irt2preg: Fitting Two Parameter Logistic Regression Model

In nando11235813/raschreg: Item Response Theory models including explanatory variables of the ability parameter

Description

Estimate the two parameter logistic model incorporating regression variables for the abiity parameter, approximating the marginal log-likelihood using Laplace approximation.

Usage

  irt2preg(items, f_reg, z_reg, init = NULL)

Arguments

items	Numeric matrix (or data.frame) containing only zeroes and ones (J columns).
f_reg	A one sided regression formula indicating the explanatory variables for the ability parameter.
z_reg	A data frame containing explanatory variables indicated in f_reg.
init	Optional numeric vector of initial values at which initialize numerical optimization.

Details

Optimizes raschreglikLA numericaly via nlminb. Rows containing at least one NA, whether in items or x_reg, are removed from both. Standard errors of model parameters are approximated by inverting the observed information matrix.

Value

An object of class rasch is a list containing the following componentes:

call	The matched call
coef	A named vector of coefficients
iter	Number of iterations used to optimize de log-likelihood
loglik	The log-likelihood value
vcov	The variance-covariance matrix of the estimated parameters
items	The item matrix
beta	(Only when regression terms are included) the estimated regression parameters
linpred	(Only when regression terms are included) prediction covariates

Author(s)

Fernando Massa, [e-mail]

References

\insertRef

rasch1960raschreg

\insertRef

baker2004raschreg

\insertRef

deboeck2004raschreg

See Also

rasch, raschd, raschreg, raschdreg, irt2p

Examples

  dif  <- c(-3, -2, -1, 0, 1, 2, 3) 
	n    <- 200
	x1x2 <- cbind(x1 = rnorm(n), x2 = ifelse(runif(n) < 0.5, 0, 1))
	beta <- c(1.2, 0.7)
	X    <- sim_rasch(n = n, delta = dif, reg = x1x2, beta = beta)
  mod  <- irt2preg(X, ~ x1 + x2, x1x2)

nando11235813/raschreg documentation built on Oct. 2, 2021, 3:11 p.m.