lcmm (version 1.6.6)

predictY: Predicted values of a hlme, lcmm, multlcmm or Jointlcmm object in the natural scale of the longitudinal outcome(s) for a specified profile of covariates.

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

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

Arguments

x

an object inheriting from class lcmm, hlme, Jointlcmm or multlcmm representing a general latent class mixed model.

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

methInteg

optional integer specifying the type of numerical integration required only for predictions with splines or Beta link functions. Value 0 (by default) specifies a Gauss-Hermite integration which is very rapid but neglects the correlation between the predic

nsim

For a lcmm or multlcmm object only; optional number of points used in the numerical integration with splines or Beta link functions. For methInteg=0, nsim should be chosen among the following values: 5, 7, 9, 15, 20, 30, 40 or 50

draws

For a lcmm or multlcmm object only; optional boolean specifying whether median and confidence bands of the predicted values should be computed (TRUE) - whatever the type of link function. In this case, a Monte Carlo approximation

ndraws

For a lcmm or multlcmm object only; if draws=TRUE, ndraws specifies the number of draws that should be generated to approximate the posterior distribution of the predicted values. By default, ndraws=2000.

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

• For all objects, a matrix is returned with the same rows (number and order) as in newdata.

  For hlme and Jointlcmm objects or lcmm 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 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.

See Also

lcmm, multlcmm, hlme, Jointlcmm

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))
pred0 <- predictY(m,newdata)
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)
}
## predictions for times from 0 to 5 for X1=1
newdata$X1 <- 1
pred1 <- predictY(m,newdata)
## 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)
}