LLTM {eRm} | R Documentation |
This function computes the parameter estimates of a linear logistic test model (LLTM) for binary item responses by using CML estimation.
LLTM(X, W, mpoints = 1, Groups = 1)
X |
Input 0/1 data matrix or data frame; rows represent individuals (N in total), columns represent items. |
W |
Design matrix for the Rasch model. If omitted, the function will compute W automatically. |
mpoints |
Number of measurement points. |
Groups |
Vector of length N which determines the group membership of each subject, starting from 1 |
Through appropriate definition of W the LLTM can be viewed as a more parsimonous
Rasch model, on the one hand, e.g. by imposing some cognitive base operations
to solve the items. One the other hand, linear extensions of the Rasch model
such as group comparisons and repeated measurement designs can be computed.
If more than one measurement point is examined, the item responses for the 2nd, 3rd, etc.
measurement point are added column-wise in X.
Available methods for LLTM-objects are print
, coef
,
model.matrix
, vcov
.
Returns on object of class eRm
and contains the log-likelihood value,
the parameter estimates and their standard errors.
loglik |
The log-likelihood. |
iter |
Number of iterations required. |
etapar |
Estimated basic item parameters. |
se_eta |
Standard errors of the estimated basic item parameters. |
betapar |
Estimated item parameters. |
NA's are not allowed in X.
Patrick Mair, Reinhold Hatzinger
Fischer, G. H., and Molenaar, I. (1995). Rasch Models - Foundations, Recent Developements, and Applications. Springer.
print.eRm
,coef.eRm
,vcov.eRm
,model.matrix.eRm
#LLTM for 2 measurement points #100 persons, 2*10 items, W generated automatically data(X_lltm) res <- LLTM(X_lltm, mpoints = 2) res