lcmm (version 1.5.7)

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 or Jointlcmm object.

Usage

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

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. Data frame should include all the covariates listed in x$Xnames2. Names of data frame should be exactly x$Xnames2 that is the names of covariates specified in lcmm call

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

ylim

optional numeric vector of length 2, giving the y coordinate range.

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

• Returns a plot

See Also

hlme, lcmm, Jointlcmm

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)
## 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")