lcmm (version 1.6.2)

predictL: Class-specific marginal predictions in the latent process scale for lcmm objects

Description

This function provides a matrix containing the class-specific predicted trajectories computed in the latent process scale, that is the latent variable underlying the curvilinear outcome(s), for a profile of covariates specified by the user. This function applies only to lcmm a nd multlcmm objects. The function plot.predict provides directly the plot of these class-specific predicted trajectories. The function predictY provides the class-specific predicted trajectories computed in the natural scale of the outcome(s).

Usage

## S3 method for class 'lcmm':
predictL(x,newdata,na.action=1,...)

Arguments

x

an object inheriting from class lcmm or multlcmm representing a (latent class) mixed model involving a latent process and estimated link function(s).

newdata

data frame containing the data from which predictions are computed. Data frame should include all the covariates listed in x$Xnames2. Names of data frame should be exactly x$Xnames2 that is the names of covariates specified in lcmm call

na.action

Integer indicating how NAs are managed. The default is 1 for 'na.omit'. The alternative is 2 for 'na.fail'. Other options such as 'na.pass' or 'na.exclude' are not implemented in the current version.

...

further arguments to be passed to or from other methods. They are ignored in this function.

Value

• Returns a matrix

See Also

plot.predict, predictY, lcmm

Examples

#### Prediction from a 2-class model with a Splines link function
data(data_Jointlcmm)
## fitted model
m<-lcmm(Ydep2~Time*X1,mixture=~Time,random=~Time,classmb=~X2+X3,
subject='ID',ng=2,data=data_Jointlcmm,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))
predictL(m,newdata)
## predictions for times from 0 to 5 for X1=1
newdata$X1 <- 1
predictY(m,newdata)