RM {eRm} | R Documentation |
This function computes the parameter estimates of a Rasch model for binary item responses by using CML estimation.
RM(X, W, se = TRUE, sum0 = TRUE, etaStart)
X |
Input 0/1 data matrix or data frame; rows represent individuals, columns represent items. Missing values are inserted as NA . |
W |
Design matrix for the Rasch model. If omitted, the function will compute W automatically. |
se |
If TRUE , the standard errors are computed. |
sum0 |
If TRUE , the parameters are normed to sum-0 by specifying
an appropriate W . If FALSE , the first parameter is restricted to 0. |
etaStart |
A vector of starting values for the eta parameters can be specified. If missing, the 0-vector is used. |
For estimating the item parameters the CML method is used.
Available methods for RM-objects are print
, coef
, model.matrix
,
vcov
, summary
, logLik
, person.parameters
, plotICC
, plotjointICC
,
LRtest
, Waldtest
.
Returns an object of class dRm, Rm, eRm
and contains the log-likelihood value, the parameter estimates and their standard errors.
loglik |
Conditional log-likelihood. |
iter |
Number of iterations. |
npar |
Number of parameters. |
convergence |
See code output in nlm . |
etapar |
Estimated basic item parameters. |
se.eta |
Standard errors of the estimated basic item parameters. |
betapar |
Estimated item (easiness) parameters. |
se.beta |
Standard errors of item parameters. |
hessian |
Hessian matrix if se = TRUE . |
W |
Design matrix. |
X |
Data matrix. |
X01 |
Dichotomized data matrix. |
call |
The matched call. |
Patrick Mair, Reinhold Hatzinger
Fischer, G. H., and Molenaar, I. (1995). Rasch Models - Foundations, Recent Developements, and Applications. Springer.
Mair, P., and Hatzinger, R. (2007). Extended Rasch modeling: The eRm package for the application of IRT models in R. Journal of Statistical Software, 20(9), 1-20.
Mair, P., and Hatzinger, R. (2007). CML based estimation of extended Rasch models with the eRm package in R. Psychology Science, 49, 26-43.
# Rasch model with beta.1 restricted to 0 data(raschdat1) res <- RM(raschdat1, sum0 = FALSE) print(res) summary(res) res$W #generated design matrix # Rasch model with sum-0 beta restriction; no standard errors computed res <- RM(raschdat1, se = FALSE, sum0 = TRUE) print(res) summary(res) res$W #generated design matrix #Rasch model with missing values data(raschdat2) res <- RM(raschdat2) print(res) summary(res)