README.Rmd

---
output:
  github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE, message=FALSE}
knitr::opts_chunk$set(collapse = TRUE,
                      comment = "#>",
                      fig.path   = 'man/figures/',
                      fig.align  = 'center',
                      out.width  = '80%', 
                      dev        = 'png',
                      dev.args   = list(type = 'cairo'))

library(tidyverse)
library(pmxploit)
theme_set(theme_minimal())

run <- pmxploit::EXAMPLERUN
```

# pmxploit <img id="logo" src="pkgdown/pmxploit.svg" alt="pmxploit" width="125px" align="right" />

[![CRAN status](https://www.r-pkg.org/badges/version/pmxploit)](https://cran.r-project.org/package=pmxploit)

The goal of **pmxploit** is to facilitate the post-processing analysis of non-linear mixed effects models developed with NONMEM software.

It provides helper functions for visualizing and summarizing population analyses results, detecting outliers, computing numerical quality criteria and comparing multiple run results.

## Requirements

**pmxploit** can read runs executed with NONMEM 7.2 and higher (with results data contained in an xml file).

## Installation

``` r
# Install the latest version from GitHub
# install.packages('devtools')
devtools::install_github('pnolain/pmxploit')
```

## Getting started

```{r, eval = FALSE}
library(pmxploit)

# load a NONMEM run from its location
run <- load_nm_run("~/pmxploit/example")

# or from an archive file
# run <- load_nm_run("~/pmxploit/example.tar.gz")
```

### Diagnostic plots

```{r dv_vs_pred}
plot_dv_vs_predictions(run, compartment = 2L, predictions = c("PRED", "IPRED"),
                       x_scale = "log", y_scale = "log")
```

```{r residuals}
plot_residuals(run, compartment = 2L, residuals = "CWRES", idv = c("TIME", "PRED"))
```

```{r residuals2, fig.height=7}
run %>%
  group_by(CMT) %>%
  plot_residuals(compartment = c(2L, 3L), residuals = "CWRES", idv = c("TIME", "PRED"))
```

### Parameters

```{r convergence, message=FALSE}
plot_convergence(run, parameters = "theta")
```


```{r}
run %>% 
  summarize_thetas()
```

```{r}
run %>% 
  summarize_omega()
```

```{r}
run %>% 
  summarize_shrinkage(type = "ETA")
```