Description Usage Arguments Value Author(s) Examples

This function simulates a sample according to a model estimated with `hlme`

,
`lcmm`

, `multlcmm`

or `Jointlcmm`

functions.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |

`object` |
an object of class |

`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 |

`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 |

`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 |

`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. |

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.

Viviane Philipps and Cecile Proust-Lima

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## 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)))
``` |

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