pmx_plot_vpc: 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,
  ...
)

Value

ggplot2 or list of ggplot2 objects

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).

Examples

Run this code

# \donttest{
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"))
)

# }

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