gpt_gen_sample: Generate Sample of Data Sets

In danheck/gpt: Generalized Processing Tree Models

Generate Sample of Data Sets

Description

Allows to simulate parameter heterogeneity. Note that truncated normal distributions are used to ensure that parameters within the specified bounds.

Usage

gpt_gen_sample(
  S,
  n,
  theta,
  eta,
  theta_sd = 0,
  eta_sd = 0,
  file,
  latent,
  restrictions = NULL,
  cpu
)

Arguments

S	sample size (number of participants)
n	vector of the length of number of trees with n per tree
theta	vector of true MPT parameter values (consider also restricted parameters!). Values will be appropriatly reordered if a named vector ist given (otherwise, check order!).
eta	vector of true continuous parameters. Values will be appropriatly reordered if a named vector ist given (otherwise, check order!).
theta_sd	vector giving the standard deviation of normally distributed MPT parameters across participants (default: homogeneity)
eta_sd	vector giving the standard deviation of normally distributed continuous parameters across participants (default: homogeneity)
file	a character vector specifying the path to the model file
latent	type of latent continuous distribution (one of "normal", "exgauss", "exwald", "gamma", "weibull", "lognormal", "wald", or beta). Can be a vector if multiple continuous variables y have different distributions (e.g., latent = c("normal", "gamma"))
restrictions	list with parameter restrictions (e.g., list("g=0.5", "mean1=mean2=100"))
cpu	number of cores used for data generation (default: number of cores minus one). alternatively, a parallel cluster initialized by 'cl <- parallel::makeCluster(4)'

See Also

gpt_gen

Examples

###### 2-High-Threshold Model (with fixed guessing) ######

## Not run: 
n <- c(targets=20, lures=20)     # number of items
theta <- c(do=.6,dn=.4, g=.5)          # MPT parameters
eta <- c(mu=400, sig=50, lambda_do=200, 
         lambda_go=600, lambda_gn=400, 
         lambda_dn=300)          # exGaussian parameters
file <- paste0(path.package("gpt"), "/models/2htm_exgauss.txt")

gen <- gpt_gen_sample(S=3, n=n, theta=theta, eta=eta, 
                      theta_sd=.1, eta_sd=10,
                      file =file, latent="exgauss")
sapply(gen, head, 3) 
# check mean of latent continuous distributions:
by(gen$data$y, gen$data$state, mean)

## End(Not run)

danheck/gpt documentation built on Feb. 12, 2024, 6:21 a.m.