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

Description Usage Arguments Details Value Author(s) References See Also Examples

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

1	raschreg(items, f_reg, z_reg, init = NULL, fixed = NULL)

`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 the explanatory variables indicated in `f_reg`.
`init`	Optional numeric vector of initial values at which initialize numerical optimization.
`fixed`	Optional numeric vector (length J + p). If supplied, only NA entries in fixed will be estimated.

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.

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

Fernando Massa, fmassa@iesta.edu.uy

\insertRef

rasch1960raschreg

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baker2004raschreg

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deboeck2004raschreg

rasch, raschd, raschreg, raschdreg, irt2p, irt2preg

  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  <- raschreg(X, ~ x1 + x2, x1x2)