lcmm: predictL – R documentation

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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, ...)

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.

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

Author(s)

Cecile Proust-Lima, Viviane Philipps

Examples

#### 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))
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")

## End(Not run)

lcmm

Extended Mixed Models Using Latent Classes and Latent Processes

v1.9.2

GPL (>= 2.0)

Authors

Cecile Proust-Lima, Viviane Philipps, Amadou Diakite and Benoit Liquet

Initial release

2020-07-07