predictY: Marginal predictions (possibly class-specific) of a 'hlme',...

In lcmm: Extended Mixed Models Using Latent Classes and Latent Processes

Description Usage Arguments Value Author(s) See Also Examples

View source: R/predictY.lcmm.R

Description

For hlme and Jointlcmm objects, the function computes the predicted values of the longitudinal marker in each latent class for a specified profile of covariates. For lcmm and multlcmm objects, the function computes predicted values in the natural scale of the outcomes for a specified profile of covariates. For linear and threshold links, the predicted values are computed analytically. For splines and Beta links, a Gauss-Hermite or Monte-Carlo integration are used to numerically compute the predictions. In addition, for any type of link function, confidence bands (and median) can be computed by a Monte Carlo approximation of the posterior distribution of the predicted values.

Usage

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## S3 method for class 'lcmm'
predictY(x,newdata,var.time,methInteg=0,nsim=20,draws=FALSE,
ndraws=2000,na.action=1,...)
## S3 method for class 'hlme'
predictY(x,newdata,var.time,draws=FALSE,na.action=1,...)
## S3 method for class 'Jointlcmm'
predictY(x,newdata,var.time,methInteg=0,nsim=20,draws=FALSE,
ndraws=2000,na.action=1,...)
## S3 method for class 'multlcmm'
predictY(x,newdata,var.time,methInteg=0,nsim=20,draws=FALSE,
ndraws=2000,na.action=1,...)

Arguments

Value

An object of class predictY with values :

- pred : a matrix with the same rows (number and order) as in newdata.

For hlme objects and lcmm or Jointlcmm with draws=FALSE, returns a matrix with ng columns corresponding to the ng class-specific vectors of predicted values computed at the point estimate

For objects of class lcmm or Jointlcmm with draws=TRUE, returns a matrix with ng*3 columns representing the ng class-specific 50%, 2.5% and 97.5% percentiles of the approximated posterior distribution of the class-specific predicted values.

For objects of class multlcmm with draws=FALSE, returns a matrix with ng+1 columns: the first column indicates the name of the outcome which is predicted and the ng subsequent columns correspond to the ng class-specific vectors of predicted values computed at the point estimate

For objects of class multlcmm with draws=TRUE, returns a matrix with ng*3+1 columns: the first column indicates the name of the outcome which is predicted and the ng*3 subsequent columns correspond to the ng class-specific 50%, 2.5% and 97.5% percentiles of the approximated posterior distribution of the class-specific predicted values.

- times : the var.time variable from newdata

Author(s)

Cecile Proust-Lima, Viviane Philipps

See Also

lcmm, multlcmm, hlme, Jointlcmm

Examples

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#### Prediction from a 2-class model with a Splines link function
## Not run: 
## fitted model
m<-lcmm(Ydep2~Time*X1,mixture=~Time,random=~Time,classmb=~X2+X3,
subject='ID',ng=2,data=data_lcmm,link="splines",B=c(
-0.175,      -0.191,       0.654,      -0.443, 
-0.345,      -1.780,       0.913,       0.016, 
 0.389,       0.028,       0.083,      -7.349, 
 0.722,       0.770,       1.376,       1.653, 
 1.640,       1.285))
summary(m)
## predictions for times from 0 to 5 for X1=0
newdata<-data.frame(Time=seq(0,5,length=100),
X1=rep(0,100),X2=rep(0,100),X3=rep(0,100))
pred0 <- predictY(m,newdata,var.time="Time")
head(pred0)
## Option draws=TRUE to compute a MonteCarlo 
# approximation of the predicted value distribution 
# (quite long with ndraws=2000 by default)
\dontrun{
pred0MC <- predictY(m,newdata,draws=TRUE,var.time="Time")
}
## predictions for times from 0 to 5 for X1=1
newdata$X1 <- 1
pred1 <- predictY(m,newdata,var.time="Time")
## Option draws=TRUE to compute a MonteCarlo 
# approximation of the predicted value distribution 
# (quite long with ndraws=2000 by default)
\dontrun{
pred1MC <- predictY(m,newdata,draws=TRUE,var.time="Time")
}

## End(Not run)