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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
library(xpose)
xpdb <- xpdb_ex_pk
xpdb$options$quiet <- TRUE
xpdb <- as.xpdb(xpdb)
knitr::opts_chunk$set(fig.path = 'man/figures/',
fig.dpi = 96,
fig.height = 4,
fig.width = 4,
out.width = '50%',
fig.align = 'center')
```
# xpose <a href="https://UUPharmacometrics.github.io/xpose/"><img src="man/figures/logo.png" align="right" /></a>
[](https://travis-ci.org/UUPharmacometrics/xpose)
[](https://ci.appveyor.com/project/guiastrennec/xpose)
[](https://CRAN.R-project.org/package=xpose)
[](https://codecov.io/gh/UUPharmacometrics/xpose)
[](https://www.tidyverse.org/lifecycle/)
[](https://CRAN.R-project.org/package=xpose)
## Overview
[xpose](https://UUPharmacometrics.github.io/xpose/) was designed as a [ggplot2](https://ggplot2.tidyverse.org/)-based alternative to [xpose4](http://xpose.sourceforge.net). xpose aims to reduce the post processing burden and improve diagnostics commonly associated the development of non-linear mixed effect models.
## Installation
``` r
# 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')
```
## Getting started
### Load xpose
``` r
library(xpose)
```
### Import run output
``` r
xpdb <- xpose_data(runno = '001')
```
### Glance at the data object
```{r, comment = ''}
xpdb
```
#### Model summary
```{r, comment = ''}
summary(xpdb, problem = 1)
```
### Generate diagnostics
#### Standard goodness-of-fit plots
```{r readme_example_figure_1}
dv_vs_ipred(xpdb)
```
#### Individual plots
```{r readme_example_figure_2, fig.width = 6, out.width = '75%'}
ind_plots(xpdb, page = 1)
```
#### Visual predictive checks
```{r readme_example_figure_3, fig.width = 6, out.width = '75%', warning = FALSE}
xpdb %>%
vpc_data(stratify = 'SEX', opt = vpc_opt(n_bins = 7, lloq = 0.1)) %>%
vpc()
```
#### Distribution plots
```{r readme_example_figure_4, fig.width = 6, out.width = '75%'}
eta_distrib(xpdb, labeller = 'label_value')
```
#### Minimization diagnostics
```{r readme_example_figure_5, fig.width = 6, out.width = '75%'}
prm_vs_iteration(xpdb, labeller = 'label_value')
```
## Recommended reading
The [xpose website](https://UUPharmacometrics.github.io/xpose/) contains several useful articles to make full use of xpose
When working with xpose, a working knowledge of ggplot2 is recommended. Help for ggplot2 can be found in:
- The ggplot2 [documentation](https://ggplot2.tidyverse.org/reference/)
- The ggplot2 [mailing list](https://groups.google.com/forum/?fromgroups#!forum/ggplot2)
- Internet resources (stack overflow, etc.)
## Contribute
Please note that the xpose project is released with a [Contributor Code of Conduct](https://github.com/UUPharmacometrics/xpose/blob/master/.github/CODE_OF_CONDUCT.md) and [Contributing Guidelines](https://github.com/UUPharmacometrics/xpose/blob/master/.github/CONTRIBUTING.md). By contributing to this project, you agree to abide these.