lcmm (version 2.2.2)

createVarianceMatrix: Compute the variance matrix

Description

The function computes the variance matrix of the random effects, the correlations, or the measurement error.

Usage

createVarianceMatrix(model, which, times1, times2, nmes)

Value

a matrix

Arguments

model

an object inheriting from class hlme, lcmm, Jointlcmm or multlcmm representing a general latent class mixed model.

which

either "random", "cor", "randomY", or "error".

times1

for which = "cor", numeric vector containing the times at which the correlation should be computed

times2

for which = "cor", numeric vector containing the times at which the correlation should be computed

nmes

for which = "randomY" and which = "error", the number of measures (ie, the dimension of the returned variance matrix)

Author

Viviane Philipps

Examples

if (FALSE) {
 ## The model
 m <- hlme(fixed = Y ~ Time, mixture = ~1, random = ~1, subject = "ID",
  ng = 2, cor = BM(Time), data = data_hlme, B = c(0,20,30,-1,5,2,0.1))

## The random effects' variance matrix (the variance of the random intercept )
B <- createVarianceMatrix(m, which = "random")

## The variance of the Brownian motion at time c(1, 2, 3, 4) 
W <- createVarianceMatrix(m, which = "cor", times1 = c(1, 2, 3, 4), times2 = c(1, 2, 3, 4))

## The variance of the measurement error at 4 visit times
S <- createVarianceMatrix(m, which = "error", nmes = 4)

## In model "m", the variance matrix of the outcome at times c(1, 2, 3, 4) is:
matrix(1, nrow = 4) %*% B %*% t(matrix(1, nrow = 4)) + W + S
}