prctilemlx: Percentiles of the empiricial distribution of longitudinal...
In mlxR: Simulation of Longitudinal Data

Description Usage Arguments Details Value Examples

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

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

`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).

See http://simulx.webpopix.org/mlxr/prctilemlx/ for more details.

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

## 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)