plot.predict: Plot of predicted trajectories and link functions

In CecileProust-Lima/lcmm: Extended Mixed Models Using Latent Classes and Latent Processes

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, shades = FALSE, ...)

## S3 method for class 'predictY'
plot(
  x,
  outcome = 1,
  legend.loc = "topright",
  legend,
  add = FALSE,
  shades = FALSE,
  ...
)

## S3 method for class 'predictYcond'
plot(x, legend.loc = "topleft", legend, add = FALSE, shades = TRUE, ...)

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.
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.
shades	logical indicating if confidence intervals should be represented with shades. Default to FALSE, the confidence intervals are represented with dotted lines.
...	other parameters to be passed through to plotting functions or to legend
outcome	for predictY and multivariate model fitted with multlcmm only, the outcome to consider.

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)

CecileProust-Lima/lcmm documentation built on March 3, 2024, 5:23 p.m.