Merge pull request #326 from billdenney/documentation_changes

Documentation changes

README.md

@@ -18,14 +18,12 @@ The primary and secondary goals of the PKNCA package are to 1) only
 give correct answers to the specific questions being asked and 2)
 automate as much as possible to simplify the task of the analyst. When
 automation would leave ambiguity or make a choice that the analyst may
-have an alternate preference for, it is either not used or is possible
-to override.
+have an alternate preference for, it is either not used, is possible
+to override or raises an error message.
 
 Note that backward compatibility will not be guaranteed until version
 1.0.  Argument and function changes will continue until then.  These
-will be especially noticeable around the inclusion of IV NCA parameters
-and additional specifications of the dosing including dose amount and
-route.
+will be especially noticeable around the inclusion of IV NCA parameters and additional specifications of the drug, which include dose amount and route of administration.
 
 # Citation
 
@@ -78,9 +76,7 @@ To install the development version from GitHub, type the following commands:
     # Summarize the results
     summary(o_results)
 
-More help is available in the function help files, and be sure to look
-at the PKNCA.options function for many choices to make PKNCA conform
-to your company's business rules for calculations and summarization.
+More help is available in the function help files. Be sure to look at the PKNCA.options function for choices on making PKNCA conform to your company’s business rules on calculation and summarization.
 
 # Feature requests
 

vignettes/v01-introduction-and-usage.Rmd

@@ -16,12 +16,12 @@ library(PKNCA)
 ```
 
 PKNCA provides functions to complete noncompartmental analysis (NCA)
-for pharmacokinetic (PK) data.  Its intent is to provide a complete
-R-based solution enabling data provenance for NCA including tracking
-data cleaning, enabling calculations, exporting results, and
-reporting.  The library is designed to give a reasonable answer
-without user intervention (load, calculate, and summarize), but it
-allows the user to override the automatic selections at any point.
+for pharmacokinetic (PK) data.  Its intent is to provide a complete 
+R-based solution-enabling data provenance for NCA. This will include 
+the tracking of data cleaning, enabling of calculations, exporting
+of results, and general reporting.  The library is designed to give
+a reasonable answer without user intervention (load, calculate, and summarize),
+but it allows the user to override the automatic selections at any point.
 
 The library design is modular to allow expansion based on needs
 unforseen by the authors including new NCA parameters, novel data
@@ -30,8 +30,7 @@ library will be discussed in a separate vignette.
 
 # Quick Start
 
-The simplest analysis requires concentration and dosing data, then
-five function calls can provide summarized results.  (Please note that this and the other examples in this document are intended to show the typical workflow, but they are not intended to run directly.  For an example to run directly, please see [the theophylline example](v02-example-theophylline.html).)
+The simplest analysis requires concentration and dosing data at a minimum. Given this, it then takes five function calls to provide summarized results.  (Please note that this and the other examples in this document are intended to show the typical workflow, but they are not intended to run directly.  For an example to run directly, please see [the theophylline example](v02-example-theophylline.html).)
 
 ```{r setup_data}
 library(PKNCA)
@@ -263,10 +262,11 @@ intervals <-
   )
 ```
 
-Intervals like the above is sufficient for designs with a single type of
-treatment-- like single doses.  For more complex treatments in a single
-analysis, like the combination of single and multiple doses, include a treatment
-column matching the treatment column name from the concentration data set.  See
+Intervals like the one above are sufficient for designs with a
+single type of treatment– such as single doses.  For more
+complex treatments in a single analysis, like the combination of
+single and multiple doses, include a treatment column matching
+the treatment column name from the concentration data set.  See
 the [Manual Interval Specification](#manual-interval-specification) section
 below for more details.
 
@@ -285,7 +285,7 @@ for the half-life or AUC~inf~ calculations.
 A few functions look at data outside of a single interval, but these functions
 do not look at data outside of a single group, and these functions are typically
 used during preparation for NCA calculations not for the calculations
-themselves.  Functions that look a group as a whole include
+themselves.  Functions that look at a group as a whole include
 `choose.auc.intervals`, `find.tau`, and `pk.tss`.
 
 ## Automatic Interval Determination

vignettes/v05-auc-calculation-with-PKNCA.Rmd

@@ -67,7 +67,7 @@ kable(as.data.frame(results_obj))
 
 # AUC to the Last Value Above the Limit of Quantification (AUC~last~)
 
-AUC~0-last~ calculates the AUC from time 0 to the last value above the limit of quantification, `tlast` (within PKNCA, this is the last value above 0).  In the figure below, AUC~0-last~ integrate the shaded region.  Integration after `tlast` is 0.
+AUC~0-last~ calculates the AUC from time 0 to the last value above the limit of quantification, `tlast` (within PKNCA, this is the last value above 0).  In the figure below, AUC~0-last~ integrates the shaded region.  Integration after `tlast` is 0.
 
 ```{r auclast}
 tlast <- pk.calc.tlast(conc=my_conc$conc,
@@ -120,7 +120,7 @@ ggplot(my_conc,
 
 AUC~0-$\infty$~ is commonly used for single-dose data.  It calculates the AUC~0-last~ and then extrapolates to $\infty$ using the estimated half-life.  Two starting points are used to estimate from `tlast` to $\infty$, the observed or half-life predicted concentration at `tlast` (`clast.obs` and `clast.pred`).
 
-The two figures below illustrate the integration with AUC~0-$\infty$,obs~ and AUC~0-$\infty$,pred$.  The difference between the tow figures is most evident at time=8 where there is a discontinuity in integration at `tlast` due to using `clast.pred` after that point and `clast.obs` before that point.  (To illustrate the integration differences, BLQ indicator shapes have been removed.  BLQ is handled identically to previous figures.)
+The two figures below illustrate the integration with AUC~0-$\infty$,obs~ and AUC~0-$\infty$,pred$.  The difference between the two figures is most evident at time=8 where there is a discontinuity in integration at `tlast` due to using `clast.pred` after that point and `clast.obs` before that point.  (To illustrate the integration differences, BLQ indicator shapes have been removed.  BLQ is handled identically to previous figures.)
 
 ```{r aucinf, fig.width=6}
 # Add one row to illustrate extrapolation beyond observed data