simulated_MLFA {MixLFA}R Documentation

simulated_MLFA: Simulated data from the MLFA model

Description

This dataset contains a list of matrices, each with a specific purpose or structure. The list includes four matrices: Y, X, Z, and id. simulated using the following parameters (described in the simulation study in (Ounajim et al., 2023)):

Usage

data(simulated_MLFA)

Format

A list with four elements:

Y

the observed outcomes matrix with 10 columns and 500 rows (100 subjects with 5 observations each).

X

the fixed effects design matrix with two columns (two explanatory variables for explaining the factor loadings variation) and 500 rows.

Z

the random effect design matrix similar to X.

id

a vector of length 500 containing subject identifiers

References

Ounajim, A., Slaoui, Y., Louis, P. Y., Billot, M., Frasca, D., & Rigoard, P. (2023). Mixture of longitudinal factor analyzers and their application to the assessment of chronic pain. Statistics in medicine, 42(18), 3259–3282. https://doi.org/10.1002/sim.9804

Examples

# Load the necessary datasets
data(simulated_MLFA)  # Load a simulated dataset based on the MLFA model
# Extract matrices from the list
# Extract matrix Y of outcomes of interest for the factor analysis model
Y <- simulated_MLFA$Y
# Extract matrix X of fixed effect covariates for describing the latent factors
X <- simulated_MLFA$X
# Extract matrix Z of random effect covariates for describing the latent factors
Z <- simulated_MLFA$Z
# Extract matrix id containing subject identifiers.
id <-simulated_MLFA$id
#' # Run the MLFA (Mixture of Longitudinal Factor Analyzers) function with:
# C: number of classes or clusters in our simulated data was set to 2.
# d: number of latent factors in our simulated data was set to 1.
# max_it: maximum number of iterations is set to 50 for a quick test.
# Estimation of the parameters of the MLFA model using the simulated data.
result_MLFA <- MLFA(C = 2, d = 2, X, Y, Z, id, max_it = 50, fixed_factor =  c(1,6))

[Package MixLFA version 1.0.0 Index]