Fit the Two-Profile LTA Model

Fits the two-profile LTA model using Mplus.

Usage

FitLTA2Profiles(
  data,
  wd = ".",
  ncores = 1L,
  mplus_bin = NULL,
  starts = c(20, 4),
  stiterations = 10,
  stscale = 5
)

Arguments

data

R object. Object of class simculta.

wd

Character string. Working directory.

ncores

Positive integer. Number of cores to use.

mplus_bin

Character string. Path to Mplus binary. If mplus_bin = NULL, the function will try to find the appropriate binary.

starts

Vector of positive integer of length two. Number of initial stage starts and number of final stage optimizations.

stiterations

Positive integer. Number of initial stage iterations.

stscale

Positive integer. Random start scale.

Value

Returns an object of class fitculta. which is a list with the following elements:

• call: Function call.

• fun: Function used ("FitLTA2Profiles").

• args: Function arguments.

• output: Mplus output files.

• elapsed: Elapsed time.

See also

Other Model Fitting Functions: FitCULTA1Profile(), FitCULTA2Profiles(), FitRILTA2Profiles(), InputCULTA1Profile(), InputCULTA2Profiles(), InputLTA2Profiles(), InputRILTA2Profiles()

Author

Ivan Jacob Agaloos Pesigan

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
FitLTA2Profiles(data = data)
} # }