R: Plot of predicted trajectories and link functions
plot.predict
R Documentation
Plot of predicted trajectories and link functions
Description
This function provides the class-specific predicted trajectories stemmed from a hlme, lcmm, multlcmm or Jointlcmm object.
Usage
## S3 method for class 'predictL'
plot(x,legend.loc="topright",legend,add=FALSE,...)
## S3 method for class 'predictY'
plot(x,outcome=1,legend.loc="topright",legend,add=FALSE,...)
## S3 method for class 'predictlink'
plot(x,legend.loc="topleft",legend,add=FALSE,...)
Arguments
x
an object inheriting from classes predictL, predictY or
predictlink representing respectively the predicted marginal mean
trajectory of the latent process, the predicted marginal mean
trajectory of the longitudinal outcome, or the predicted link function of a fitted latent class model.
outcome
for predictY and multivariate model fitted with multlcmm
only, the outcome to consider.
legend.loc
keyword for the position of the legend from the list "bottomright", "bottom", "bottomleft", "left", "topleft","top", "topright", "right" and "center".
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.
...
other parameters to be passed through to plotting functions or to legend
Author(s)
Cecile Proust-Lima, Benoit Liquet and Viviane Philipps
See Also
hlme, lcmm, Jointlcmm, multlcmm
Examples
################# Prediction from linear latent class model
## 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(predictL(m,newdata,var.time="Time"),legend.loc="right",bty="l")
## data from the first subject for predictions plot
firstdata<-data_hlme[1:3,]
plot(predictL(m,firstdata,var.time="Time"),legend.loc="right",bty="l")
## Not run:
################# Prediction from a joint latent class model
## 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_lcmm,
B=c(0.7576, 0.4095, -0.8232, -0.2737, 0, 0, 0, 0.2838, -0.6338,
2.6324, 5.3963, -0.0273, 1.398, 0.8168, -15.041, 10.164, 10.2394,
11.5109, -2.6219, -0.4553, -0.6055, 1.473, -0.0383, 0.8512, 0.0389,
0.2624, 1.4982))
# class-specific predicted trajectories
#(with characteristics of subject ID=193)
data <- data_lcmm[data_lcmm$ID==193,]
plot(predictY(m3,newdata=data,var.time="Time"),bty="l")
## End(Not run)