Sampling Covariance Matrix
Usage
# S3 method for class 'fitculta'
vcov(object, ...)Examples
if (FALSE) { # \dontrun{
# complete list of R function arguments -------------------------------------
# random seed for reproducibility
set.seed(42)
# dimensions
n <- 1000 # number of individuals
m <- 6 # measurement occasions
p <- 4 # number of items
q <- 1 # common trait dimension
# covariate parameters
mu_x <- 11.4009
sigma_x <- 24.67566
# profile membership and transition parameters
nu_0 <- -3.563
kappa_0 <- 0.122
alpha_0 <- -3.586
beta_00 <- 2.250
gamma_00 <- 0.063
gamma_10 <- 0.094
# trait parameters
psi_t <- 0.10 * diag(1)
mu_t <- 0
psi_p <- diag(p)
psi_p_1 <- 0.10
psi_p_2 <- 0.10
psi_p_3 <- 0.50
psi_p_4 <- 0.50
diag(psi_p) <- c(
psi_p_1,
psi_p_2,
psi_p_3,
psi_p_4
)
mu_p <- rep(x = 0, times = p)
common_trait_loading <- matrix(
data = 1,
nrow = p,
ncol = q
)
# state parameters
common_state_loading <- matrix(
data = 1,
nrow = p,
ncol = 1
)
phi_0 <- 0.000
phi_1 <- 0.311
psi_s0 <- 1.00
psi_s <- 0.25
theta <- 0.15 * diag(p)
# profile-specific means
mu_profile <- cbind(
c(2.253, 1.493, 1.574, 1.117),
c(-0.278, -0.165, -0.199, -0.148)
)
# data generation -----------------------------------------------------------
data <- GenCULTA2Profiles(
n = n,
m = m,
mu_x = mu_x,
sigma_x = sigma_x,
nu_0 = nu_0,
kappa_0 = kappa_0,
alpha_0 = alpha_0,
beta_00 = beta_00,
gamma_00 = gamma_00,
gamma_10 = gamma_10,
mu_t = mu_t,
psi_t = psi_t,
mu_p = mu_p,
psi_p = psi_p,
common_trait_loading = common_trait_loading,
common_state_loading = common_state_loading,
phi_0 = phi_0,
phi_1 = phi_1,
psi_s0 = psi_s0,
psi_s = psi_s,
theta = theta,
mu_profile = mu_profile
)
# model fitting -------------------------------------------------------------
# NOTE: Model fitting takes time
fit <- FitCULTA2Profiles(data = data)
vcov(fit)
} # }