lcmm (version 2.2.1)

simulate.lcmm: Data simulation according to models from lcmm package

Description

This function simulates a sample according to a model estimated with hlme, lcmm, multlcmm or Jointlcmm functions.

Usage

# S3 method for lcmm
simulate(
  object,
  nsim,
  seed,
  times,
  tname = NULL,
  n,
  Xbin = NULL,
  Xcont = NULL,
  entry = 0,
  dropout = NULL,
  pMCAR = 0,
  ...
)

Value

a data frame with one line per observation and one column per variable. Variables appears in the following order : subject id, measurement time, entry time, binary covariates, continuous covariates, longitudinal outcomes, latent class, entry time, survival time, event indicator.

Arguments

object

an object of class hlme, lcmm, multlcmm or Jointlcmm

nsim

not used (for compatibility with stats::simulate). The function simulates only one sample

seed

the random seed

times

either a data frame with 2 columns containing IDs and measurement times, or a vector of length 4 specifying the minimal and maximum measurement times, the spacing between 2 consecutive visits and the margin around this spacing

tname

the name of the variable representing the measurement times in object. Default to the second column's name of times if it is a data frame, and to object$var.time otherwise.

n

number of subjects to simulate. Required only if times is not a data frame.

Xbin

an optional named list giving the probabilities of the binary covariates to simulate. The list's names should match the binary covariate's names used in object.

Xcont

an optional named list giving the mean and standard deviation of the Gaussian covariates to simulate. The list's names should match the continuous covariate's names used in object.

entry

expression to simulate a subject's entry time. Default to 0.

dropout

expression to simulate a subject's time to dropout. Default to NULL, no dropout is considered.

pMCAR

optional numeric giving an observation's probability to be missing. Default to 0, no missing data are introduced.

...

additionnal options. None is used yet.

Author

Viviane Philipps and Cecile Proust-Lima

Examples

## estimation of a 2 classes mixed model
m2 <- hlme(Y~Time*X1,mixture=~Time,random=~Time,classmb=~X2+X3,subject='ID',
         ng=2,data=data_hlme,B=c(0.11,-0.74,-0.07,20.71,
                                 29.39,-1,0.13,2.45,-0.29,4.5,0.36,0.79,0.97))

## simulate according to model m2 with same number of subjects and
## same measurement times as in data_lcmm. Binary covariates X1 and X2 are simulated
## according to a Bernoulli distribution with probability p=0.5, continuous covariate
## X3 is simulated according to a Gaussian distribution with mean=1 and sd=1 :
dsim1 <- simulate(m2, times=data_hlme[,c("ID","Time")],
                  Xbin=list(X1=0.5, X2=0.5), Xcont=list(X3=c(1,1)))

## simulate a dataset of 300 subjects according to the same model
## with new observation times, equally spaced and ranging from 0 to 3 :
dsim2 <- simulate(m2, times=c(0,3,0.5,0), n=300, tname="Time",
                  Xbin=list(X1=0.5, X2=0.5), Xcont=list(X3=c(1,1)))