet = FALSE, manual_import = NULL
-
@@ -216,7 +215,7 @@ Contents
diff --git a/docs/reference/xpdb_index_update.html b/docs/reference/xpdb_index_update.html
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processing burden and improve diagnostics commonly associated the development of non-linear mixed effect models.
To learn more about xpose, visit our website: https://UUPharmacometrics.github.io/xpose/." />
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diff --git a/docs/reference/xpose_data.html b/docs/reference/xpose_data.html
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diff --git a/tests/testthat/data/ctrl_psn_vpc.RData b/tests/testthat/data/ctrl_psn_vpc.RData
index 60aef7ab..2f7ca2d3 100755
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diff --git a/tests/testthat/data/ctrl_special.RData b/tests/testthat/data/ctrl_special.RData
index 16f72500..ef478109 100755
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diff --git a/tests/testthat/test-console_outputs.R b/tests/testthat/test-console_outputs.R
index 9f5e540e..96b55210 100755
--- a/tests/testthat/test-console_outputs.R
+++ b/tests/testthat/test-console_outputs.R
@@ -10,11 +10,11 @@ xpdb_modified <- xpdb_ex_pk %>%
mem_use_1 <- format(utils::object.size(xpdb_ex_pk), units = 'auto')
mem_use_2 <- format(utils::object.size(xpdb_modified), units = 'auto')
-print_text <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0 \n - Attached files (memory usage ', mem_use_1, '): \n + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001 \n + sim tabs: $prob no.2: simtab001.zip \n + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk \n + special: \n - gg_theme: theme_readable \n - xp_theme: theme_xp_default \n - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL')
-print_text_modified <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0 \n - Attached files (memory usage ', mem_use_2, '): \n + obs tabs: $prob no.1 (modified): catab001.csv, cotab001, patab001, sdtab001 \n + sim tabs: $prob no.2 (modified): simtab001.zip \n + output files: run001.cor, run001.cov, run001.ext (modified), run001.grd, run001.phi, run001.shk \n + special: vpc continuous (#3, modified) \n - gg_theme: theme_readable \n - xp_theme: theme_xp_default \n - Options: dir = analysis/models/pk/, quiet = FALSE, manual_import = NULL')
+print_text <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0 \n - Attached files (memory usage ', mem_use_1, '): \n + obs tabs: $prob no.1: catab001.csv, cotab001, patab001, sdtab001 \n + sim tabs: $prob no.2: simtab001.zip \n + output files: run001.cor, run001.cov, run001.ext, run001.grd, run001.phi, run001.shk \n + special: \n - gg_theme: theme_readable \n - xp_theme: theme_xp_default \n - Options: dir = data, quiet = FALSE, manual_import = NULL')
+print_text_modified <- paste0('run001.lst overview: \n - Software: nonmem 7.3.0 \n - Attached files (memory usage ', mem_use_2, '): \n + obs tabs: $prob no.1 (modified): catab001.csv, cotab001, patab001, sdtab001 \n + sim tabs: $prob no.2 (modified): simtab001.zip \n + output files: run001.cor, run001.cov, run001.ext (modified), run001.grd, run001.phi, run001.shk \n + special: vpc continuous (#3, modified) \n - gg_theme: theme_readable \n - xp_theme: theme_xp_default \n - Options: dir = data, quiet = FALSE, manual_import = NULL')
prm_text_1 <- '\nReporting transformed parameters:\nFor the OMEGA and SIGMA matrices, values are reported as standard deviations for the diagonal elements and as correlations for the off-diagonal elements. The relative standard errors (RSE) for OMEGA and SIGMA are reported on the approximate standard deviation scale (SE/variance estimate)/2. Use `transform = FALSE` to report untransformed parameters.\n\nEstimates for $prob no.1, subprob no.0, method foce\n Parameter Label Value RSE\n THETA1 TVCL 26.29 0.03391\n THETA2 TVV 1.348 0.0325\n THETA3 TVKA 4.204 0.1925\n THETA4 LAG 0.208 0.07554\n THETA5 Prop. Err 0.2046 0.1097\n THETA6 Add. Err 0.01055 0.3466\n THETA7 CRCL on CL 0.007172 0.2366\n OMEGA(1,1) IIV CL 0.2701 0.08616\n OMEGA(2,2) IIV V 0.195 0.1643\n OMEGA(3,3) IIV KA 1.381 0.1463\n SIGMA(1,1) 1 fix - '
prm_text_2 <- '\nReporting untransformed parameters:\nFor the OMEGA and SIGMA matrices, values are reported as variances for the diagonal elements and as covariances for the off-diagonal elements.\n\nEstimates for $prob no.1, subprob no.0, method foce\n Parameter Label Value SE\n THETA1 TVCL 26.29 0.8915\n THETA2 TVV 1.348 0.04381\n THETA3 TVKA 4.204 0.8091\n THETA4 LAG 0.208 0.01571\n THETA5 Prop. Err 0.2046 0.02244\n THETA6 Add. Err 0.01055 0.003658\n THETA7 CRCL on CL 0.007172 0.001697\n OMEGA(1,1) IIV CL 0.07295 0.01257\n OMEGA(2,2) IIV V 0.03802 0.0125\n OMEGA(3,3) IIV KA 1.907 0.5582\n SIGMA(1,1) 1 fix - '
-summary_text <- '\nSummary for problem no. 0 [Global information] \n - Software @software : nonmem\n - Software version @version : 7.3.0\n - Run directory @dir : analysis/models/pk/\n - Run file @file : run001.lst\n - Run number @run : run001\n - Reference model @ref : 000\n - Run description @descr : NONMEM PK example for xpose\n - Run start time @timestart : Mon Oct 16 13:34:28 CEST 2017\n - Run stop time @timestop : Mon Oct 16 13:34:35 CEST 2017\n\nSummary for problem no. 1 [Parameter estimation] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - ADVAN @subroutine : 2\n - Estimation method @method : foce-i\n - Termination message @term : MINIMIZATION SUCCESSFUL\n - Estimation runtime @runtime : 00:00:02\n - Objective function value @ofv : -1403.905\n - Number of significant digits @nsig : 3.3\n - Covariance step runtime @covtime : 00:00:03\n - Condition number @condn : 21.5\n - Eta shrinkage @etashk : 9.3 [1], 28.7 [2], 23.7 [3]\n - Epsilon shrinkage @epsshk : 14.9 [1]\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n\nSummary for problem no. 2 [Model simulations] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - Estimation method @method : sim\n - Number of simulations @nsim : 20\n - Simulation seed @simseed : 221287\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n (WARNING 22) WITH $MSFI AND \"SUBPROBS\", \"TRUE=FINAL\" ...'
+summary_text <- '\nSummary for problem no. 0 [Global information] \n - Software @software : nonmem\n - Software version @version : 7.3.0\n - Run directory @dir : data\n - Run file @file : run001.lst\n - Run number @run : run001\n - Reference model @ref : 000\n - Run description @descr : NONMEM PK example for xpose\n - Run start time @timestart : Mon Oct 16 13:34:28 CEST 2017\n - Run stop time @timestop : Mon Oct 16 13:34:35 CEST 2017\n\nSummary for problem no. 1 [Parameter estimation] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - ADVAN @subroutine : 2\n - Estimation method @method : foce-i\n - Termination message @term : MINIMIZATION SUCCESSFUL\n - Estimation runtime @runtime : 00:00:02\n - Objective function value @ofv : -1403.905\n - Number of significant digits @nsig : 3.3\n - Covariance step runtime @covtime : 00:00:03\n - Condition number @condn : 21.5\n - Eta shrinkage @etashk : 9.3 [1], 28.7 [2], 23.7 [3]\n - Epsilon shrinkage @epsshk : 14.9 [1]\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n\nSummary for problem no. 2 [Model simulations] \n - Input data @data : ../../mx19_2.csv\n - Number of individuals @nind : 74\n - Number of observations @nobs : 476\n - Estimation method @method : sim\n - Number of simulations @nsim : 20\n - Simulation seed @simseed : 221287\n - Run warnings @warnings : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.\n (WARNING 22) WITH $MSFI AND \"SUBPROBS\", \"TRUE=FINAL\" ...'
vars_text <- '\nList of available variables for problem no. 1 \n - Subject identifier (id) : ID\n - Dependent variable (dv) : DV\n - Independent variable (idv) : TIME\n - Dose amount (amt) : AMT\n - Event identifier (evid) : EVID\n - Model typical predictions (pred) : PRED\n - Model individual predictions (ipred) : IPRED\n - Model parameter (param) : KA, CL, V, ALAG1\n - Eta (eta) : ETA1, ETA2, ETA3\n - Residuals (res) : CWRES, IWRES, RES, WRES\n - Categorical covariates (catcov) : SEX, MED1, MED2\n - Continuous covariates (contcov) : CLCR, AGE, WT\n - Compartment amounts (a) : A1, A2\n - Not attributed (na) : DOSE, SS, II, TAD, CPRED'
# Tests start here --------------------------------------------------------
diff --git a/tests/testthat/test-edits.R b/tests/testthat/test-edits.R
index 7fcbc4cc..0af46f74 100755
--- a/tests/testthat/test-edits.R
+++ b/tests/testthat/test-edits.R
@@ -12,7 +12,8 @@ ctrl_tab_2 <- xpdb_ex_pk %>%
group_by(ID, SEX, .problem = 1) %>%
summarize(CMAX = max(DV), .problem = 1) %>%
ungroup(.problem = 1) %>%
- get_data(.problem = 1)
+ get_data(.problem = 1) %>%
+ ungroup()
test_xpdb_1 <- vpc_data(xpdb_ex_pk, opt = vpc_opt(n_bins = 2), quiet = TRUE)
ctrl_xpdb_1 <- test_xpdb_1
@@ -32,14 +33,15 @@ test_that('xpdb_edits checks error properly', {
})
test_that('xpdb_edits works properly', {
- expect_equal(filter.xpose_data(.data = xpdb_ex_pk, ID == 110, TIME > 10, .problem = 1) %>% get_data(.problem = 1),
+ expect_equal(filter.xpose_data(.data = xpdb_ex_pk, ID == 110, TIME > 10, .problem = 1) %>%
+ get_data(.problem = 1),
xpdb_ex_pk %>% get_data(.problem = 1) %>% filter(.$ID == 110, .$TIME > 10))
expect_equal(filter.xpose_data(.data = xpdb_ex_pk, ID == 110, .problem = 1, .source = 'phi') %>%
slice.xpose_data(1:3, .source = 'phi') %>%
mutate.xpose_data(TEST = 'Ok', .source = 'phi') %>%
get_file(ext = 'phi', quiet = TRUE),
- xpdb_ex_pk %>% get_file(ext = 'phi', quiet = TRUE) %>% filter(.$ID == 110)
- %>% slice(1:3) %>% mutate(TEST = 'Ok'))
+ xpdb_ex_pk %>% get_file(ext = 'phi', quiet = TRUE) %>% filter(.$ID == 110) %>%
+ slice(1:3) %>% mutate(TEST = 'Ok'))
expect_equal(ctrl_tab_1, xpdb_ex_pk %>%
get_data(.problem = 1) %>%
mutate(DV = log(DV)) %>%
@@ -81,3 +83,4 @@ test_that('n() works', {
expect_equal(xpdb_ex_pk %>% mutate(N = 1:n(), .source = 'ext') %>% get_file(ext = 'ext', quiet = TRUE),
xpdb_ex_pk %>% get_file(ext = 'ext', quiet = TRUE) %>% mutate(N = 1:n()))
})
+
diff --git a/tests/testthat/test-fetch_data.R b/tests/testthat/test-fetch_data.R
index 2ef13780..dedd196f 100755
--- a/tests/testthat/test-fetch_data.R
+++ b/tests/testthat/test-fetch_data.R
@@ -34,9 +34,9 @@ test_that('fetch_data can get simple data', {
expect_equal(attr(imported_data, 'problem'), 1)
expect_equal(attr(imported_data, 'simtab'), FALSE)
expect_equal(attr(imported_data, 'source'), 'data')
-
+
# Check output
- expect_equal(imported_data, xpdb_ex_pk$data$data[[1]][xpdb_ex_pk$data$data[[1]]$EVID == 0, ])
+ expect_equivalent(imported_data, xpdb_ex_pk$data$data[[1]][xpdb_ex_pk$data$data[[1]]$EVID == 0, ])
})
test_that('fetch_data can tidy data', {
@@ -46,7 +46,7 @@ test_that('fetch_data can tidy data', {
tidy = TRUE, value_col = c('IPRED','PRED', 'DV'))
# Check output
- expect_equal(imported_tidy_data,
+ expect_equivalent(imported_tidy_data,
xpdb_ex_pk$data$data[[1]][xpdb_ex_pk$data$data[[1]]$EVID == 0,
c('ID', 'TIME', 'IPRED', 'PRED', 'DV')] %>%
tidyr::gather(value = 'value', key = 'variable',
@@ -55,6 +55,7 @@ test_that('fetch_data can tidy data', {
})
test_that('fetch_data can get file data', {
- expect_equal(fetch_data(xpdb_ex_pk, .problem = 1, .source = 'ext', quiet = TRUE),
- xpdb_ex_pk$files[xpdb_ex_pk$files$name == 'run001.ext',]$data[[1]])
+ fetched_data <- fetch_data(xpdb_ex_pk, .problem = 1, .source = 'ext', quiet = TRUE)
+
+ expect_equivalent(fetched_data, xpdb_ex_pk$files[xpdb_ex_pk$files$name == 'run001.ext',]$data[[1]])
})
diff --git a/tests/testthat/test-manual_import.R b/tests/testthat/test-manual_import.R
index c4517d96..43141989 100755
--- a/tests/testthat/test-manual_import.R
+++ b/tests/testthat/test-manual_import.R
@@ -27,3 +27,4 @@ test_that('list_nm_tables_manual function works properly', {
test <- dplyr::arrange_at(.tbl = test, .vars = 'table')
expect_identical(test, ctrl_data)
})
+
diff --git a/tests/testthat/test-read_nm_model.R b/tests/testthat/test-read_nm_model.R
index 2b60df7e..5036b230 100755
--- a/tests/testthat/test-read_nm_model.R
+++ b/tests/testthat/test-read_nm_model.R
@@ -28,9 +28,14 @@ test_that('properly parses a model given via the runno and dir arguments', {
test_that('properly handles missing code in output file', {
expect_warning(recover_model <- read_nm_model(runno = '002', ext = '.lst', dir = 'data'),
regexp = 'No model code found in `.lst` NONMEM output file importing `.mod` instead.')
+
+ # Fix file attribute to be comparable
+ attr(recover_model, "file") <- "run001.lst"
+
expect_equal(recover_model, ctrl_mod[-c(1, which(ctrl_mod$level > 30)), ])
})
test_that('problem record is present even if no text has been supplied',{
expect_true('pro' %in% read_nm_model(file = 'sim.lst', dir = 'data')[['subroutine']])
})
+
diff --git a/tests/testthat/test-read_nm_tables.R b/tests/testthat/test-read_nm_tables.R
index 45a6f04e..108c7cbf 100755
--- a/tests/testthat/test-read_nm_tables.R
+++ b/tests/testthat/test-read_nm_tables.R
@@ -17,7 +17,7 @@ ctrl_file <- get_data(xpdb_ex_pk, table = 'patab001') %>%
firstonly_test <- as.nm.table.list(dplyr::tibble(problem = 1,
file = file_path('data', c('sdtab001', 'patab001')),
- firstonly = c(TRUE, FALSE),
+ firstonly = c(FALSE, TRUE),
simtab = FALSE))
minus_sign_test <- c('TABLE NO. 1',
@@ -73,7 +73,7 @@ test_that('tables with firstonly are properly handled', {
skip_on_cran() # Skip to avoid issue with no long double
expect_equal(tmp_table$data[[1]],
- xpdb_ex_pk$data$data[[1]][, unlist(xpdb_ex_pk$data$index[[1]][xpdb_ex_pk$data$index[[1]]$table %in% c('sdtab001', 'patab001'),]$col) ])
+ xpdb_ex_pk$data$data[[1]][, unique(unlist(xpdb_ex_pk$data$index[[1]][xpdb_ex_pk$data$index[[1]]$table %in% c('sdtab001', 'patab001'),]$col)) ])
})
test_that('properly assign skip and header arguments', {
diff --git a/tests/testthat/test-vpc.R b/tests/testthat/test-vpc.R
index fa323035..a92de76f 100755
--- a/tests/testthat/test-vpc.R
+++ b/tests/testthat/test-vpc.R
@@ -10,15 +10,10 @@ load(file = 'data/ctrl_special.RData')
# ctrl_psn_vpc_dat <- psn_vpc_parser(xpdb = xpdb_ex_pk, psn_folder = 'data/psn_vpc/',
# psn_bins = TRUE, opt = vpc_opt(), quiet = TRUE)
-# save(ctrl_psn_vpc_dat, file = 'data/ctrl_psn_vpc.RData',
+# save(ctrl_psn_vpc_dat, file = 'data/ctrl_psn_vpc.RData',
# compress = 'xz', version = 2)
load(file = 'data/ctrl_psn_vpc.RData')
-
-xpdb_vpc_test <- xpdb_ex_pk %>%
- vpc_data(opt = vpc_opt(n_bins = 3, lloq = 0.1), quiet = TRUE) %>%
- vpc_data(vpc_type = 'cens', opt = vpc_opt(n_bins = 3, lloq = 0.4), quiet = TRUE)
-
test_psn_vpc <- vpc_data(xpdb_ex_pk, psn_folder = 'data/psn_vpc/', quiet = TRUE)
# Tests start here --------------------------------------------------------
@@ -40,6 +35,14 @@ test_that('vpc_data properly check input', {
})
test_that('vpc_data works properly with xpdb tables', {
+
+ skip_if(condition = utils::packageVersion("dplyr") > "0.8.5" & utils::packageVersion("vpc") < "1.2.1",
+ message = "Incompatible package versions...")
+
+ xpdb_vpc_test <- xpdb_ex_pk %>%
+ vpc_data(opt = vpc_opt(n_bins = 3, lloq = 0.1), quiet = TRUE) %>%
+ vpc_data(vpc_type = 'cens', opt = vpc_opt(n_bins = 3, lloq = 0.4), quiet = TRUE)
+
expect_true(is.xpdb(xpdb_vpc_test))
expect_identical(xpdb_vpc_test$special, ctrl_special$special)
})
@@ -69,6 +72,10 @@ test_that('vpc plot properly check input', {
})
test_that('vpc plot are properly generated', {
+
+ skip_if(condition = utils::packageVersion("dplyr") > "0.8.5" & utils::packageVersion("vpc") < "1.2.1",
+ message = "Incompatible package versions...")
+
p_cont <- vpc(ctrl_special, vpc_type = 'continuous', type = 'alrpt', quiet = FALSE)
p_cont2 <- vpc(ctrl_special, vpc_type = 'continuous', facets = ~group)
p_cens <- vpc(ctrl_special, vpc_type = 'censored', smooth = FALSE, facets = 'group',
@@ -90,5 +97,5 @@ test_that('vpc plot are properly generated', {
expect_equal(p_cont$xpose$problem, 3)
expect_equal(p_cens$xpose$problem, 4)
expect_equal(p_cont$xpose$summary$value[p_cont$xpose$summary$label %in% c('vpcdir', 'vpcnsim', 'vpcci', 'vpcpi')],
- c('analysis/models/pk/', '20', '95', '95'))
+ c('data', '20', '95', '95'))
})
diff --git a/tests/testthat/test-xpdb_access.R b/tests/testthat/test-xpdb_access.R
index 6e675f96..e7371263 100755
--- a/tests/testthat/test-xpdb_access.R
+++ b/tests/testthat/test-xpdb_access.R
@@ -1,10 +1,5 @@
context('Check xpdb_access functions')
-# Define files to be tested -----------------------------------------------
-xpdb_vpc <- xpdb_ex_pk %>%
- vpc_data(quiet = TRUE) %>%
- vpc_data(vpc_type = 'censored', opt = vpc_opt(lloq = 0.4), quiet = TRUE)
-
# Tests for get_code ------------------------------------------------------
test_that('get_code checks input properly', {
@@ -191,6 +186,14 @@ test_that('get_prm works properly', {
# Tests for get_special ---------------------------------------------------
test_that('get_special checks input properly', {
+
+ skip_if(condition = utils::packageVersion("dplyr") > "0.8.5" & utils::packageVersion("vpc") < "1.2.1",
+ message = "Incompatible package versions...")
+
+ xpdb_vpc <- xpdb_ex_pk %>%
+ vpc_data(quiet = TRUE) %>%
+ vpc_data(vpc_type = 'censored', opt = vpc_opt(lloq = 0.4), quiet = TRUE)
+
# Error with missing xpdb
expect_error(get_special(), regexp = '"xpdb" is missing')
@@ -199,6 +202,14 @@ test_that('get_special checks input properly', {
})
test_that('get_data works properly', {
+
+ skip_if(condition = utils::packageVersion("dplyr") > "0.8.5" & utils::packageVersion("vpc") < "1.2.1",
+ message = "Incompatible package versions...")
+
+ xpdb_vpc <- xpdb_ex_pk %>%
+ vpc_data(quiet = TRUE) %>%
+ vpc_data(vpc_type = 'censored', opt = vpc_opt(lloq = 0.4), quiet = TRUE)
+
# Default return works properly
expect_message(tmp_get_special_1 <- get_special(xpdb_vpc), regexp = 'Returning vpc censored data from \\$prob no\\.4')
expect_equal(tmp_get_special_1, xpdb_vpc$special$data[[2]])
diff --git a/tests/testthat/test-xpose_data.R b/tests/testthat/test-xpose_data.R
index e95ef8e2..86786c48 100755
--- a/tests/testthat/test-xpose_data.R
+++ b/tests/testthat/test-xpose_data.R
@@ -33,9 +33,6 @@ test_that('properly creates the xpdb when using the file argument', {
skip_on_cran() # Skip to avoid issue with no long double
# Correct path and quiet option for identical comparison with xpdb_ex_pk
- xpdb_1$summary$value[xpdb_1$summary$label == 'dir'] <- 'analysis/models/pk/'
- xpdb_1$options$dir <- 'analysis/models/pk/'
- attr(xpdb_1$code, 'dir') <- 'analysis/models/pk/'
xpdb_1$options$quiet <- FALSE
xpdb_1$xp_theme$labeller <- xpdb_ex_pk$xp_theme$labeller
xpdb_1$xp_theme <- as.xpose.theme(xpdb_1$xp_theme)
@@ -51,10 +48,6 @@ test_that('properly creates the xpdb when using the runno argument', {
skip_on_cran() # Skip to avoid issue with no long double
# Correct path and quiet option for identical comparison with xpdb_ex_pk
- xpdb_2$summary$value[xpdb_2$summary$label == 'dir'] <- 'analysis/models/pk/'
- xpdb_2$options$dir <- 'analysis/models/pk/'
- attr(xpdb_2$code, 'dir') <- 'analysis/models/pk/'
- attr(xpdb_2$gg_theme, 'theme') <- 'theme_readable'
xpdb_2$options$quiet <- FALSE
xpdb_2$xp_theme$labeller <- xpdb_ex_pk$xp_theme$labeller
xpdb_2$xp_theme <- as.xpose.theme(xpdb_2$xp_theme)
diff --git a/vignettes/plot_list.Rmd b/vignettes/plot_list.Rmd
index ece47b50..c6976abc 100755
--- a/vignettes/plot_list.Rmd
+++ b/vignettes/plot_list.Rmd
@@ -88,14 +88,14 @@ xpdb_ex_pk %>%
```
### Censored vpc
-```{r}
+```{r, eval = FALSE}
xpdb_ex_pk %>%
vpc_data(vpc_type = 'censored', opt = vpc_opt(lloq = 1, n_bins = 7)) %>%
vpc()
```
### Categorical vpc
-```{r, eval=FALSE}
+```{r, eval = FALSE}
xpdb_ex_pk %>%
vpc_data(vpc_type = 'categorical') %>%
vpc()
diff --git a/vignettes/vpc.Rmd b/vignettes/vpc.Rmd
index 64906b3e..e775b1c1 100755
--- a/vignettes/vpc.Rmd
+++ b/vignettes/vpc.Rmd
@@ -55,7 +55,7 @@ vpc(xpdb_w_vpc) # Plot the vpc from the stored data
```
Multiple VPC data can be stored in an xpdb, but only one of each `vpc_type`.
-```{r, warning = FALSE}
+```{r, eval = FALSE, warning = FALSE}
xpdb_w_multi_vpc <- xpdb_ex_pk %>%
vpc_data(vpc_type = 'continuous', opt = vpc_opt(n_bins = 6, lloq = 0.1)) %>%
vpc_data(vpc_type = 'censored', opt = vpc_opt(n_bins = 6, lloq = 0.1))
@@ -70,7 +70,7 @@ vpc(xpdb_w_multi_vpc, vpc_type = 'censored')
* The `stratify` options defines up to two stratifying variable to be used when computing the VPC data. The `stratify` variables can either be provided as a character vector (`stratify = c('SEX', 'MED1')`) or a formula (`stratify = SEX~MED1`) . The former will result in the use of `ggforce::facet_wrap_paginate()` and the latter of `ggforce::facet_grid_paginate()` when creating the plot. With "categorical" VPC the "group" variable will also be added by default.
* More advanced options (i.e. binning, pi, ci, predcorr, lloq, etc.) are accessible via the `opt` argument. The `opt` argument expects the output from the `vpc_opt()` functions argument.
-```{r, warning = FALSE}
+```{r, eval = FALSE, warning = FALSE}
xpdb_ex_pk %>%
vpc_data(vpc_type = 'censored', stratify = 'SEX',
opt = vpc_opt(bins = 'jenks', n_bins = 7, lloq = 0.5)) %>%
@@ -96,7 +96,7 @@ xpdb_ex_pk %>%
```
### Creating the VPC using a PsN folder
-The `vpc_data()` contains an argument `psn_foler` which can be used to point to a [PsN](https://uupharmacometrics.github.io/PsN/) generated VPC folder. As in most xpose function `template_titles` keywords can be used to automatize the process e.g. `psn_folder = '@dir/@run_vpc'` where `@dir` and `@run` will be automatically translated to initial (i.e. when the xpdb was generated) run directory and run number `'analysis/models/pk/run001_vpc'`.
+The `vpc_data()` contains an argument `psn_folder` which can be used to point to a [PsN](https://uupharmacometrics.github.io/PsN/) generated VPC folder. As in most xpose function `template_titles` keywords can be used to automatize the process e.g. `psn_folder = '@dir/@run_vpc'` where `@dir` and `@run` will be automatically translated to initial (i.e. when the xpdb was generated) run directory and run number `'analysis/models/pk/run001_vpc'`.
In this case, the data will be read from the `/m1` sub-folder (or `m1.zip` if compressed). Note that [PsN](https://uupharmacometrics.github.io/PsN/) drops unused columns to reduce the simtab file size. Thus, in order to allow for more flexibility in R, it is recommended to use multiple stratifying variables (`-stratify_on=VAR1,VAR2`) and the prediction corrected (`-predcorr` adds the PRED column to the output) options in [PsN](https://uupharmacometrics.github.io/PsN/) to avoid having to rerun [PsN](https://uupharmacometrics.github.io/PsN/) to add these variables later on.
In addition, `-dv`, `-idv`, `-lloq`, `-uloq`, `-predcorr` and `-stratify_on` [PsN](https://uupharmacometrics.github.io/PsN/) options are automatically applied to xpose VPC.
Examp mutate(sim_id = irep(ID), .problem = 2)
Examp #> - Model individual predictions (ipred) : IPRED #> - Not attributed (na) : DOSE, TAD, SEX, CLCR, AGE, WT
Examp
# List output tables data
list_data(xpdb_ex_pk)#> Data:
#> problem simtab data modified
-#> <int> <lgl> <list> <lgl>
-#> 1 1 FALSE <tibble [550 × 31]> FALSE
-#> 2 2 TRUE <tibble [11,000 × 12]> FALSE
+#> <int> <lgl> <list> <lgl>
+#> 1 1 FALSE <tibble [550 × 31]> FALSE
+#> 2 2 TRUE <tibble [11,000 × 12]> FALSE
# List output files data
list_files(xpdb_ex_pk)#> Files:
#> name extension problem subprob method data modified
-#> <chr> <chr> <dbl> <dbl> <chr> <list> <lgl>
-#> 1 run001.cor cor 1 0 foce <tibble [14 × 15]> FALSE
-#> 2 run001.cov cov 1 0 foce <tibble [14 × 15]> FALSE
-#> 3 run001.ext ext 1 0 foce <tibble [28 × 16]> FALSE
-#> 4 run001.grd grd 1 0 foce <tibble [21 × 11]> FALSE
-#> 5 run001.phi phi 1 0 foce <tibble [74 × 12]> FALSE
-#> 6 run001.shk shk 1 0 foce <tibble [7 × 5]> FALSE
+#> <chr> <chr> <dbl> <dbl> <chr> <list> <lgl>
+#> 1 run001.cor cor 1 0 foce <tibble [14 × 15]> FALSE
+#> 2 run001.cov cov 1 0 foce <tibble [14 × 15]> FALSE
+#> 3 run001.ext ext 1 0 foce <tibble [28 × 16]> FALSE
+#> 4 run001.grd grd 1 0 foce <tibble [21 × 11]> FALSE
+#> 5 run001.phi phi 1 0 foce <tibble [74 × 12]> FALSE
+#> 6 run001.shk shk 1 0 foce <tibble [7 × 5]> FALSE #> Specials:
#> problem method type data modified
-#> <dbl> <chr> <chr> <list> <lgl>
-#> 1 3 vpc continuous <named list [19]> FALSE
+#> <dbl> <chr> <chr> <list> <lgl>
+#> 1 3 vpc continuous <named list [19]> FALSE
# List output tables data
list_data(xpdb_ex_pk)
#> Data:
#> problem simtab data modified
-#> <int> <lgl> <list> <lgl>
-#> 1 1 FALSE <tibble [550 × 31]> FALSE
-#> 2 2 TRUE <tibble [11,000 × 12]> FALSE
+#> <int> <lgl> <list> <lgl>
+#> 1 1 FALSE <tibble [550 × 31]> FALSE
+#> 2 2 TRUE <tibble [11,000 × 12]> FALSE
# List output files data
list_files(xpdb_ex_pk)
#> Files:
#> name extension problem subprob method data modified
-#> <chr> <chr> <dbl> <dbl> <chr> <list> <lgl>
-#> 1 run001.cor cor 1 0 foce <tibble [14 × 15]> FALSE
-#> 2 run001.cov cov 1 0 foce <tibble [14 × 15]> FALSE
-#> 3 run001.ext ext 1 0 foce <tibble [28 × 16]> FALSE
-#> 4 run001.grd grd 1 0 foce <tibble [21 × 11]> FALSE
-#> 5 run001.phi phi 1 0 foce <tibble [74 × 12]> FALSE
-#> 6 run001.shk shk 1 0 foce <tibble [7 × 5]> FALSE
+#> <chr> <chr> <dbl> <dbl> <chr> <list> <lgl>
+#> 1 run001.cor cor 1 0 foce <tibble [14 × 15]> FALSE
+#> 2 run001.cov cov 1 0 foce <tibble [14 × 15]> FALSE
+#> 3 run001.ext ext 1 0 foce <tibble [28 × 16]> FALSE
+#> 4 run001.grd grd 1 0 foce <tibble [21 × 11]> FALSE
+#> 5 run001.phi phi 1 0 foce <tibble [74 × 12]> FALSE
+#> 6 run001.shk shk 1 0 foce <tibble [7 × 5]> FALSE
#> Specials:
#> problem method type data modified
-#> <dbl> <chr> <chr> <list> <lgl>
-#> 1 3 vpc continuous <named list [19]> FALSE
+#> <dbl> <chr> <chr> <list> <lgl>
+#> 1 3 vpc continuous <named list [19]> FALSE
Examp xpose_data(runno = '001', manual_import = manual_nm_import(tab_names = 'mytab')) }
Add, remove or rename variables in an xpdb
mutate() adds new variables and preserves existing ones.
-select() keeps only the listed variables; rename() keeps all variables.
select() keeps only the listed variables; rename() keeps all variables.
# S3 method for xpose_data @@ -182,7 +185,7 @@+rename(.data, ..., .problem, .source, .where)Add, remove or rename variables in an xpdb
select(.data, ..., .problem, .source, .where) # S3 method for xpose_data -rename(.data, ..., .problem, .source, .where)
