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

run001.lst overview: 
 - Software: nonmem 7.3.0 
 - 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 = data, quiet = TRUE, manual_import = NULL

Model summary

summary(xpdb, problem = 1)

Summary for problem no. 0 [Global information] 
 - Software                      @software   : nonmem
 - Software version              @version    : 7.3.0
 - Run directory                 @dir        : data
 - Run file                      @file       : run001.lst
 - Run number                    @run        : run001
 - Reference model               @ref        : 000
 - Run description               @descr      : NONMEM PK example for xpose
 - Run start time                @timestart  : Mon Oct 16 13:34:28 CEST 2017
 - Run stop time                 @timestop   : Mon Oct 16 13:34:35 CEST 2017

Summary for problem no. 1 [Parameter estimation] 
 - Input data                    @data       : ../../mx19_2.csv
 - Number of individuals         @nind       : 74
 - Number of observations        @nobs       : 476
 - ADVAN                         @subroutine : 2
 - Estimation method             @method     : foce-i
 - Termination message           @term       : MINIMIZATION SUCCESSFUL
 - Estimation runtime            @runtime    : 00:00:02
 - Objective function value      @ofv        : -1403.905
 - Number of significant digits  @nsig       : 3.3
 - Covariance step runtime       @covtime    : 00:00:03
 - Condition number              @condn      : 21.5
 - Eta shrinkage                 @etashk     : 9.3 [1], 28.7 [2], 23.7 [3]
 - Epsilon shrinkage             @epsshk     : 14.9 [1]
 - Run warnings                  @warnings   : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.

Summary for problem no. 2 [Model simulations] 
 - Input data                    @data       : ../../mx19_2.csv
 - Number of individuals         @nind       : 74
 - Number of observations        @nobs       : 476
 - Estimation method             @method     : sim
 - Number of simulations         @nsim       : 20
 - Simulation seed               @simseed    : 221287
 - Run warnings                  @warnings   : (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION.
                                               (WARNING 22) WITH $MSFI AND "SUBPROBS", "TRUE=FINAL" ...

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.