Title:	'ggplot2' Based Tool to Facilitate Diagnostic Plots for NLME Models
Description:	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. This package focuses on plots recommended by ISoP <doi:10.1002/psp4.12161>. While not required, you can get/install the 'R' 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization. When 'lixoftConnectors' is available, 'R' can use 'Monolix' directly to create the required Chart Data instead of exporting it from the 'Monolix' gui.
Version:	1.3.0
URL:	https://github.com/ggPMXdevelopment/ggPMX
BugReports:	https://github.com/ggPMXdevelopment/ggPMX/issues
Depends:	R (≥ 3.5)
Imports:	data.table, yaml, R6, gtable, ggplot2 (≥ 3.4.0), ggforce, magrittr, stringr, assertthat, GGally, zoo, knitr, rmarkdown, tidyr, dplyr, purrr, readr, rlang, checkmate, scales
License:	GPL-2
Suggests:	testthat, xtable, vdiffr, rxode2, nlmixr2est, nlmixr2data, nlme, lixoftConnectors, xgxr, withr, lifecycle
VignetteBuilder:	knitr
NeedsCompilation:	no
RoxygenNote:	7.3.2
Encoding:	UTF-8
Packaged:	2025-07-16 08:10:33 UTC; codespace
Author:	Amine Gassem [aut], Bruno Bieth [aut], Irina Baltcheva [aut], Thomas Dumortier [aut], Christian Bartels [aut], Souvik Bhattacharya [aut], Inga Ludwig [aut], Ines Paule [aut], Didier Renard [aut], Matthew Fidler [aut], Seid Hamzic [aut], Benjamin Guiastrennec [ctb], Kyle T Baron [ctb], Qing Xi Ooi [ctb], Aleksandr Pogodaev [aut, cre], Danielle Navarro [aut], Ibtissem Rebai [ctb], Mahmoud Ali [ctb], Novartis Pharma AG [cph]
Maintainer:	Aleksandr Pogodaev <alex.pogodaev@novartis.com>
Repository:	CRAN
Date/Publication:	2025-07-16 10:20:02 UTC

ggPMX: A ggplot2 toolbox for Nonlinear Mixed-Effect Model graphical

Description

This package aims to generate diagnostic plots in a standard way. The tool reads data from many sources (MONOLIX,NONMEM,others) and generates standard grahics that can be easily integrated in a single report.

Details

• Get data from different system and create a data source

• Plot many plots using the generic plot method plot_pmx.

For support, feedback or bug reports, please reach out to ggPMX_ORG@dl.mgd.novartis.com.

Version History

Jan 11 2017, 0.0.0

Init ggPMX from Novartis rtemplate.

Feb 06 2017, 0.3.0

Import version 0.3.0 of package.

Author(s)

Maintainer: Aleksandr Pogodaev alex.pogodaev@novartis.com (ORCID)

Authors:

• Amine Gassem contact@ag-study.com

• Bruno Bieth biethb@hotmail.com

• Irina Baltcheva irina.baltcheva@gmail.com

• Thomas Dumortier thomas.dumortier@novartis.com

• Christian Bartels christian.bartels@novartis.com

• Souvik Bhattacharya souvik.bhattacharya@novartis.com

• Inga Ludwig inga.ludwig@novartis.com

• Ines Paule ines.paule@novartis.com

• Didier Renard didier.renard@novartis.com

• Matthew Fidler matt.fidler@novartis.com (ORCID)

• Seid Hamzic se.hamzic@gmail.com

• Danielle Navarro djnavarro@protonmail.com

Other contributors:

• Benjamin Guiastrennec guiastrennec@gmail.com [contributor]

• Kyle T Baron kyleb@metrumrg.com (ORCID) [contributor]

• Qing Xi Ooi ooi.qingxi@gmail.com [contributor]

• Ibtissem Rebai ibtissem.rebai@certara.com [contributor]

• Mahmoud Ali mahmoud.ali@certara.com [contributor]

• Novartis Pharma AG [copyright holder]

See Also

Useful links:

• https://github.com/ggPMXdevelopment/ggPMX

• Report bugs at https://github.com/ggPMXdevelopment/ggPMX/issues

Method for subsetting "pmx_gpar" objects

Description

Method for subsetting "pmx_gpar" objects

Usage

## S3 method for class 'pmx_gpar'
x[index, ...]

Arguments

Value

if exists the parameter description

Give the whole abbreviation definition

Description

Give the whole abbreviation definition

Usage

abbrev(param)

Arguments

Value

character abbreviation definition

Examples

abbrev("VPC")

Add draft layer annotation

Description

This function adds the word draft to certain graphics.

Usage

add_draft(
  label = "DRAFT",
  size = 10,
  colour = "grey50",
  x = Inf,
  y = -Inf,
  ...
)

Arguments

Value

ggplot2 annotation

Performs checks of names in shrink list

Description

Performs checks of names in shrink list

Usage

check_shrink(shrink_list)

Arguments

creates a graphic distribution object

Description

creates a graphic distribution object

Usage

distrib(
  labels,
  is.shrink,
  type = c("box", "hist"),
  is.jitter = FALSE,
  jitter = NULL,
  facets = NULL,
  histogram = NULL,
  shrink = NULL,
  dname = NULL,
  ...
)

Arguments

Details

labels is a list that contains:

• title: plot title default "EBE distribution"

• subtitle: plot subtitle default empty

• x: x axis label default to "Etas"

• y: y axis label default to empty

• legend: legend title default to "random Effect"

shrink is a list that contains:

• fun: shrinkage function can be sd or var

• size: shrinkage text size

• color: shrinkage text color

• vjust: shrinkage position vertical adjustment

Value

distrib object

See Also

Other plot_pmx: eta_cov(), eta_pairs(), individual(), plot_pmx(), plot_pmx.distrib(), plot_pmx.eta_cov(), plot_pmx.eta_pairs(), plot_pmx.individual(), plot_pmx.pmx_dens(), plot_pmx.pmx_gpar(), plot_pmx.pmx_param_history(), plot_pmx.pmx_qq(), plot_pmx.residual()

This creates an ETA covariance matrix which can be used to define the co-relation between the parameters and its shrinkage..

Description

This creates an ETA covariance matrix which can be used to define the co-relation between the parameters and its shrinkage..

Usage

eta_cov(
  labels,
  type = c("cats", "conts"),
  dname = NULL,
  show.correl = TRUE,
  correl = NULL,
  facets = NULL,
  point = NULL,
  covariates = NULL,
  is.strat.color = FALSE,
  ...
)

Arguments

Details

labels is a list that contains:

• title: plot title default "EBE vs. covariates"

• x: x axis label default to "Etas"

• y: y axis label default to empty

Value

eta_cov object

See Also

Other plot_pmx: distrib(), eta_pairs(), individual(), plot_pmx(), plot_pmx.distrib(), plot_pmx.eta_cov(), plot_pmx.eta_pairs(), plot_pmx.individual(), plot_pmx.pmx_dens(), plot_pmx.pmx_gpar(), plot_pmx.pmx_param_history(), plot_pmx.pmx_qq(), plot_pmx.residual()

Eta Covariates plots

Description

Eta Covariates plots

Relationships between (ETA) and categorical covariates

Relationships between (ETA) and continuous covariates

Usage

dummy(
  dname,
  show.correl,
  correl,
  point,
  facets,
  covariates,
  filter,
  strat.facet,
  strat.color,
  trans,
  pmxgpar,
  labels,
  axis.title,
  axis.text,
  ranges,
  is.smooth,
  smooth,
  is.band,
  band,
  is.draft,
  draft,
  is.identity_line,
  identity_line,
  scale_x_log10,
  scale_y_log10,
  color.scales
)

pmx_plot_eta_cats(ctr, ...)

pmx_plot_eta_conts(ctr, ...)

Arguments

Value

ggplot2 object

ggplot2 object

ggplot2 object

Examples

# basic use  ---------------------------------------

ctr <- theophylline()
ctr %>% pmx_plot_eta_cats
ctr %>% pmx_plot_eta_conts

# update graphical parameter  ----------------------

## plot selected covariates
ctr %>% pmx_plot_eta_cats(
  covariates = pmx_cov(values = list("SEX"))
  )


## update labels
ctr %>% pmx_plot_eta_cats(
  labels = list(title = "New eta cats title")
)

## remove draft
ctr %>% pmx_plot_eta_cats(is.draft = FALSE)

## change text color line
ctr %>% pmx_plot_eta_conts(
  correl=list(colour="magenta")
  )

## set covariates custom labels 

ctr %>% pmx_plot_eta_conts(
  covariates=pmx_cov(values=list("WT0","AGE0"),
                     labels=list("Weight","Age"))
)

## set effects and covaraites custom labels


ctr <- theophylline( settings = pmx_settings(
  effects=list( levels=c("ka", "V", "Cl"), 
                labels=c("Concentration","Volume","Clearance")
  )
)
)
ctr %>% pmx_plot_eta_conts(
  covariates=pmx_cov(values=list("WT0","AGE0"),
                     labels=list("Weight","Age"))
)

Eta distribution plots

Description

Eta distribution plots

Eta Distribution boxplot

Eta Distribution histogram plot

Usage

eta_distribution_plot(
  jitter,
  type,
  dname,
  is.shrink,
  shrink,
  is.jitter,
  histogram,
  filter,
  strat.facet,
  facets,
  strat.color,
  trans,
  pmxgpar,
  labels,
  axis.title,
  axis.text,
  ranges,
  is.smooth,
  smooth,
  is.band,
  band,
  is.draft,
  draft,
  is.identity_line,
  identity_line,
  scale_x_log10,
  scale_y_log10,
  color.scales,
  ...
)

pmx_plot_eta_box(ctr, ...)

pmx_plot_eta_hist(ctr, ...)

Arguments

Value

ggplot2 object

Examples

# *************** basic use ***************** ------

ctr <- theophylline()
## boxplot variation
p <- ctr %>% pmx_plot_eta_box()
## histogram variation
p <- ctr %>% pmx_plot_eta_hist()

# update graphical parameter  ----------------------

## add jitter
ctr %>%
  pmx_plot_eta_hist(is.jitter = TRUE, jitter = list(alpha = 0.4, color = "red"))

## remove shrinkage
ctr %>%
  pmx_plot_eta_hist(is.shrink = FALSE)

## update histogram graphical parameters
ctr %>%
  pmx_plot_eta_hist(
    histogram = list(
      color = NA,
      position = "fill",
      binwidth = 1 / 100
    )
  )



# stratification  ----------------------------------

## categorical stratification color parameter
ctr %>% pmx_plot_eta_hist(is.jitter = TRUE, strat.facet = ~STUD, strat.color = ~SEX)
  
## categorical stratification facetting
ctr %>% pmx_plot_eta_hist(strat.facet = ~SEX)

## using formula categorical stratification facetting
ctr %>% pmx_plot_eta_hist(
  strat.facet = STUD ~ SEX,
  shrink = pmx_shrink(hjust = 0.5)
)

# subsetting  --------------------------------------

## select a set of random effect
ctr %>% pmx_plot_eta_hist(filter = EFFECT %in% c("ka", "Cl"))
## filter and stratify by facets
ctr %>% pmx_plot_eta_hist(
  filter = EFFECT %in% c("ka", "Cl"), strat.facet = ~SEX
)
ctr %>% pmx_plot_eta_hist(
  filter = EFFECT %in% c("ka", "Cl"), strat.facet = ~SEX
)

This creates an eta correlation which defines the relationship between parameters

Description

This creates an eta correlation which defines the relationship between parameters

Usage

eta_pairs(
  is.title,
  title,
  dname = NULL,
  type.eta = c("mode", "mean"),
  text_color = "black",
  is.shrink = TRUE,
  is.smooth = TRUE,
  smooth = NULL,
  point = NULL,
  shrink = NULL,
  is.hline = FALSE,
  hline = NULL,
  is.vreference_line = FALSE,
  vreference_line = list(colour = "orange", linetype = "longdash"),
  ...
)

Arguments

Value

ecorrel object

See Also

Other plot_pmx: distrib(), eta_cov(), individual(), plot_pmx(), plot_pmx.distrib(), plot_pmx.eta_cov(), plot_pmx.eta_pairs(), plot_pmx.individual(), plot_pmx.pmx_dens(), plot_pmx.pmx_gpar(), plot_pmx.pmx_param_history(), plot_pmx.pmx_qq(), plot_pmx.residual()

Try to evaluate a symbol in the parent frame (on error return the symbol)

Description

Try to evaluate a symbol in the parent frame (on error return the symbol)

Usage

eval_sym_parent_env(x)

Arguments

Value

evaluated symbol or the symbol in case of evaluation error

Get ggPMX Option

Description

Get ggPMX Option

Usage

getPmxOption(name, default = NULL)

Arguments

Value

value of the option or NULL

Examples

## Not run: 
pmxOptions(myOption = 10)
getPmxOption("myOption")

## End(Not run)

Get abbreviation definition by key

Description

Get abbreviation definition by key

Usage

get_abbrev(ctr, param)

Arguments

Value

character abbreviation definition

Get category covariates

Description

Get category covariates

Usage

get_cats(ctr)

Arguments

Value

a charcater vector

See Also

Other pmxclass: get_conts(), get_covariates(), get_data(), get_occ(), get_plot(), get_plot_config(), get_strats(), plot_names(), plots(), pmx_update(), set_data(), set_plot()

Get continuous covariates

Description

Get continuous covariates

Usage

get_conts(ctr)

Arguments

Value

a charcater vector

See Also

Other pmxclass: get_cats(), get_covariates(), get_data(), get_occ(), get_plot(), get_plot_config(), get_strats(), plot_names(), plots(), pmx_update(), set_data(), set_plot()

Get covariates variables

Description

Get covariates variables

Usage

get_covariates(ctr)

Arguments

Value

a charcater vector

See Also

Other pmxclass: get_cats(), get_conts(), get_data(), get_occ(), get_plot(), get_plot_config(), get_strats(), plot_names(), plots(), pmx_update(), set_data(), set_plot()

Get controller data set

Description

Get controller data set

Usage

get_data(
  ctr,
  data_set = c("estimates", "predictions", "eta", "finegrid", "input", "sim",
    "individual")
)

Arguments

Value

a data.table of the named data set if available.

See Also

Other pmxclass: get_cats(), get_conts(), get_covariates(), get_occ(), get_plot(), get_plot_config(), get_strats(), plot_names(), plots(), pmx_update(), set_data(), set_plot()

Get controller occasional covariates

Description

Get controller occasional covariates

Usage

get_occ(ctr)

Arguments

Value

a charcater vector

See Also

Other pmxclass: get_cats(), get_conts(), get_covariates(), get_data(), get_plot(), get_plot_config(), get_strats(), plot_names(), plots(), pmx_update(), set_data(), set_plot()

Get plot object

Description

Get plot object

Usage

get_plot(ctr, nplot, which_pages = "all")

Arguments

Value

ggplot object

See Also

Other pmxclass: get_cats(), get_conts(), get_covariates(), get_data(), get_occ(), get_plot_config(), get_strats(), plot_names(), plots(), pmx_update(), set_data(), set_plot()

Examples

library(ggPMX)
ctr <- theophylline()
p1 <- ctr %>% get_plot("iwres_ipred")
## get all pages or some pages
p2 <- ctr %>% get_plot("individual")
## returns one page of individual plot
p2 <- ctr %>% get_plot("individual", which_pages = 1)
p3 <- ctr %>% get_plot("individual", which_pages = c(1, 3))
## get distribution plot
pdistri <- ctr %>% get_plot("eta_hist")

Get the plot config by name

Description

Get the plot config by name

Usage

get_plot_config(ctr, pname)

Arguments

Value

the config object

See Also

Other pmxclass: get_cats(), get_conts(), get_covariates(), get_data(), get_occ(), get_plot(), get_strats(), plot_names(), plots(), pmx_update(), set_data(), set_plot()

Examples

ctr <- theophylline()
ctr %>% set_plot("IND", pname = "indiv1")
ctr %>% get_plot_config("distr1")

Get extra stratification variables

Description

Get extra stratification variables

Usage

get_strats(ctr)

Arguments

Value

a charcater vector

See Also

Other pmxclass: get_cats(), get_conts(), get_covariates(), get_data(), get_occ(), get_plot(), get_plot_config(), plot_names(), plots(), pmx_update(), set_data(), set_plot()

Remove named elements from gtable

Description

Remove named elements from gtable

Usage

gtable_remove_grobs(table, names, ...)

Arguments

Value

table The table with removed grobs

This function can be used to obtain individual prediction and compare with observed data and population prediction for each individual separately

Description

This function can be used to obtain individual prediction and compare with observed data and population prediction for each individual separately

Usage

individual(
  labels,
  facets = NULL,
  dname = NULL,
  ipred_line = NULL,
  pred_line = NULL,
  point = NULL,
  bloq = NULL,
  is.legend,
  use.finegrid,
  ...
)

Arguments

Value

individual fit object

See Also

plot_pmx.individual

Other plot_pmx: distrib(), eta_cov(), eta_pairs(), plot_pmx(), plot_pmx.distrib(), plot_pmx.eta_cov(), plot_pmx.eta_pairs(), plot_pmx.individual(), plot_pmx.pmx_dens(), plot_pmx.pmx_gpar(), plot_pmx.pmx_param_history(), plot_pmx.pmx_qq(), plot_pmx.residual()

Merge input and fingrid data sets

Description

Merge input and fingrid data sets

Usage

input_finegrid(input, finegrid)

Arguments

Value

data.table

Check if an object is a pmx_gpar class

Description

Check if an object is a pmx_gpar class

Usage

is.pmx_gpar(x)

Arguments

Value

logical returns TRUE if it is a pmx_gpar object

Merge 2 lists

Description

left join , the first list is updated by the seond one

Usage

l_left_join(base_list, overlay_list, recursive = TRUE)

Arguments

Value

list

Obtain the data source config

Description

Obtain the data source config

Usage

load_config(x, sys = c("mlx", "nm", "mlx18"))

Arguments

Value

a list :data configuration object

Load data set

Description

Load data set

Usage

load_data_set(x, path, sys, ...)

Arguments

Value

data.table

Load all/or some source data set

Description

Load all/or some source data set

Usage

load_source(sys, path, dconf, ...)

Arguments

Value

list of data.table

Determine the number of pages in a paginated facet plot

Description

This is a simple helper that returns the number of pages it takes to plot all panels when using facet_wrap_paginate . It partially builds the plot so depending on the complexity of your plot it might take some time to calculate...

Usage

n_pages(plot)

Arguments

Value

If the plot uses using either facet_wrap_paginate or facet_grid_paginate it returns the total number of pages. Otherwise it returns NULL

Creates parameter kable

Description

Creates parameter kable

Usage

param_table(ctr, fun, return_table = FALSE, scientific = FALSE, digits = 2)

Arguments

Value

Returns a kable with the parameter estimates from get_data('estimates')

Examples

#ctr <- theophylline()
#my_params <- ctr %>% param_table(fun = "var")

Parse MONOLIX mlxtran file

Description

Parse MONOLIX mlxtran file

Usage

parse_mlxtran(file_name)

Arguments

Value

list key/values to initialize ggPMX controller

Creates pmx controller using monlix data having Occasional variable

Description

Creates pmx controller using monlix data having Occasional variable

Usage

pk_occ()

Value

pmx controller

Examples

## Not run: 
pk_occ()

## End(Not run)

Creates pkpd pmx controller using package internal data

Description

Creates pkpd pmx controller using package internal data

Usage

pk_pd(code = "3")

Arguments

Get plot names

Description

Get plot names

Usage

plot_names(ctr)

Arguments

Value

list of plot names

See Also

Other pmxclass: get_cats(), get_conts(), get_covariates(), get_data(), get_occ(), get_plot(), get_plot_config(), get_strats(), plots(), pmx_update(), set_data(), set_plot()

This is a generic plot method that produces all plots by default described in pmx model evaluation guidance.

Description

This is a generic plot method that produces all plots by default described in pmx model evaluation guidance.

Usage

plot_pmx(x, dx, ...)

## S3 method for class 'pmx_vpc'
plot_pmx(x, dx, ...)

Arguments

See Also

pmx_gpar.

Other plot_pmx: distrib(), eta_cov(), eta_pairs(), individual(), plot_pmx.distrib(), plot_pmx.eta_cov(), plot_pmx.eta_pairs(), plot_pmx.individual(), plot_pmx.pmx_dens(), plot_pmx.pmx_gpar(), plot_pmx.pmx_param_history(), plot_pmx.pmx_qq(), plot_pmx.residual()

Plot EBE distribution

Description

Plot EBE distribution

Usage

## S3 method for class 'distrib'
plot_pmx(x, dx, ...)

Arguments

Value

ggplot2 plot

See Also

distrib

Other plot_pmx: distrib(), eta_cov(), eta_pairs(), individual(), plot_pmx(), plot_pmx.eta_cov(), plot_pmx.eta_pairs(), plot_pmx.individual(), plot_pmx.pmx_dens(), plot_pmx.pmx_gpar(), plot_pmx.pmx_param_history(), plot_pmx.pmx_qq(), plot_pmx.residual()

This plots an ETA covariance matrix which can be used to define the co-relation between the parameters and its shrinkage

Description

This plots an ETA covariance matrix which can be used to define the co-relation between the parameters and its shrinkage

Usage

## S3 method for class 'eta_cov'
plot_pmx(x, dx, ...)

Arguments

Value

ggplot2 plot

See Also

eta_cov

Other plot_pmx: distrib(), eta_cov(), eta_pairs(), individual(), plot_pmx(), plot_pmx.distrib(), plot_pmx.eta_pairs(), plot_pmx.individual(), plot_pmx.pmx_dens(), plot_pmx.pmx_gpar(), plot_pmx.pmx_param_history(), plot_pmx.pmx_qq(), plot_pmx.residual()

Plot random effect correlation plot

Description

Plot random effect correlation plot

Usage

## S3 method for class 'eta_pairs'
plot_pmx(x, dx, ...)

Arguments

Value

ggpairs plot

See Also

distrib

Other plot_pmx: distrib(), eta_cov(), eta_pairs(), individual(), plot_pmx(), plot_pmx.distrib(), plot_pmx.eta_cov(), plot_pmx.individual(), plot_pmx.pmx_dens(), plot_pmx.pmx_gpar(), plot_pmx.pmx_param_history(), plot_pmx.pmx_qq(), plot_pmx.residual()

This function can be used to plot individual prediction and compare with observed data and population prediction for each individual separately

Description

This function can be used to plot individual prediction and compare with observed data and population prediction for each individual separately

Usage

## S3 method for class 'individual'
plot_pmx(x, dx, ...)

Arguments

Value

a list of ggplot2

See Also

Other plot_pmx: distrib(), eta_cov(), eta_pairs(), individual(), plot_pmx(), plot_pmx.distrib(), plot_pmx.eta_cov(), plot_pmx.eta_pairs(), plot_pmx.pmx_dens(), plot_pmx.pmx_gpar(), plot_pmx.pmx_param_history(), plot_pmx.pmx_qq(), plot_pmx.residual()

This function plots EBE versus covariates using qq plots

Description

This function plots EBE versus covariates using qq plots

Usage

## S3 method for class 'pmx_dens'
plot_pmx(x, dx, ...)

Arguments

Value

ggplot2 plot

See Also

eta_cov

Other plot_pmx: distrib(), eta_cov(), eta_pairs(), individual(), plot_pmx(), plot_pmx.distrib(), plot_pmx.eta_cov(), plot_pmx.eta_pairs(), plot_pmx.individual(), plot_pmx.pmx_gpar(), plot_pmx.pmx_param_history(), plot_pmx.pmx_qq(), plot_pmx.residual()

The ggPMX base plot function

Description

This function should be called internally by other plots to set general settings like , smoothing, add band, labelling, theming,...

Usage

## S3 method for class 'pmx_gpar'
plot_pmx(x, dx, ...)

Arguments

Value

ggplot2 object

See Also

Other plot_pmx: distrib(), eta_cov(), eta_pairs(), individual(), plot_pmx(), plot_pmx.distrib(), plot_pmx.eta_cov(), plot_pmx.eta_pairs(), plot_pmx.individual(), plot_pmx.pmx_dens(), plot_pmx.pmx_param_history(), plot_pmx.pmx_qq(), plot_pmx.residual()

S3 method for plots of class pmx_param_history

Description

S3 method for plots of class pmx_param_history

Usage

## S3 method for class 'pmx_param_history'
plot_pmx(x, dx, ...)

Arguments

Value

ggplot2 plot

See Also

Other plot_pmx: distrib(), eta_cov(), eta_pairs(), individual(), plot_pmx(), plot_pmx.distrib(), plot_pmx.eta_cov(), plot_pmx.eta_pairs(), plot_pmx.individual(), plot_pmx.pmx_dens(), plot_pmx.pmx_gpar(), plot_pmx.pmx_qq(), plot_pmx.residual()

This function plot EBE versus covariates using qq plots

Description

This function plot EBE versus covariates using qq plots

Usage

## S3 method for class 'pmx_qq'
plot_pmx(x, dx, ...)

Arguments

Value

ggplot2 plot

See Also

eta_cov

Other plot_pmx: distrib(), eta_cov(), eta_pairs(), individual(), plot_pmx(), plot_pmx.distrib(), plot_pmx.eta_cov(), plot_pmx.eta_pairs(), plot_pmx.individual(), plot_pmx.pmx_dens(), plot_pmx.pmx_gpar(), plot_pmx.pmx_param_history(), 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.

Description

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.

Usage

## S3 method for class 'residual'
plot_pmx(x, dx, ...)

Arguments

Value

ggplot2 object

See Also

residual

Other plot_pmx: distrib(), eta_cov(), eta_pairs(), individual(), plot_pmx(), plot_pmx.distrib(), plot_pmx.eta_cov(), plot_pmx.eta_pairs(), plot_pmx.individual(), plot_pmx.pmx_dens(), plot_pmx.pmx_gpar(), plot_pmx.pmx_param_history(), plot_pmx.pmx_qq()

Plot shrink in eta matric

Description

Plot shrink in eta matric

Usage

plot_shrink(x, shrink.dx, shrink)

Arguments

Value

ggplot2 object

Get plots description

Description

Get plots description

Usage

plots(ctr)

Arguments

Value

data.frame of plots

See Also

Other pmxclass: get_cats(), get_conts(), get_covariates(), get_data(), get_occ(), get_plot(), get_plot_config(), get_strats(), plot_names(), pmx_update(), set_data(), set_plot()

Create a pmx object

Description

Create a pmx object from a data source

Usage

pmx(
  config,
  sys = "mlx",
  directory,
  input,
  dv,
  dvid,
  cats = NULL,
  conts = NULL,
  occ = NULL,
  strats = NULL,
  settings = NULL,
  endpoint = NULL,
  sim = NULL,
  bloq = NULL,
  id = NULL,
  time = NULL,
  sim_blq = NULL
)

pmx_mlx(
  config,
  directory,
  input,
  dv,
  dvid,
  cats,
  conts,
  occ,
  strats,
  settings,
  endpoint,
  sim,
  bloq,
  id,
  time,
  sim_blq
)

pmx_mlxtran(
  file_name,
  config = "standing",
  call = FALSE,
  endpoint,
  version = -1,
  ...
)

Arguments

Details

pmx_mlx is a wrapper to mlx for the MONOLIX system ( sys="mlx")

pmx_mlxtran parses mlxtran file and guess pmx_mlx arguments. In case of multi endpoint the first endpoint is selected. You can though set the endpoint through the same argument. When you set call=TRUE,no controller is created but only the parameters parsed by mlxtran. This can be very helpful, in case you would like to customize parameters (adding settings vi pmx_settings, chnag eth edefault endpoint.)

Value

pmxClass controller object.

Examples

## Example to create the controller using theophylline data
theophylline <- file.path(system.file(package = "ggPMX"), "testdata",
                          "theophylline")
WORK_DIR <- file.path(theophylline, "Monolix")
input_file <- file.path(theophylline, "data_pk.csv")

## using only mandatory variables
ctr <- pmx(
  sys="mlx",
  config = "standing",
  directory = WORK_DIR,
  input = input_file,
  dv = "Y",
  dvid ="DVID"
)
## Using covariates
ctr <- pmx(
  sys="mlx",
  config = "standing",
  directory = WORK_DIR,
  input = input_file,
  dv = "Y",
  dvid ="DVID",
  cats=c("SEX"),
  conts=c("WT0","AGE0"),
  strats="STUD"
)
## using settings parameter
ctr <- pmx(
  sys="mlx",
  config = "standing",
  directory = WORK_DIR,
  input = input_file,
  dv = "Y",
  dvid ="DVID",
  settings=list(is.draft=FALSE)
)

## using mlxtran file
mlxtran_file <- 
  file.path(system.file(package = "ggPMX"), 
    "testdata","1_popPK_model","project.mlxtran")
pmx_mlxtran(mlxtran_file)

## mlxtran , call =TRUE to get the pmx_mlx argument parsed by  pmx_mlxtran
params <- pmx_mlxtran(mlxtran_file,call=TRUE)

str(params)
# $ directory: chr results_pathile 
# $ input    : chr observation file path
# $ dv       : chr "DV"
# $ cats     : chr [1:4] "SEX" "RACE" "DISE" "ILOW"
# $ conts    : chr [1:4] "AGE0" "WT0" "HT0" "TRT"
# $ occ      : chr "ISS"
# $ dvid     : chr "YTYPE"
# $ endpoint :List of 5
# ..$ code     : chr "1"
# ..$ label    : chr ""
# ..$ unit     : chr ""
# ..$ file.code: chr "1"
# ..$ trans    : NULL
# ..- attr(*, "class")= chr "pmxEndpointClass"
# $ config   : chr "standing"

This function can be used to set ggPMX options

Description

getPmxOption retrieves the value of a ggPMX option. ggPMXOptions sets the value of ggPMX options; it can also be used to return a list of all currently-set ggPMX options.

Usage

pmxOptions(...)

Arguments

Details

There is a global option set, which is available by default.

Value

list with options that were set by the user

Options used in ggPMX

• template_dir: path to template directory

Examples

## Not run: 
pmxOptions(template_dir = PATH_TO_CUSTOM_CONFIGURATION)

## End(Not run)

Creates BLOQ object attributes

Description

Creates BLOQ object attributes

Usage

pmx_bloq(
  cens = "CENS",
  limit = "LIMIT",
  colour = "pink",
  size = 2,
  linewidth = 1,
  alpha = 0.9,
  show = TRUE,
  ...
)

Arguments

Details

To define that a measurement is censored, the observation data set should include a CENSORING column ( default to 'CENS' ) and put 1 for lower limit or -1 for upper limit.
Optionally, data set can contain have a limit column ( default to 'LIMIT') column to set the other limit.

Combine tables

Description

Combine tables

Usage

pmx_combine_tables(x)

Arguments

Value

A list containing 'data' and 'index' of the combined table.

Compute Shrinkage

Description

Compute Shrinkage

Usage

pmx_comp_shrink(
  ctr,
  fun = c("var", "sd"),
  strat.facet,
  strat.color,
  filter,
  ...
)

Arguments

Value

data.table

This function can be used to define the pmx configuration used in plots. e.g. Monolix/Nonmem

Description

This function can be used to define the pmx configuration used in plots. e.g. Monolix/Nonmem

Usage

pmx_config(sys = "mlx", inputs, plots, ...)

Arguments

Details

To create a controller user can create a pmxConfig object using
- either an input template file
- or a plot template file
- or both.
By default the 'standing' configuration will be used.

Value

pmxConfig object

Examples

# *************** Create a controller using custom plot configuration ***************** ------

library(ggPMX)
theophylline <- file.path(
  system.file(package = "ggPMX"), "testdata",
  "theophylline"
)
WORK_DIR <- file.path(theophylline, "Monolix")
input_file <- file.path(theophylline, "data_pk.csv")


# create a controller with a custom plots template
ctr <-  pmx_mlx(
  config = pmx_config(
    plots=file.path( system.file(package = "ggPMX"),"examples/plots.yaml"),
    inputs = system.file(package = "ggPMX","examples/custom_inputs.yaml")
  ),
  directory = WORK_DIR,
  input = input_file,
  dv = "Y",
  dvid = "DVID",
  cats = c("SEX"),
  conts = c("WT0", "AGE0"),
  strats = "STUD"
)

## get the list of plots
ctr %>% plots
ctr %>% get_plot("custom_res_time")
ctr %>% get_plot("custom_npde_time")

Creates a deep copy of the controller

Description

Creates a deep copy of the controller

Usage

pmx_copy(ctr, keep_globals = FALSE, ...)

Arguments

Details

The controller is an 'R6' object, it behaves like a reference object. Some functions ( methods) can have a side effect on the controller and modify it internally. Technically speaking we talk about chaining not piping here. However , using pmx_copy user can work on a copy of the controller.

By default the copy does not keep global parameters set using pmx_settings.

Value

an object of pmxClass

Examples

ctr <- theophylline()
cctr <- ctr %>% pmx_copy()
## Any change in the ctr has no side effect in the ctr and vice versa

Select/Map covariates using human labels

Description

Select/Map covariates using human labels

Usage

pmx_cov(values, labels = NULL)

Arguments

Details

In case of 'pmx_plot_eta_cats' and 'pmx_plot_eta_conts' you can customize the covariates and covaraites labels using 'pmx_cov'.

Value

pmxCOVObject object

Creates a density plot object

Description

Creates a density plot object

Usage

pmx_dens(
  x,
  labels,
  dname = NULL,
  xlim = 3,
  var_line = NULL,
  snd_line = NULL,
  vline = NULL,
  is.legend = TRUE,
  ...
)

Arguments

Details

labels is a list that contains:

• title: plot title default "IWRES density plot"

• x: x axis label default to "Etas"

• y: y axis label default to empty

var_line is a list that contains:

• linetype: default to 1

• color: default to black

• linewidth: default to 1

snd_line is a list that contains:

• linetype: default to 2

• color: default to black

• linewidth: default to 1

vline is a list that contains:

• linetype: default to 3

• color: default to black

• linewidth: default to 1

Value

list with options for density plot object

Creates pmx endpoint object

Description

Creates pmx endpoint object

Usage

pmx_endpoint(code, label = "", unit = "", file.code = code, trans = NULL)

Arguments

Details

In case of multiple endpoints, pkpd case for example, we need to pass endpoint to the pmx call. Internally , ggPMX will filter the observations data set to keep only rows satisfying DVID==code. The code is also used to find the right predictions and or fingrid files. ggPMX use the configuration file to fine the path of the predictions file (like the single endpoint case) and then filter the right file using the code parameter.
For example:

• predictions{code}.txt for mlx16

• predictions{code}.txt and y{code}_residual for mlx18

For some tricky examples the code parameter is not enough to find the files. In that case the file.code parameter is used to distinguish the endpoint files.

Examples

## Use file.code parameter
pk_pd_path <- file.path(system.file(package = "ggPMX"), "testdata","pk_pd")

WORK_DIR <- file.path(pk_pd_path, "RESULTS")

ep <- pmx_endpoint(
  code="4",
  file.code="2"
)

input_file <- file.path(pk_pd_path, "pk_pd.csv")

ctr <- pmx_mlx(
  config = "standing",
  directory = WORK_DIR,
  input = input_file,
  dv = "dv",
  dvid = "dvid",
  cats = "sex",
  conts = "wt",
  endpoint = ep
 )

## using mlxtran 

ep <- pmx_endpoint(
  code="3",
 file.code="1"
)

mlxtran_file <- file.path(pk_pd_path, "pk_pd.mlxtran")
ctr <- pmx_mlxtran(mlxtran_file,endpoint=ep)

Generate clean file paths

Description

Wrapper around 'file.path' that cleans trailing forward slash and missing 'dir'.

Usage

pmx_file_path(dir, file)

Arguments

Value

A string or vector of string of the full file path.

filter data in a pmx controller

Description

filter data in a pmx controller

Usage

pmx_filter(
  ctr,
  data_set = c("estimates", "predictions", "eta", "finegrid", "shrink", "input",
    "individual", "sim_blq"),
  pmx_exp
)

Arguments

Value

Returns a pmx controller with a filtered data set.

Examples

## example of global filter
ctr <- theophylline()
ctr %>% pmx_filter(data_set = "prediction", ID == 5 & TIME < 2)
ctr %>% get_data("prediction")

Get List of built-in configurations

Description

Get List of built-in configurations

Usage

pmx_get_configs(sys = "mlx")

Arguments

Value

names of the config

Examples

pmx_get_configs()

Get file extension

Description

Extract file extension from the filename string.

Usage

pmx_get_extension(x, dot = TRUE)

Arguments

Value

A string or vector of string of the file(s) extension.

Handling pmx Graphical parameters

Description

Handling pmx Graphical parameters

Usage

pmx_gpar(
  is.title,
  labels,
  axis.title,
  which_pages,
  print,
  axis.text,
  ranges,
  is.smooth,
  smooth,
  is.band,
  band,
  is.draft,
  draft,
  discrete,
  is.identity_line,
  identity_line,
  smooth_with_bloq,
  scale_x_log10,
  scale_y_log10,
  color.scales,
  is.legend,
  legend.position
)

Arguments

Details

This object contains all general graphic settings. It used internally by all pmx_plot(generic function) to set the default behavior.

Value

An object of class "pmx_gpar".

Index table columns

Description

Index table columns

Usage

pmx_index_table(x)

Arguments

Value

A tibble of the index.

Test for nm_table_list class

Description

Reports whether x is a 'nm_table_list' object

Usage

pmx_is.nm.table.list(x)

Arguments

Value

Logical value, 'TRUE' for 'nm_table_list' class and 'FALSE' otherwise.

List NONMEM output tables

Description

List NONMEM output tables file names from a nm_model object.

Usage

pmx_list_nm_tables(nm_model = NULL)

Arguments

See Also

pmx_read_nm_model, pmx_read_nm_tables

Examples

## Not run: 
pmx_read_nm_model(file = 'run001.lst') %>% 
  pmx_list_nm_tables()

## End(Not run)

Creates an nm_table_list from manually defined table name patterns

Description

Creates an nm_table_list from manually defined table name patterns

Usage

pmx_list_nm_tables_manual(runno = NULL, file = NULL, dir = NULL, tab_list)

Arguments

Value

A 'nm_table_list'

Generate extension string

Description

Generate consistent extension strings by adding dot prefix whenever necessary.

Usage

pmx_make_extension(x)

Arguments

Value

A string or vector of strings of extension(s).

Manually define nonmem tables to be imported

Description

Manually provide names of the table files to be imported.

Usage

pmx_manual_nm_import(
  tab_names = c("sdtab", "mutab", "patab", "catab", "cotab", "mytab", "extra", "xptab",
    "cwtab"),
  tab_suffix = "",
  sim_suffix = "sim"
)

Arguments

Merge firstonly table with full length tables

Description

Merge firstonly table with full length tables

Usage

pmx_merge_firstonly(x, quiet)

Arguments

Value

A list containing 'data' and 'index' of the merged table.

Message function

Description

Message function with quiet option inspired from 'ronkeizer/vpc'.

Usage

pmx_msg(txt, quiet = TRUE)

Arguments

Value

Silent when quiet is 'TRUE' or a message is quiet is 'FALSE'.

Creates pmx controller from an nlimxr fit object

Description

Creates pmx controller from an nlimxr fit object

Usage

pmx_nlmixr(fit, dvid, conts, cats, strats, endpoint, settings, vpc = FALSE)

Arguments

Value

pmxClass controller object.

Creates pmx controller from NONMEM model outputs

Description

Creates pmx controller from NONMEM model outputs

Usage

pmx_nm(
  file = NULL,
  directory = ".",
  runno = NULL,
  ext = ".lst",
  table_suffix = "",
  sim_suffix = "sim",
  simfile = NULL,
  prefix = "run",
  table_names = c("sdtab", "mutab", "patab", "catab", "cotab", "mytab", "extra", "xptab",
    "cwtab"),
  dvid = "DVID",
  pred = "PRED",
  time = "TIME",
  dv = "DV",
  conts,
  cats,
  npde,
  iwres,
  ipred,
  endpoint,
  strats = "",
  settings = pmx_settings(),
  vpc = TRUE,
  bloq = NULL,
  obs = FALSE,
  quiet = FALSE
)

Arguments

Value

pmxClass controller object.

Author(s)

The ggPMX NONMEM reader (pmx_nm) is strongly based on NONMEM reading functions of the xpose package (v.0.4.11) (Thanks to Benjamin Guiastrennec) To avoid conflicts with the xpose package, the necessary xpose-based functions have been renamed with a "pmx_" prefix. If the user wants to use individual functions e.g. "read_nm_tables" please use the xpose-package

Examples

## using only runnumber 
# ctr <- pmx_nm(
#  directory=model_dir,
#  runno = "001" 
#)

## using a model file (e.g. run001.lst)
#ctr <- pmx_nm(
#  directory=model_dir,
#  file = "run001.lst" 
#)

## if simulation was performed post-hoc, an additional simulation file can be loaded for VPC
#ctr <- pmx_nm(
#  directory=model_dir,
#  file = "run001.lst", 
#  simfile = "simulation.ctl"
#)

## loading with individual table(s)-names
#ctr <- pmx_nm(directory = model_dir,
#              runno = 3, 
#              table_names = "xptab")

Parse NONMEM output files

Description

Function parsing NONMEM output files from their raw input.

Usage

pmx_parse_nm_files(dat, quiet)

Arguments

Value

A tibble containing the parsed 'data' along with 'name', 'problem', 'subprob', and 'method'.

Generic pmx plot

Description

Generic pmx plot

Usage

pmx_plot(ctr, pname, ...)

Arguments

Value

ggplot2 object

Generic pmx stratified plot

Description

Generic pmx stratified plot

Usage

pmx_plot_cats(ctr, pname, cats, chunk = "", print = TRUE, ...)

Arguments

Value

ggplot2 object(s)

Eta matrix plot

Description

Eta matrix plot

Usage

pmx_plot_eta_matrix(
  ctr,
  title,
  dname,
  type.eta,
  text_color,
  is.shrink,
  shrink,
  point,
  is.smooth,
  smooth,
  is.hline,
  hline,
  is.vreference_line,
  vreference_line,
  filter,
  strat.facet,
  facets,
  strat.color,
  trans,
  pmxgpar,
  labels,
  axis.title,
  axis.text,
  ranges,
  is.band,
  band,
  is.draft,
  draft,
  is.identity_line,
  identity_line,
  scale_x_log10,
  scale_y_log10,
  color.scales,
  ...
)

Arguments

Value

ggplot2 object

Examples

# basic use  ---------------------------------------

ctr <- theophylline()
p <- ctr %>% pmx_plot_eta_matrix

# update graphical parameter  ----------------------

## update labels
ctr %>% pmx_plot_eta_matrix(
  labels = list(title = "Eta matrix new title")
)

## remove draft
ctr %>% pmx_plot_eta_matrix(is.draft = FALSE)

## change text color line
ctr %>% pmx_plot_eta_matrix(
  text_color="red",
  shrink=pmx_shrink(mapping=aes(color="magenta"))
  )

## custom point aes and static parameters
## we can customize any geom_point parameter
ctr %>% pmx_plot_eta_matrix(
  point = list(color = "blue", shape = 4)
)



# stratification  ----------------------------------

## IGNORE continuous stratification
ctr %>% pmx_plot_eta_matrix(strat.color = "WT0")
## IGNORE  categorical stratification
ctr %>% pmx_plot_eta_matrix(strat.facet = ~SEX)

# subsetting  --------------------------------------

## we can use any expression involving the data
ctr %>% pmx_plot_eta_matrix(filter = EFFECT%in%  c("Cl","ka"))

Individual plot

Description

Individual plot

Usage

pmx_plot_individual(
  ctr,
  which_pages = 1L,
  print = FALSE,
  dname,
  pred_line,
  ipred_line,
  point,
  is.legend,
  use.finegrid,
  bloq,
  filter,
  strat.facet,
  facets,
  strat.color,
  trans,
  pmxgpar,
  labels,
  axis.title,
  axis.text,
  ranges,
  is.smooth,
  smooth,
  is.band,
  band,
  is.draft,
  draft,
  is.identity_line,
  identity_line,
  scale_x_log10,
  scale_y_log10,
  color.scales,
  ...
)

Arguments

Value

ggplot2 or list of ggplot2 objects

Examples

# basic use  ---------------------------------------

ctr <- theophylline()
ctr %>% pmx_plot_individual(which_pages = 1)
## multiple pages
ctr %>% pmx_plot_individual(which_pages = c(1, 3))
## change faceting
ctr %>% pmx_plot_individual(facets = list(nrow = 5, ncol = 5), which_pages = 2)


# update graphical parameter  ----------------------

## update labels
ctr %>% pmx_plot_individual(
  labels = list(title = "Custom individual plot")
)

## remove draft
ctr %>% pmx_plot_individual(is.draft = FALSE)

## Customize ipred_line with any geom_line parameter
ctr %>% pmx_plot_individual(
  pred_line = list(color = "red", linetype = 20, alpha = 0.5)
)

## Customize ipred_line with any geom_line parameter
ctr %>% pmx_plot_individual(
  ipred_line = list(size = 5)
)


## Customize any geom_point parameter
ctr %>% pmx_plot_individual(
  point = list(aes(alpha = DV), color = "green", shape = 4)
)


## legend

p <- ctr %>% pmx_plot_individual(
  is.legend=TRUE,
  point=list(shape=20),
  pred_line=list(linetype=6)
)

# # stratification  ----------------------------------
#
# ## continuous stratification
ctr %>% pmx_plot_individual(strat.color = "WT0")

# # subsetting  --------------------------------------
#
# ## we can use any expression involving the data
# ## filter and stratify
ctr %>% pmx_plot_individual(
  filter = SEX == 1, strat.facet = ~SEX, 
  facets = list(nrow = 5, ncol = 5))

# # transformation  --------------------------------------
#
# ## apply a log transformation in y
ctr %>% pmx_plot_individual(trans = "log10_y")
# ## apply a custonm trsnformation to normalize axis between 0 and 1

## get a list of parameter
p <- ctr %>% pmx_plot_individual(
  which_pages="all",
  point=list(shape=4,color='blue',size=10),
  facets = list(nrow = 5, ncol = 5),
  labels = list(title = "My individuals",x='my time',y='PD data')
) 

IWRES density plot

Description

IWRES density plot

Usage

pmx_plot_iwres_dens(
  ctr,
  sim_blq,
  dname,
  xlim,
  var_line,
  snd_line,
  vline,
  filter,
  strat.facet,
  facets,
  strat.color,
  trans,
  pmxgpar,
  labels,
  axis.title,
  axis.text,
  ranges,
  is.smooth,
  smooth,
  is.band,
  band,
  is.draft,
  draft,
  is.identity_line,
  identity_line,
  scale_x_log10,
  scale_y_log10,
  color.scales,
  ...
)

Arguments

Value

ggplot2 or list of ggplot2 objects

SAEM Convergence Plot

Description

This plot displays the sequence of estimates for population parameters computed after each iteration of the SAEM algorithm. The purpose is to check the convergence of the algorithm. In addition, a convergence indicator gives the estimation for -2 x log-likelihood along the iterations.

Usage

pmx_plot_saem_convergence(ctr, ...)

Arguments

Value

A ggplot object showing SAEM convergence plot.

VPC plot

Description

VPC plot

Usage

pmx_plot_vpc(
  ctr,
  type,
  idv,
  obs,
  pi,
  ci,
  rug,
  bin,
  is.legend,
  sim_blq,
  dname,
  filter,
  strat.facet,
  facets,
  strat.color,
  trans,
  pmxgpar,
  labels,
  axis.title,
  axis.text,
  ranges,
  is.smooth,
  smooth,
  is.band,
  band,
  is.draft,
  draft,
  is.identity_line,
  identity_line,
  scale_x_log10,
  scale_y_log10,
  color.scales,
  is.footnote,
  ...
)

Arguments

Details

You can use pmx_vpc_bin to set the bin parameters. In case of stratification, binning can be different for each strat level (case within_strat equal to FALSE).

Value

ggplot2 or list of ggplot2 objects

See Also

Other vpc: pmx_vpc(), pmx_vpc_bin(), pmx_vpc_ci(), pmx_vpc_obs(), pmx_vpc_pi(), pmx_vpc_rug()

Examples

library(ggPMX)

theo_path <- file.path(
  system.file(package = "ggPMX"), "testdata",
  "theophylline"
)
WORK_DIR <- file.path(theo_path, "Monolix")
input_file <- file.path(theo_path, "data_pk.csv")
vpc_file <- file.path(theo_path, "sim.csv")

ctr <- pmx_mlx(
  config = "standing",
  directory = WORK_DIR,
  input = input_file,
  dv = "Y",
  dvid = "dvid",
  cats = c("SEX"),
  conts = c("WT0", "AGE0"),
  strats = "STUD",
  settings = pmx_settings(
    use.labels=TRUE,
    cats.labels=list(
      SEX=c("0"="Male","1"="Female")
    )
  ),
  sim = pmx_sim(
    file = vpc_file,
    irun ="rep",
    idv="TIME"
  )
)


ctr %>% pmx_plot_vpc(
  strat.facet=~SEX,
  facets=list(nrow=2),
  type="percentile",
  is.draft = FALSE,
  pi = pmx_vpc_pi(interval = c(0.1,0.9),
              median=list(color="green"),
              extreme= list(color="green")),
  obs = pmx_vpc_obs(color="blue",shape=18,size=2),
  ci = pmx_vpc_ci(interval = c(0.1,0.9),
              median=list(fill="pink")),
  bin=pmx_vpc_bin("kmeans",n=5)
)

ctr %>% 
  pmx_plot_vpc(bin= pmx_vpc_bin(
     style = "fixed",
     fixedBreaks=c(-10,2, 5, 10,15,50))
  )

# example with legend 

ctr %>% pmx_plot_vpc(
  is.legend = TRUE,
  pi = pmx_vpc_pi(interval=c(0.02,0.98),median = list(linetype="dotted")),
  ci = pmx_vpc_ci(interval = c(0.05,0.95),median=list(fill="red"))
)

This function creates a qq plot object

Description

This function creates a qq plot object

Usage

pmx_qq(
  x,
  labels,
  dname = NULL,
  point = NULL,
  xmax = TRUE,
  facets = NULL,
  is.reference_line = NULL,
  reference_line = NULL,
  is.shrink = NULL,
  shrink = NULL,
  is.hline = NULL,
  hline = NULL,
  is.vline = NULL,
  vline = NULL,
  ...
)

Arguments

Details

labels is a list that contains:

• title: plot title default "EBE vs. covariates"

• x: x axis label default to "Etas"

• y: y axis label default to empty

point is a list that contains:

• shape: default to 1

• color: default to black

• size: default to 1

Value

pmx_qq object

Quantile-quantile plots

Description

Quantile-quantile plots

Quantile-quantile plot of IWRES

Quantile-quantile plot of eta variables

Quantile-quantile plot of NPDE

Quantile-quantile plot of NPD

Quantile-quantile plot of CWRES

Usage

pmx_qq_plot(
  dname,
  point,
  is.reference_line,
  reference_line,
  is.shrink,
  shrink,
  is.hline,
  hline,
  is.vline,
  vline,
  filter,
  strat.facet,
  facets,
  strat.color,
  trans,
  pmxgpar,
  labels,
  axis.title,
  axis.text,
  ranges,
  is.smooth,
  smooth,
  is.band,
  band,
  is.draft,
  draft,
  is.identity_line,
  identity_line,
  scale_x_log10,
  scale_y_log10,
  color.scales,
  ...
)

pmx_plot_iwres_qq(ctr, ...)

pmx_plot_eta_qq(ctr, ...)

pmx_plot_npde_qq(ctr, ...)

pmx_plot_npd_qq(ctr, ...)

pmx_plot_cwres_qq(ctr, ...)

Arguments

Value

ggplot2 object

Examples

# *************** basic use ***************** ------

ctr <- theophylline()
ctr %>% pmx_plot_eta_qq
ctr %>% pmx_plot_npde_qq
ctr %>% pmx_plot_iwres_qq

# update graphical parameter  ----------------------

## add reference line
ctr %>% pmx_plot_npde_qq(reference_line=list(color="blue"))

## remove reference line
ctr %>% pmx_plot_eta_qq(reference_line=NULL)


# stratification  ----------------------------------

## categorical stratification color parameter
ctr %>% pmx_plot_iwres_qq(strat.facet=~STUD,strat.color="SEX")
## categorical stratification facetting
ctr %>% pmx_plot_eta_qq(strat.facet = ~SEX)

## do not use symmetric axis
ctr %>% pmx_plot_npde_qq(xmax=FALSE,reference_line=list())

Convert raw strings to tibble

Description

Convert raw data strings to a tibble format.

Usage

pmx_raw_to_tibble(x, sep, file)

Arguments

Value

A tibble.

Define data import arguments

Description

Define data import arguments

Usage

pmx_read_args(
  x,
  quiet,
  col_types = readr::cols(.default = "d"),
  na = "NA",
  comment = "TABLE",
  skip = 1,
  ...
)

Arguments

Value

A list of 2 levels fun (the import function) and params (a list of arguments to be used when calling fun).

Define data import functions

Description

Define data import functions

Usage

pmx_read_funs(fun)

Arguments

Value

A data import function.

NONMEM output file import function

Description

Quickly import NONMEM output files into R.

Usage

pmx_read_nm_files(
  runno = NULL,
  prefix = "run",
  ext = c(".ext", ".cor", ".cov", ".phi", ".grd", ".shk"),
  file = NULL,
  dir = NULL,
  quiet = FALSE
)

Arguments

Examples

## Not run: 
# Using the `file` argument to import a model file:
ext_file <- pmx_read_nm_files(file = 'run001.ext', dir = 'models')

# Using the `runno` argument to import a model file:
ext_file <- pmx_read_nm_files(runno = '001', ext = '.ext', dir = 'models')

## End(Not run)

NONMEM model file parser

Description

Parse NONMEM model files in R format

Usage

pmx_read_nm_model(
  runno = NULL,
  prefix = "run",
  ext = ".lst",
  file = NULL,
  dir = NULL
)

Arguments

Value

A tibble of class model containing the following columns:

• problem: a numeric identifier for the $PROBLEM associated with the code.

• level: a unique numeric identifier to each subroutine block associated with the code.

• subroutine: a character identifier named after the 3 first letters of the subroutine name e.g. '$THETA' and '$TABLE' will become 'the' and 'tab' respectively. In addition all output from the .lst is labeled 'lst', the general nonmem output e.g. NM-TRAN messages are labelled 'oth'. With priors thp, tpv, omp, opd, sip, spd abbreviations are given to the THETAP, THETAPV, OMEGAP, etc.

• code: the code without comments or subroutine names e.g. '$THETA 0.5 ; TVCL' will return '0.5'.

• comment: the last comment of a record e.g. '0.5 ; Clearance (L/h) ; TVCL' will return 'TVCL'.

See Also

pmx_read_nm_tables

Examples

## Not run: 
# Using the `file` argument to import a model file:
nm_model <- pmx_read_nm_model(file = 'run001.lst', dir = 'models')

# Using the `runno` argument to import a model file:
nm_model <- pmx_read_nm_model(runno = '001', ext = '.lst', dir = 'models')

## End(Not run)

NONMEM output table import function

Description

Quickly import NONMEM output tables into R. This function automatically detects the optimal settings to import the tables from nonmem. It is based on the read_nm_tables function of xpose. Slight adjustment were made for purposes of pmx_nm()

Usage

pmx_read_nm_tables(
  file = NULL,
  dir = NULL,
  combined = TRUE,
  rm_duplicates = TRUE,
  quiet = FALSE,
  simtab = NULL,
  ziptab = TRUE,
  user_mode = TRUE,
  ...
)

Arguments

Table format requirement

When using pmx_read_nm_tables with the combined argument set to FALSE an ID column must be present in all data tables. When combined is set to TRUE instead an ID column must be present in at least one table for each problem and for each 'firstonly' category. ID columns are required to properly combine/merge tables and removing NA records. If the ID column is missing from a table and combined = FALSE pmx_read_nm_tables will return the following warning: Unknown variables: `ID`. While the data is returned beware that NA records might be left in the data and the output should be checked carefully. If combined = TRUE pmx_read_nm_tables is more strict and will return the following warning instead: Dropped `<tablenames>` due to missing required `ID` column..

Examples

## Not run: 

# Adjustment to the original code: usermode is set to "usermode = TRUE" 
# in order to improve this function for purposes of pmx_nm() (nonmem_reader.R)
# In order to use this function seperatly, the use of the original function in 
# the xpose package is advised.

# Import tables manually and return them as a list of individual tables
nm_tables <- pmx_read_nm_tables(file = c('sdtab001', 'patab001'), 
                            dir = 'models', combined = FALSE)

# Import tables manually and return them as a single merged table
nm_tables <- pmx_read_nm_tables(file = c('sdtab001', 'patab001'), 
                            dir = 'models', combined = TRUE)


## End(Not run)

Register plot (Jun2025, Alex: I believe it doesn't work at all)

Description

Register plot (Jun2025, Alex: I believe it doesn't work at all)

Usage

pmx_register_plot(ctr, pp, pname = NULL)

Arguments

Generates ggpmX report from a pre-defined template

Description

Generates ggpmX report from a pre-defined template

Usage

pmx_report(
  contr,
  name,
  save_dir,
  plots_subdir = "ggpmx_GOF",
  output = c("all", "plots", "report"),
  template = "standing",
  footnote = output == "all",
  edit = FALSE,
  format = NULL,
  title,
  ...
)

Arguments

Details

pmx_report uses pre-defined template .Rmd to generate the report. The idea is to pass the controller as a report argument using knitr params artifact.

Examples

library(ggPMX)
# you probably want to create the report in your own directory
# But using a temp directory allows for easy cleanup

## case1: generate a single report
withr::with_tempdir({
  ctr <- theophylline()
  ctr %>% pmx_report(
    name = "my_report",
    save_dir = getwd(),
    output="report"
  )
})


## case2: generate standalone plots
withr::with_tempdir({
  ctr <- theophylline()
  ctr %>% pmx_report(
    name = "my_report",
    save_dir = getwd(),
    output="plots"
  )
})


## case3: generate both : reports + plots
## by default add footnote
## Note, you can force footnote to FALSE using footnote parameter
withr::with_tempdir({
  ctr <- theophylline()
  ctr %>% pmx_report(
    name="my_report",
    save_dir=getwd(),
    output="all"
  )
})


## case4 : generate standalone plots with footnotes
withr::with_tempdir({
  ctr <- theophylline()
  ctr %>% pmx_report(
    name="my_report",
    save_dir=getwd(),
    footnote=TRUE,
    output="plots"
  )
})


##  case6: dynamic edit
## uncomment to run
# ctr <- theophylline()
# ctr %>% pmx_report(
#   save_dir = file.path(getwd(),"case6"),
#   name="my_report",
#   output="report",
#   edit = TRUE)


## case7 : generate individual plots report
## ctr <- theophylline()
## ctr %>% pmx_report(
##   name="report2",
##   save_dir = getwd(),
##   template="individual",
##   format="all",
##   which_pages=1:2
## )

Gets build-in report templates

Description

Gets build-in report templates

Usage

pmx_report_template()

Value

list of templates names

Examples

pmx_report_template()

Create controller global settings

Description

Create controller global settings

Usage

pmx_settings(
  is.draft = TRUE,
  use.abbrev = TRUE,
  color.scales = NULL,
  cats.labels = NULL,
  use.labels = FALSE,
  use.titles = FALSE,
  effects = NULL,
  ...
)

Arguments

Value

pmxSettingsClass object

Examples

library(ggPMX)
library(ggplot2)
ctr <- theophylline(
  settings=
    pmx_settings(
      color.scales=list(
        "Study",
        labels=c("Study 1","Study 2"),
        values=c("1"="lightyellow","2"="lightblue")),
      cats.labels=list(
        SEX=c("0"="M","1"="F"),
        STUD=c("1"="Study 1","2"="Study 2")
      ),
      use.abbrev=TRUE,
      is.draft=TRUE,
      use.labels=TRUE
    )
)



ctr %>% 
  pmx_plot_npde_time(strat.color="STUD",strat.facet=~SEX)
# 
# 
ctr  %>%
  pmx_plot_eta_box(strat.color="STUD", strat.facet =~SEX)

ctr  %>%  pmx_plot_eta_hist

Create shrinkage parameter object

Description

Create shrinkage parameter object

Usage

pmx_shrink(
  fun = c("var", "sd"),
  size = 1,
  color = "green",
  vjust = 1.5,
  hjust = 0.5,
  ...
)

Arguments

Value

pmxShrinkClass object (list)

Create simulation object

Description

Create simulation object

Usage

pmx_sim(file, data, irun, idv)

Arguments

Examples

library(ggPMX)

theo_path <- file.path(
  system.file(package = "ggPMX"), "testdata",
  "theophylline"
)
WORK_DIR <- file.path(theo_path, "Monolix")
input_file <- file.path(theo_path, "data_pk.csv")
vpc_file <- file.path(theo_path, "sim.csv")

ctr <- pmx_mlx(
  config = "standing",
  directory = WORK_DIR,
  input = input_file,
  dv = "Y",
  dvid = "dvid",
  cats = c("SEX"),
  conts = c("WT0", "AGE0"),
  strats = "STUD",
  settings = pmx_settings(
    use.labels=TRUE,
    cats.labels=list(
      SEX=c("0"="Male","1"="Female")
    )
  ),
  sim = pmx_sim(
    file = vpc_file,
    irun ="rep",
    idv="TIME"
  )
)


ctr %>% pmx_plot_vpc(
  strat.facet=~SEX,
  facets=list(nrow=2),
  type="percentile",
  is.draft = FALSE,
  pi = pmx_vpc_pi(interval = c(0.1,0.9),
              median=list(color="green"),
              extreme= list(color="green")),
  obs = pmx_vpc_obs(color="blue",shape=18,size=2),
  ci = pmx_vpc_ci(interval = c(0.1,0.9),
              median=list(fill="pink")),
  bin=pmx_vpc_bin("kmeans",n=5)
)

ctr %>% 
  pmx_plot_vpc(bin= pmx_vpc_bin(
     style = "fixed",
     fixedBreaks=c(-10,2, 5, 10,15,50))
  )

# example with legend 

ctr %>% pmx_plot_vpc(
  is.legend = TRUE,
  pi = pmx_vpc_pi(interval=c(0.02,0.98),median = list(linetype="dotted")),
  ci = pmx_vpc_ci(interval = c(0.05,0.95),median=list(fill="red"))
)

Define ggPMX theme

Description

This theme is a simple wrapper gdoc theme from ggthems package.

Usage

pmx_theme(...)

Arguments

Value

ggplot2 theme object

Check tidyr version

Description

Check the version of tidyr to handle the gap between v0.8.3 and v1.0.0

Usage

pmx_tidyr_new_interface()

Value

the package version of tidyr

Update plot object

Description

Update plot object

Usage

pmx_update(
  ctr,
  pname,
  strat.color = NULL,
  strat.facet = NULL,
  color.scales = NULL,
  filter = NULL,
  trans = NULL,
  ...,
  pmxgpar = NULL
)

Arguments

Details

trans is a transformation that user can apply to x, or y coordinates. The transformation is applied to the data before the plotting. This gives more felxiblilty to the user and also conserves all static positions like annotations ( draft specially)

For example:

var_x apply variance to x coordinates the variance function

var_xy apply variance to both This mechanism is applied internally to scale log.

Value

controller object with the plot updated

See Also

Other pmxclass: get_cats(), get_conts(), get_covariates(), get_data(), get_occ(), get_plot(), get_plot_config(), get_strats(), plot_names(), plots(), set_data(), set_plot()

Update file extension

Description

Change the extension of a file.

Usage

pmx_update_extension(x, ext)

Arguments

Value

A string or vector of strings of file name(s).

Creates vpc object

Description

Creates vpc object

Usage

pmx_vpc(
  type = c("percentile", "scatter"),
  idv = "TIME",
  obs = pmx_vpc_obs(),
  pi = pmx_vpc_pi(),
  ci = pmx_vpc_ci(),
  rug = pmx_vpc_rug(),
  bin = pmx_vpc_bin(),
  labels = NULL,
  facets = NULL,
  is.legend = TRUE,
  is.footnote = TRUE,
  dname = NULL,
  ...
)

Arguments

Value

list with parameters of the vpc object

See Also

Other vpc: pmx_plot_vpc(), pmx_vpc_bin(), pmx_vpc_ci(), pmx_vpc_obs(), pmx_vpc_pi(), pmx_vpc_rug()

Creates vpc bins

Description

Creates vpc bins

Usage

pmx_vpc_bin(style, within_strat = TRUE, seed = 42, ...)

Arguments

Details

This is a wrapper to the bin based VPC

Value

list with options for 'pmx_vpc_bin' object

See Also

Other vpc: pmx_plot_vpc(), pmx_vpc(), pmx_vpc_ci(), pmx_vpc_obs(), pmx_vpc_pi(), pmx_vpc_rug()

Sets vpc confidence interval layer

Description

Sets vpc confidence interval layer

Usage

pmx_vpc_ci(
  show = c("all", "median"),
  interval = c(0.025, 0.975),
  method = c("ribbon", "rectangle"),
  median = list(fill = "red", alpha = 0.3),
  extreme = list(fill = "#3388cc", alpha = 0.3)
)

Arguments

Value

list with options for Confidence Interval layer

See Also

Other vpc: pmx_plot_vpc(), pmx_vpc(), pmx_vpc_bin(), pmx_vpc_obs(), pmx_vpc_pi(), pmx_vpc_rug()

Sets vpc observation layer

Description

Sets vpc observation layer

Usage

pmx_vpc_obs(show = TRUE, color = "#000000", size = 1, alpha = 0.7, shape = 1)

Arguments

Value

list with options for ggplot2 layer with observations

See Also

Other vpc: pmx_plot_vpc(), pmx_vpc(), pmx_vpc_bin(), pmx_vpc_ci(), pmx_vpc_pi(), pmx_vpc_rug()

Sets vpc percentile layer

Description

Sets vpc percentile layer

Usage

pmx_vpc_pi(
  show = c("all", "median", "area"),
  interval = c(0.05, 0.95),
  median = list(color = "#000000", linewidth = 1, alpha = 0.7, linetype = "solid"),
  extreme = list(color = "#000000", linewidth = 1, alpha = 0.7, linetype = "dashed"),
  area = list(fill = "blue", alpha = 0.1)
)

Arguments

Value

list with options for Prediction Interval layer

See Also

Other vpc: pmx_plot_vpc(), pmx_vpc(), pmx_vpc_bin(), pmx_vpc_ci(), pmx_vpc_obs(), pmx_vpc_rug()

Sets vpc rug layer

Description

Sets vpc rug layer

Usage

pmx_vpc_rug(show = TRUE, color = "#000000", linewidth = 1, alpha = 0.7, size)

Arguments

Details

When the vpc confidence interval layer method is rectangles we don't show rug separators.

Value

list with options for the rug layer

See Also

Other vpc: pmx_plot_vpc(), pmx_vpc(), pmx_vpc_bin(), pmx_vpc_ci(), pmx_vpc_obs(), pmx_vpc_pi()

S3 print abbreviation

Description

S3 print abbreviation

Usage

## S3 method for class 'abbreviation'
print(x, ...)

Arguments

Value

print abbreviation

This function can be used to print configuration of the defined object using S3 method.

Description

This function can be used to print configuration of the defined object using S3 method.

Usage

## S3 method for class 'configs'
print(x, ...)

Arguments

Value

print result

Print pmxClass object

Description

Print pmxClass object

Usage

## S3 method for class 'pmxClass'
print(x, ...)

Arguments

Value

print object to screen

S3 method print pmxConfig object

Description

S3 method print pmxConfig object

Usage

## S3 method for class 'pmxConfig'
print(x, ...)

Arguments

Value

invisible object

Print pmx_gpar object

Description

Print pmx_gpar object

Usage

## S3 method for class 'pmx_gpar'
print(x, ...)

Arguments

Value

a character description of graphical parameters

Reads .ext files generated by NONMEM

Description

Reads .ext files generated by NONMEM

Usage

read_extfile(
  run = NA_real_,
  project = getwd(),
  file = paste0(run, ".ext"),
  path = NULL,
  read_fun = c("data.table", "read.table"),
  quiet
)

Arguments

Value

A list with param, omega, and sigma in a format ready to be used.

Author(s)

This function is based on read_nmext from mrgsolve, Original Author: Kyle T Baron. This function has some changes to the original code: Addition of param, "quiet", (option of pmx_msg function, from xpose package) (Line: 27) The code was slightly adjusted to check for multiple tables and also extract SE (ITERATION == 1000000001) (Line: 44-58, Line: 86-96, respectively) The output was also slightly adjusted to fit ggPMX output (df and df2) (Line: 105,106) as_bmat was replaced by bmat_like to create the diagonal matrix (Line 116:142)

Examples

#project <- system.file("nonmem", package = "mrgsolve")
#est <- read_nmext(1005, project = project)

Read Modelling input data

Description

Read Modelling input data

Usage

read_input(
  ipath,
  dv,
  dvid,
  cats = "",
  conts = "",
  strats = "",
  occ = "",
  endpoint = NULL,
  id = NULL,
  time = NULL
)

Arguments

Value

data.table well formatted containing modelling input data

Read MONOLIX individual parameters

Description

Read MONOLIX individual parameters

Usage

read_mlx_ind_est(path, x, ...)

Arguments

Value

data.table object

Read MONOLIX parameter estimation file

Description

Read MONOLIX parameter estimation file

Usage

read_mlx_par_est(path, x, ...)

Arguments

Value

data.table object

Read MONOLIX model predictions

Description

Read MONOLIX model predictions

Usage

read_mlx_pred(path, x, ...)

Arguments

Value

data.table object

Read MONOLIX SAEM convergence file

Description

Read MONOLIX SAEM convergence file

Usage

read_mlx_saem_conv(path, ...)

Arguments

Value

data.table object

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

magrittr

%>%

This function create a residual for each observed value and also generates a residual distribution

Description

This function create a residual for each observed value and also generates a residual distribution

Usage

residual(
  x,
  y,
  labels = NULL,
  point = NULL,
  is.hline = FALSE,
  hline = NULL,
  dname = NULL,
  facets = NULL,
  bloq = NULL,
  square_plot = TRUE,
  ...
)

Arguments

Details

Some parameters are a list of parameters :

point is a list that contains:

• shape: default to 1

• color: default to black

• size: default to 1

labels is a list that contains:

• title: plot title default to AES_X versus AES_Y

• subtitle: plot subtitle default empty

• x: x axis label default to AES_X

• y: y axis label default to AES_Y

Value

a residual object

See Also

plot_pmx.residual

Scatter residual plots

Description

Scatter residual plots

DV vs PRED plot

DV vs IPRED plot

IWRES vs IPRED plot

|IWRES| vs IPRED plot

|IWRES| vs TIME plot

IWRES vs TIME plot

NPDE vs TIME plot

NPDE vs PRED plot

NPD vs TIME plot

NPD vs EPRED plot

NPD vs PRED plot

CWRES vs TIME plot

CWRES vs CPRED plot

CWRES vs PRED plot

Usage

residual_scatter(
  sim_blq,
  point,
  is.hline,
  hline,
  dname,
  bloq,
  filter,
  strat.facet,
  facets,
  strat.color,
  trans,
  pmxgpar,
  labels,
  axis.title,
  axis.text,
  ranges,
  is.smooth,
  smooth,
  is.band,
  band,
  is.draft,
  draft,
  is.identity_line,
  identity_line,
  scale_x_log10,
  scale_y_log10,
  color.scales,
  ...
)

pmx_plot_dv_pred(ctr, ...)

pmx_plot_dv_ipred(ctr, ...)

pmx_plot_iwres_ipred(ctr, ...)

pmx_plot_abs_iwres_ipred(ctr, ...)

pmx_plot_abs_iwres_time(ctr, ...)

pmx_plot_iwres_time(ctr, ...)

pmx_plot_npde_time(ctr, ...)

pmx_plot_npde_pred(ctr, ...)

pmx_plot_npd_time(ctr, ...)

pmx_plot_npd_epred(ctr, ...)

pmx_plot_npd_pred(ctr, ...)

pmx_plot_cwres_time(ctr, ...)

pmx_plot_cwres_cpred(ctr, ...)

pmx_plot_cwres_pred(ctr, ...)

Arguments

Value

ggplot2 object

Examples

# NOTES ######################
# examples are availables for all residual plots:
# - pmx_plot_abs_iwres_ipred
# - pmx_plot_dv_ipred
# - pmx_plot_dv_pred
# - pmx_plot_iwres_ipred
# - pmx_plot_iwres_time
# - pmx_plot_npde_time


# basic use  ---------------------------------------

ctr <- theophylline()
p <- ctr %>% pmx_plot_dv_pred()
## p is a ggplot2 object you can add any layer here
p + ggplot2::theme_minimal()

# update graphical parameter  ----------------------

## update labels
ctr %>% pmx_plot_dv_pred(
  labels = list(title = "DV versus PRED new title")
)

## remove draft
ctr %>% pmx_plot_dv_pred(is.draft = FALSE)

## remove horizontal line
ctr %>% pmx_plot_dv_pred(is.hline = FALSE)

## custom point aes and static parameters
## we can customize any geom_point parameter
ctr %>% pmx_plot_dv_pred(
  point = list(aes(alpha = DV), color = "green", shape = 4)
)



# stratification  ----------------------------------

## continuous stratification
ctr %>% pmx_plot_dv_pred(strat.color = ~WT0)
## categorical stratification
ctr %>% pmx_plot_dv_pred(strat.facet = ~SEX)
## using formula notation
ctr %>% pmx_plot_dv_pred(strat.facet = STUD~SEX)

# subsetting  --------------------------------------

## we can use any expression involving the data
ctr %>% pmx_plot_dv_pred(filter = DV > mean(DV) & PRED < median(PRED))
## filter and stratify
ctr %>% pmx_plot_dv_pred(filter = SEX == 1, strat.facet = ~SEX)


# transformation  --------------------------------------

## apply a log transformation in y
ctr %>% pmx_plot_dv_pred(trans = "log10_y")

update or add a new abbreviation

Description

update or add a new abbreviation

Usage

set_abbrev(ctr, ...)

Arguments

Examples

ctr <- theophylline()
ctr %>% set_abbrev("new_param" = "new value")
ctr %>% get_abbrev("new_param")

Set a controller data set

Description

Set a controller data set

Usage

set_data(ctr, ..., envir = parent.frame())

Arguments

Details

This function can be used to set an existing data set or to create a new one. The basic idea is to change the built-in data set (change the factor level names, change some rows values or apply any other data set operation) and use the new data set using the dname parameter of pmx_plot family functions.

See Also

Other pmxclass: get_cats(), get_conts(), get_covariates(), get_data(), get_occ(), get_plot(), get_plot_config(), get_strats(), plot_names(), plots(), pmx_update(), set_plot()

Examples

ctr <- theophylline()
dx <- ctr %>% get_data("eta")
dx <- dx[, EFFECT := factor(
  EFFECT,
  levels = c("ka", "V", "Cl"),
  labels = c("Concentration", "Volume", "Clearance")
)]
## update existing data set
ctr %>% set_data(eta = dx)
## or create a new data set
ctr %>% set_data(eta_long = dx)

Create a new plot of the desired type

Description

Create a new plot of the desired type

Usage

set_plot(
  ctr,
  ptype = c("IND", "DIS", "SCATTER", "ETA_PAIRS", "ETA_COV", "PMX_QQ", "VPC", "PMX_DENS",
    "PARAM_HISTORY"),
  pname,
  use.defaults = TRUE,
  filter = NULL,
  strat.color = NULL,
  strat.facet = NULL,
  color.scales = NULL,
  trans = NULL,
  ...
)

Arguments

Value

invisible ctr object

See Also

Other pmxclass: get_cats(), get_conts(), get_covariates(), get_data(), get_occ(), get_plot(), get_plot_config(), get_strats(), plot_names(), plots(), pmx_update(), set_data()

Creates pmx controller using theophylline data

Description

Creates pmx controller using theophylline data

Usage

theophylline(settings = NULL, ...)

Arguments

Value

pmx controller

Examples

## Not run: 
theophylline()

## End(Not run)

merge facets formula with new formula

Description

merge facets formula with new formula

Usage

wrap_formula(x, origin = "lfacet")

Arguments

Value

formula object