prctilemlx: Percentiles of the empiricial distribution of longitudinal...

In MarcLavielle/mlxR: Simulation of Longitudinal Data

Percentiles of the empiricial distribution of longitudinal data

Description

Compute and display percentiles of the empiricial distribution of longitudinal data.

Usage

prctilemlx(
  r,
  col = NULL,
  number = 8,
  level = 80,
  plot = TRUE,
  color = "#9a35ff",
  group = NULL,
  facet = TRUE,
  labels = NULL,
  band = NULL
)

Arguments

r	a data frame with a column ‘⁠id⁠’, a column ‘⁠time⁠’ and a column with values. The times should be the same for each individual.
col	a vector with the three column indexes for ‘⁠id⁠’, ‘⁠time/x⁠’ and ‘⁠y⁠’. Default = c(1, 2,3).
number	the number of intervals (i.e. the number of percentiles minus 1).
level	the largest interval (i.e. the difference between the lowest and the highest percentile).
plot	if TRUE the empirical distribution is displayed, if FALSE the values are returned
color	a color to be used for the plots (default="#9a35ff")
group	variable to be used for defining groups (by default, ‘⁠group⁠’ is used when it exists)
facet	makes subplots for different groups if TRUE
labels	vector of strings
band	is deprecated (use number and level instead) ; a list with two fields number the number of intervals (i.e. the number of percentiles minus 1). level the largest interval (i.e. the difference between the lowest and the highest percentile).

Details

See https://simulx.lixoft.com/mlxr-documentation/ for more details.

Value

a ggplot object if plot=TRUE ; otherwise, a list with fields:

• proba a vector of probabilities of length band$number+1

• color a vector of colors used for the plot of length band$number

• y a data frame with the values of the empirical percentiles computed at each time point

Examples

## Not run: 
  myModel <- inlineModel("
  [LONGITUDINAL]
  input = {ka, V, Cl}
  EQUATION:
  C = pkmodel(ka,V,Cl)
  
  [INDIVIDUAL]
  input = {ka_pop, V_pop, Cl_pop, omega_ka, omega_V, omega_Cl}
  DEFINITION:
  ka = {distribution=lognormal, reference=ka_pop, sd=omega_ka}
  V  = {distribution=lognormal, reference=V_pop,  sd=omega_V }
  Cl = {distribution=lognormal, reference=Cl_pop, sd=omega_Cl}
  ")
  
  N=2000
  
  pop.param   <- c(
    ka_pop  = 1,    omega_ka  = 0.5,
    V_pop   = 10,   omega_V   = 0.4,
    Cl_pop  = 1,    omega_Cl  = 0.3)
    
  res <- simulx(model     = myModel,
                parameter = pop.param,
                treatment = list(time=0, amount=100),
                group     = list(size=N, level='individual'),
                output    = list(name='C', time=seq(0,24,by=0.1)))
  # res$C is a data.frame with 2000x241=482000 rows and 3 columns
  head(res$C)
  # we can display the empirical percentiles of C using the default 
  # settings (i.e. percentiles of order 10%, 20%, ... 90%)
  prctilemlx(res$C)
  # The 3 quartiles (i.e. percentiles of order 25%, 50% and 75%) are displayed by 
  # selecting a 50% interval splitted into 2 subintervals
  prctilemlx(res$C, number=2, level=50)
  # A one 90% interval can be displayed using only one interval
  prctilemlx(res$C, number=1, level=90)
  # or 75 subintervals in order to better represent the continuous distribution 
  # of the data within this interval
  prctilemlx(res$C, number=75, level=90)
  # prctilemlx produces a ggplot object that can be modified
  pl <- prctilemlx(res$C, number=4, level=80) 
  pl + ylab("concentration") + ggtitle("predictive distribution")
  # The percentiles are not plotted by setting plot=FALSE
  pr.out <- prctilemlx(res$C, number=4, level=80, plot=FALSE)
  print(pr.out$proba)
  print(pr.out$color)
  print(pr.out$y[1:5,])

## End(Not run)

MarcLavielle/mlxR documentation built on May 28, 2023, 4:25 p.m.