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

Description Usage Arguments Details Value Examples

View source: R/prctilemlx.R

Description

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

Usage

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prctilemlx(
  r = NULL,
  col = NULL,
  project = NULL,
  outputVariableName = NULL,
  number = 8,
  level = 80,
  plot = TRUE,
  color = NULL,
  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).

project

simulx project filename (with extension ".smlx")

outputVariableName

name of the output to consider. By default the first output will be consider. You must define either a 'r' dataframe and the associated 'col' argument or a simulx project and the name of the output 'outputVariableName"

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

colors to be used for the plots In case of one group or facet = TRUE, only the first color will be used

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

Value

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

Examples

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## 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)

RsSimulx documentation built on April 8, 2021, 5:07 p.m.