RM | 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 |
W |
Design matrix for the Rasch model. If omitted, the function will compute W automatically. |
se |
If |
sum0 |
If |
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.parameter
, LRtest
,
Waldtest
, plotICC
, plotjointICC
.
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 |
etapar |
Estimated basic item difficulty 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 |
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.
RSM
,PCM
, LRtest
, Waldtest
# Rasch model with beta.1 restricted to 0
res <- RM(raschdat1, sum0 = FALSE)
res
summary(res)
res$W #generated design matrix
# Rasch model with sum-0 beta restriction; no standard errors computed
res <- RM(raschdat1, se = FALSE, sum0 = TRUE)
res
summary(res)
res$W #generated design matrix
#Rasch model with missing values
res <- RM(raschdat2)
res
summary(res)
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