Class-specific marginal predictions in the latent process scale for lcmm, Jointlcmm and multlcmm objects
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
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).
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an object inheriting from class
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
A character string containing the name of the variable that corresponds to time in the data frame (x axis in the plot).
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.
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.
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
Cecile Proust-Lima, Viviane Philipps
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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)) 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)
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