lcmm: plot.predict – R documentation

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plot.predict

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

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)

lcmm

Extended Mixed Models Using Latent Classes and Latent Processes

v1.9.2

GPL (>= 2.0)

Authors

Cecile Proust-Lima, Viviane Philipps, Amadou Diakite and Benoit Liquet

Initial release

2020-07-07