lcmm (version 2.2.1)

predictL: Class-specific marginal predictions in the latent process scale for lcmm, Jointlcmm and multlcmm objects

Description

This function provides a matrix containing the class-specific predicted trajectories computed in the latent process scale, that is the latent process underlying the curvilinear outcome(s), for a profile of covariates specified by the user. This function applies only to lcmm and 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

predictL(x, newdata, var.time, na.action = 1, confint = FALSE, ...)

Value

An object of class predictL with values :

- pred : a matrix containing the class-specific predicted values in the latent process scale, the lower and the upper limits of the confidence intervals (if calculated).

- times : the var.time variable from newdata

Arguments

x

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

newdata

data frame containing the data from which predictions are computed. The data frame should include at least all the covariates listed in x$Xnames2. Names in the data frame should be exactly x$Xnames2 that are the names of covariates specified in lcmm or multlcmm calls.

var.time

A character string containing the name of the variable that corresponds to time in the data frame (x axis in the plot).

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.

confint

logical indicating if confidence should be provided. Default to FALSE.

...

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

Author

Cecile Proust-Lima, Viviane Philipps

See Also

plot.predict, predictY, lcmm

Examples

#### Prediction from a 2-class model with a Splines link function
if (FALSE) {
## 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))
predictL(m,newdata,var.time="Time")
## predictions for times from 0 to 5 for X1=1
newdata$X1 <- 1
predictY(m,newdata,var.time="Time")
}