mlxR (version 4.2.0)

prctilemlx: 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).

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

Details

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

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,])
# }