stdUnique {MixLFA} | R Documentation |
This function generates uniqueness plots (proportion of variance in the outcome variables in Y that is not explained by the factor analysis model) based on the estimated error variance.
stdUnique(res_MLFA, C)
res_MLFA |
a list containing the MLFA model parameters returned by the MLFA function. |
C |
an integer giving the number of mixture components. |
No return value. This function generates a plot of the uniqueness of the factor analysis model for a given cluster C
(variance that is 'unique' to the outcome variable and not explained by the common factors).
# 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))
# Generate the uniqueness plots for the first cluster
stdUnique(result_MLFA, C=1)