th_est_ml: Latent Trait Estimation

In flexmet: Flexible Latent Trait Metrics using the Filtered Monotonic Polynomial Item Response Model

Description

Compute latent trait estimates using either maximum likelihood (ML) or expected a posteriori (EAP) trait estimation.

Usage

th_est_ml(dat, bmat, maxncat = 2, cvec = NULL, dvec = NULL, lb = -4, ub = 4)

th_est_eap(
  dat,
  bmat,
  maxncat = 2,
  cvec = NULL,
  dvec = NULL,
  int = int_mat(npts = 33)
)

Arguments

dat	Data matrix of binary item responses with one column for each item. Alternatively, a vector of binary item responses for one person.
bmat	Matrix of FMP item parameters, one row for each item.
maxncat	Maximum number of response categories (the first maxncat - 1 columns of bmat are intercepts)
cvec	Vector of lower asymptote parameters, one element for each item.
dvec	Vector of upper asymptote parameters, one element for each item.
lb	Lower bound at which to truncate ML estimates.
ub	Upper bound at which to truncate ML estimates.
int	Matrix with two columns used for numerical integration in EAP. Column 1 contains the x coordinates and Column 2 contains the densities.

Value

Matrix with two columns: est and either sem or psd

est	Latent trait estimate
sem	Standard error of measurement (mle estimates)
psd	Posterior standard deviation (eap estimates)

Examples

set.seed(3453)
bmat <- sim_bmat(n_items = 20, k = 0)$bmat

theta <- rnorm(10)
dat <- sim_data(bmat = bmat, theta = theta)

## mle estimates
mles <- th_est_ml(dat = dat, bmat = bmat)

## eap estimates
eaps <- th_est_eap(dat = dat, bmat = bmat)

cor(mles[,1], eaps[,1])
# 0.9967317

flexmet documentation built on July 14, 2021, 1:06 a.m.