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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')

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.