pmx_plot_vpc: VPC plot

In ggPMX: 'ggplot2' Based Tool to Facilitate Diagnostic Plots for NLME Models

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

ctr	pmx controller
type	charcater can be either percentile or scatter
idv	chracater individual variable
obs	pmx_vpc_obs object observation layer pmx_vpc_obs
pi	pmx_vpc_pi object percentile layer pmx_vpc_pi
ci	pmx_vpc_ci object confidence interval layer pmx_vpc_ci
rug	pmx_vpc_rug object rug layer pmx_vpc_rug. Note: consider not using a rug layer when bin[["within_strat"]]=TRUE, since the rugs plotted will not reflect the bins.
bin	pmx_vpc_bin object pmx_vpc_bin specify within pmx_plot_vpc() e.g.: bin = pmx_vpc_bin(style = "kmeans", n = 10)
is.legend	logical if TRUE add legend
sim_blq	logical if TRUE uses sim_blq values for plotting. Only for Monolix 2018 and later.
dname	added for compatibility with other ggPMX plots pmx_update parameters
filter	expression filter which will be applied to plotting data.
strat.facet	formula optional stratification parameter by facetting. This split plot by strats(each strat in a facet)
facets	list facet_wrap parameters.
strat.color	character optional stratification parameter by grouping. This will split the plot by group (color) of strat.
trans	character define the transformation to apply on x or y or both variables
pmxgpar	a object of class pmx_gpar possibly the output of the pmx_gpar: Shared basic graphics parameters
labels	list list containing plot and/or axis labels: title, subtitle, x , y
axis.title	list containing element_text attributes to customize the axis title. (similar to ggplot2 axis.title theme)
axis.text	list containing element_text attributes to customize the axis text (similar to ggplot2 axis.text theme)
ranges	list limits of x/y ranges
is.smooth	logical if set to TRUE add smooth layer
smooth	list geom_smooth graphical/smoothing fun parameters
is.band	logical if TRUE add horizontal band
band	list horizontal band parameters. geom_hline graphical parameters.
is.draft	logical if TRUE add draft layer
draft	list draft layer parameters. geom_text graphical parameters.
is.identity_line	logical if TRUE add an identity line
identity_line	listgeom_abline graphical parameters.
scale_x_log10	logical if TRUE use log10 scale for x axis.
scale_y_log10	logical if TRUE use log10 scale for y axis.
color.scales	list define scales parameter in case of strat.color pmx_settings
is.footnote	logical if TRUE add footnote
...	others graphics parameters passed : pmx_gpar internal function to customize shared graphical parameters pmx_vpc pmx vpc object. pmx_update function. pmx_vpc parameters

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_bin(), pmx_vpc_ci(), pmx_vpc_obs(), pmx_vpc_pi(), pmx_vpc_rug(), pmx_vpc()

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"))
)

ggPMX documentation built on May 29, 2024, 1:40 a.m.