lcmm (version 1.6.6)

plot.linkfunction: Plot of the estimated transformation between the outcome and the underlying latent process

Description

Displays the estimated transformation(s) specified in the option link of lcmm and multlcmm function. It corresponds to the (non)linear parameterized link assumed between the oberved longitudinal outcome and the underlying latent process and estimated as long as other regression parameters.

Usage

## S3 method for class 'lcmm':
plot.linkfunction(x,legend.loc="topright",legend,add=FALSE,...)
## S3 method for class 'multlcmm':
plot.linkfunction(x,legend.loc="topleft",legend=x$Ynames,add=FALSE,...)

Arguments

x

an object inheriting from class lcmm, representing a fitted latent class mixed model

legend.loc

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

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

See Also

lcmm

Examples

#### Estimation of homogeneous mixed models with different assumed link
#### functions, a quadratic mean trajectory for the latent process with 
#### independent random intercept, slope and quadratic slope
#### (comparison of linear, Beta and 3 splines link functions)
data(data_Jointlcmm)
# linear link function
m10<-lcmm(Ydep2~Time+I(Time^2),random=~Time+I(Time^2),subject='ID',ng=1,
data=data_Jointlcmm,link="linear",
B=c(-0.7454, -0.2031,  0.2715,  0.2916 , 0.6114, -0.0064,  0.0545,
  0.0128, 25.3795, 2.2371))
summary(m10)
# Beta link function
m11<-lcmm(Ydep2~Time+I(Time^2),random=~Time+I(Time^2),subject='ID',ng=1,
data=data_Jointlcmm,link="beta",B=c(-0.9109, -0.0831,  0.5194,  0.1910 ,
 0.8984, -0.0179, -0.0636,  0.0045,  0.5514, -0.7692,  0.7037,  0.0899))
summary(m11)
plot.linkfunction(m11)
# I-splines with 3 equidistant nodes
m12<-lcmm(Ydep2~Time+I(Time^2),random=~Time+I(Time^2),subject='ID',ng=1,
data=data_Jointlcmm,link="3-equi-splines",B=c(-0.9272, -0.0753 , 0.5304, 
 0.1950,  0.9260, -0.0204, -0.0739 , 0.0059, -7.8369,  0.9228 ,-1.4689,
  2.0396,  1.8102))
summary(m12)
# I-splines with 5 nodes at quantiles
m13<-lcmm(Ydep2~Time+I(Time^2),random=~Time+I(Time^2),subject='ID',ng=1,
data=data_Jointlcmm,link="5-quant-splines",B=c(-0.9262, -0.0759,  0.5306,
  0.1927,  0.9364, -0.0191, -0.0764,  0.0063, -7.8144, -1.0691,  1.3714,
 1.9428,  1.2957,  0.9396,  1.0587))
summary(m13)
# I-splines with 5 nodes, and interior nodes entered manually
m14<-lcmm(Ydep2~Time+I(Time^2),random=~Time+I(Time^2),subject='ID',ng=1,
data=data_Jointlcmm,link="5-manual-splines",intnodes=c(10,20,25),
B=c(-0.9315, -0.0739 , 0.5254 , 0.1933,  0.9418, -0.0206, -0.0776,
  0.0064, -7.8645, 0.7470,  1.2080,  1.5537 , 1.7558 , 1.3386 , 1.0982))
summary(m14)
plot.linkfunction(m14,bty="l")



#### Plot of estimated different link functions:
#### (applicable for models that only differ in the "link function" used. 
#### Otherwise, the latent process scale is different and a rescaling
#### is necessary)
transfo <- data.frame(marker=m10$estimlink[,1],linear=m10$estimlink[,2],
beta=m11$estimlink[,2],spl_3e=m12$estimlink[,2],spl_5q=m13$estimlink[,2],
spl_5m=m14$estimlink[,2])
plot(transfo[,1]~transfo[,2],xlim=c(-10,5),col=1,type='l',
xlab="latent process",ylab="marker",bty="l")
par(new=TRUE)
plot(transfo[,1]~transfo[,3],xlim=c(-10,5),col=2,type='l',
xlab="",ylab="",bty="l")
par(new=TRUE)
plot(transfo[,1]~transfo[,4],xlim=c(-10,5),col=3,type='l',
xlab="",ylab="",bty="l")
par(new=TRUE)
plot(transfo[,1]~transfo[,5],xlim=c(-10,5),col=4,type='l',
xlab="",ylab="",bty="l")
legend(x="bottomright",legend=colnames(transfo[,2:5]),col=1:4,
lty=1,inset=.02,bty="n")