ggPMX v0.9.4


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'ggplot2' Based Tool to Facilitate Diagnostic Plots for NLME Models

At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling activities in order to reduce the overall effort required for generating such plots. For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox, called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient for publication and submissions using few lines of code.


ggPMX R package

Travis-CI Build Status CRAN\_Status\_Badge

ggPMX is an open-source R package freely available on CRAN since April 2019. It generates standard diagnostic plots for mixed effect models used in pharmacometric activities. The package builds on the R-package ggplot2 and aims at providing a workflow that is consistent, reproducible and efficient, resulting in high quality graphics ready-to-use in submission documents and publications. Intuitive functions and options allow for optimal figure customization and graphics stratification. ggPMX enables straightforward generation of PDF, Word or PNG output files that contain all diagnostic plots for keeping track of modeling results. The package is currently compatible with Monolix versions 2016 and 2018R1.

Using simple syntax, the toolbox produces various goodness-of-fit diagnostics such as:

  • residual- and empirical Bayes estimate (EBE)-based plots,
  • distribution plots,
  • prediction- and simulation-based diagnostics (visual predictive checks).

In addition, shrinkage and summary parameters tables can be also produced. By default, the PDF- or Word-format diagnostic report contains essential goodness-of-fit plots. However, these can be adapted to produce different sets of diagnostics as desired by the user, and any of the plots may be customized individually. The types of supported customizations include modifications of the graphical parameters, labels, and various stratifications by covariates.



Install ggPMX



Github version:



ggPMX is now ready for inputs and enhancements by the pharmacometric community.

Functions in ggPMX

Name Description
FacetWrapPaginate Extend facet_wrap using ggproto
eta_cov This creates an ETA covariance matrix which can be used to define the co-relation between the parameters and its shrinkage..
facet_wrap_paginate Split facet_wrap over multiple plots
eta_pairs This creates an eta correlation which defines the relationship between parameters
eta_distribution_plot Eta distribution plots
distrib creates a graphic distribution object
add_draft Add draft layer annotation
eta_cov_plot Eta Covariates plots
getPmxOption Get ggPMX Option
abbrev Give the whole abbreviation definition
get_abbrev Get abbreviation definition by key
get_data Get controller data set
parse_mlxtran Parse MONOLIX mlxtran file
n_pages Determine the number of pages in a paginated facet plot
gtable_remove_grobs Remove named elements from gtable
individual This function can be used to obtain individual prediction and compare with observed data and population prediction for each individual separately
get_plot_config Get the plot config by name
get_plot Get plot object
plot_pmx.individual This function can be used to plot individual prediction and compare with observed data and population prediction for each individual separately
plot_pmx.eta_pairs Plot random effect correlation plot
get_conts Get continuous covariates
is.pmx_gpar Check if an object is a pmx_gpar class
get_occ Get controller occasional covariates
plot_names Get plot names
pmx_bloq Creates BLOQ object attributes
input_finegrid Merge input and fingrid data sets
pk_occ Creates pmx controller using monlix data having Occasional variable
pk_pd Creates pkpd pmx controller using package internal data
get_covariates Get covariates variables
get_cats Get category covariates
plot_pmx This is a generic plot method that produces all plots by default described in pmx model evaluation guidance.
get_strats Get extra stratification variables
pmx_comp_shrink Compute Shrinkage
pmx_gpar Handling pmx Graphical parameters
pmx_theme Define ggPMX theme
print.configs This function can be used to print configuration of the defined object using S3 method.
print.pmxClass Print pmxClass object
pmx_get_configs Get List of built-in configurations
pmx_sim Create simulation object
l_left_join Merge 2 lists
ggPMX ggPMX: A ggplot2 toolbox for Nonlinear Mixed-Effect Model graphical
pmx Create a pmx object
load_config Obtain the data source config
pmxOptions This function can be used to set ggPMX options
pmx_register_plot Register plot
pmx_plot_individual Individual plot
pmx_plot_eta_matrix Eta matrix plot
plot_pmx.pmx_qq This function plot EBE versus covariates using qq plots
load_data_set Load data set
pmx_plot Generic pmx plot
load_source Load all/or some source data set
plot_pmx.distrib Plot EBE distribution
plot_pmx.residual This function plots residual for each observed value by finding the difference between observed and predicted points. It also fits a distribution to the residual value.
plot_pmx.pmx_dens This function plot EBE versus covariates using qq plots
plot_shrink Plot shrink in eta matric
plots Get plots description
pmx_endpoint Creates pmx endpoint object
plot_pmx.pmx_gpar The ggPMX base plot function
print.pmxConfig S3 method print pmxConfig object
[.pmx_gpar Method for subsetting "pmx_gpar" objects
pmx_config This function can be used to define the pmx configuration used in plots. e.g. Monolox/Nonmem
pmx_copy Creates a deep copy of the controller
print.pmx_gpar Print pmx_gpar object
pmx_plot_cats Generic pmx stratified plot
pmx_plot_iwres_dens IWRES density plot
reexports Objects exported from other packages
pmx_plot_vpc VPC plot
pmx_vpc_obs Sets vpc observation layer
residual This function create a residual for each observed value and also generates a residual distribution
pmx_filter filter data in a pmx controller
theophylline Creates pmx controller using theophylline data
pmx_vpc_bin Creates vpc bins
set_abbrev update or add a new abbreviation
pmx_report Generates ggpmX report from a pre-defined template
pmx_vpc_ci Sets vpc confidence interval layer
residual_scatter Scatter residual plots
pmx_report_template Gets build-in report templates
pmx_update Update plot object
pmx_settings Create controller global settings
pmx_vpc Creates vpc object
read_input Read Modelling input data
pmx_dens Creates a density plot object
pmx_cov Select/Map covariates using human labels
plot_pmx.eta_cov This plots an ETA covariance matrix which can be used to define the co-relation between the parameters and its shrinkage
print.abbreviation S3 print abbreviation
pmx_qq_plot Quantile-quantile plots
pmx_vpc_rug Sets vpc rug layer
pmx_qq This function creates a qq plot object
read_mlx_par_est Read MONOLIX parameter estimation file
pmx_vpc_pi Sets vpc percentile layer
read_mlx_ind_est Read MONOLIX individual parameters
set_data Set a controller data set
set_plot Create a new plot of the desired type
read_mlx_pred Read MONOLIX model predictions
wrap_formula merge facets formula with new formula
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Last month downloads


License GPL-2
LazyData true
VignetteBuilder knitr
NeedsCompilation no
RoxygenNote 6.0.1
Packaged 2019-06-06 11:40:34 UTC; agstudy
Repository CRAN
Date/Publication 2019-06-06 12:00:03 UTC

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