xclass: Cross classifications

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

Cross classifications

Description

This function crosses the posterior classifications of two estimated models

Usage

xclass(m1, m2)

Arguments

m1	an object inheriting from classes hlme, lcmm, multlcmm, Jointlcmm, mpjlcmm or externVar
m2	an object inheriting from classes hlme, lcmm, multlcmm, Jointlcmm, mpjlcmm or externVar

Value

the contingency table of the two classifications

Author(s)

Viviane Philipps and Cecile Proust-Lima

Examples

## Estimation of the models
m2 <- hlme(Y~Time*X1,mixture=~Time,random=~Time,classmb=~X2+X3,subject='ID',ng=2,
data=data_hlme,B=c(0.11,-0.74,-0.07,20.71,29.39,-1,0.13,2.45,-0.29,4.5,0.36,0.79,0.97))
m3 <- hlme(fixed = Y ~ Time * X1, mixture = ~Time, random = ~Time,subject = "ID",
classmb = ~X2 + X3, ng = 3, data = data_hlme,B=c(-0.21, 0.31, -2.11, -0.81, -0.24,
-0.18, 25.4, 20.09, 30.18, -0.43, -1.1, 0.25, 2.37, -0.29, 2.34, 0.03, 0.74, 0.97))

## Compare the classifications
xclass(m2,m3)
# The 39 subjects in class 2 of m3 come from class 1 of m2.
# In the same way, all the subjects in class 3 come from class 2 of m2.
# Class 1 of m3 mixes subject from class 1 and class 2 of m2.

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