In guiastrennec/xpose: Diagnostics for Pharmacometric Models

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.align  = 'center')

xpose

Overview

xpose was designed as a ggplot2-based alternative to xpose4. xpose aims to reduce the post processing burden and improve diagnostics commonly associated the development of non-linear mixed effect models.

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')

Getting started

Load xpose

library(xpose)

Import run output

xpdb <- xpose_data(runno = '001')

Glance at the data object

xpdb

Model summary

summary(xpdb, problem = 1)

Generate diagnostics

Standard goodness-of-fit plots

dv_vs_ipred(xpdb)

Individual plots

ind_plots(xpdb, page = 1)

Visual predictive checks

xpdb %>% 
  vpc_data(stratify = 'SEX', opt = vpc_opt(n_bins = 7, lloq = 0.1)) %>% 
  vpc()

Distribution plots

eta_distrib(xpdb, labeller = 'label_value')

Minimization diagnostics

prm_vs_iteration(xpdb, labeller = 'label_value')

Recommended reading

The xpose website 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
• The ggplot2 mailing list
• Internet resources (stack overflow, etc.)

Contribute

Please note that the xpose project is released with a Contributor Code of Conduct and Contributing Guidelines. By contributing to this project, you agree to abide these.

guiastrennec/xpose documentation built on Feb. 16, 2024, 8:14 p.m.