lcmm (version 1.6.6)

plot.predict: Plot of class-specific marginal predictions for the longitudinal outcome

Description

This function provides the class-specific predicted trajectories stemmed from a hlme, lcmm, multlcmm or Jointlcmm object.

Usage

## S3 method for class 'hlme':
plot.predict(x,newdata,var.time,legend.loc="topright",
na.action=1,legend,add=FALSE,confint=FALSE,...)
## S3 method for class 'lcmm':
plot.predict(x,newdata,var.time,legend.loc="topright",
na.action=1,legend,add=FALSE,confint=FALSE,...)
## S3 method for class 'Jointlcmm':
plot.predict(x,newdata,var.time,legend.loc="topright",
na.action=1,legend,add=FALSE,confint=FALSE,...)
## S3 method for class 'multlcmm':
plot.predict(x,newdata,var.time,legend.loc="topright",
na.action=1,legend,add=FALSE,confint=FALSE,...)

Arguments

x

an object inheriting from classes hlme, lcmm or Jointlcmm representing respectively a fitted latent class linear mixed-effects model, a general latent class mixed model or a joint 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

var.time

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

legend.loc

keyword for the position of the legend from the list "bottomright", "bottom", "bottomleft", "left", "topleft","top", "topright", "right" and

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.

legend

character or expression to appear in the legend. If no legend should be added, "legend" should be NULL.

add

logical indicating if the curves should be added to an existing plot. Default to FALSE.

confint

logical indicating if confidence intervals for the predictions should be calculated and plotted. Default to FALSE.

...

other parameters to be passed through to plotting functions or to legend

Value

• Returns a data frame containing the time values at which the predictions are computated (corresponding to the x-axis in the plot), the predicted values of the latent process, the lower and the upper limits of the confidence intervals (if calculated).

See Also

hlme, lcmm, Jointlcmm, multlcmm

Examples

################# Prediction from linear latent class model
data(data_hlme)
## fitted model
m<-lcmm(Y~Time*X1,mixture=~Time,random=~Time,classmb=~X2+X3,
subject='ID',ng=2,data=data_hlme,B=c(0.41,0.55,-0.18,-0.41,
-14.26,-0.34,1.33,13.51,24.65,2.98,1.18,26.26,0.97))
## newdata for predictions plot
newdata<-data.frame(Time=seq(0,5,length=100),
X1=rep(0,100),X2=rep(0,100),X3=rep(0,100))
plot.predict(m,newdata,"Time","right",bty="l")
## data from the first subject for predictions plot
firstdata<-data_hlme[1:3,]
plot.predict(m,firstdata,"Time","right",bty="l")


################# Prediction from a joint latent class model
data(data_Jointlcmm)
## fitted model - see help of Jointlcmm function for details on the model
m3 <- Jointlcmm(fixed= Ydep1~Time*X1,mixture=~Time,random=~Time,
classmb=~X3,subject='ID',survival = Surv(Tevent,Event)~X1+mixture(X2),
hazard="3-quant-splines",hazardtype="PH",ng=3,data=data_Jointlcmm,
B=c(0.7667, 0.4020, -0.8243, -0.2726, 0.0000, 0.0000, 0.0000, 0.3020,
-0.6212, 2.6247, 5.3139, -0.0255, 1.3595, 0.8172, -11.6867, 10.1668,
10.2355, 11.5137, -2.6209, -0.4328, -0.6062, 1.4718, -0.0378, 0.8505,
0.0366, 0.2634, 1.4981))
# class-specific predicted trajectories 
#(with characteristics of subject ID=193)
data <- data_Jointlcmm[data_Jointlcmm$ID==193,]
plot.predict(m3,var.time="Time",newdata=data,bty="l")