Fixed_coef {MixLFA} | R Documentation |
This function extracts the fixed effect coefficients \beta
from the results obtained from a
Mixture of Longitudinal Factor Analyzers (MLFA) model for a specified class and factor.
Fixed_coef(res_MLFA, C, d)
res_MLFA |
list containing the MLFA model parameters returned by the MLFA function. |
C |
an integer giving the number of mixture components. |
d |
an integer giving the factor index from which to extract the coefficients. This corresponds to the specific latent factor of interest. |
The function first determines the number of predictor variables (p
) by evaluating the number of columns in the
predictor matrix X
that was used in the MLFA. It then extracts the relevant coefficients from the estimated fixed effects \beta
vector
associated with the specified class C
and factor d
. The \beta
vector is structured such that coefficients
for each factor are stored in contiguous blocks; this function selects the appropriate block corresponding to the
factor d
within the class C
.
A numeric vector of length ncol(X) (number of fixed covariates in the MLFA model) containing the coefficients for the specified class and factor.
# 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))
# Extract the fixed effect coefficients for the latent factor 1 in cluster 1
coef_vector <- Fixed_coef(result_MLFA, C=1, d=1)