LRSM(X, W , mpoints = 1, groupvec = 1, se = TRUE, sum0 = TRUE, etaStart)
NA
.TRUE
, the standard errors are computed.TRUE
, the parameters are normalized to sum-0 by specifying
an appropriate W
. If FALSE
, the first parameter is restricted to 0.eRm
containing:code
output in nlm
.se = TRUE
.W
the LRSM can be viewed as a more parsimonous
RSM, 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.
If W
is user-defined, it is nevertheless necessary to
specify mpoints
and groupvec
. It is important that first the time contrasts and
then the group contrasts have to be imposed.
Available methods for LRSM-objects are print
, coef
,
model.matrix
, vcov
,summary
, logLik
, person.parameters
.LLTM
,LPCM
#LRSM for two measurement points
#20 subjects, 2*3 items, W generated automatically, first parameter set to 0,
#no standard errors computed.
data(lrsmdat)
res <- LRSM(lrsmdat, mpoints = 2, groupvec = 1, sum0 = FALSE, se = FALSE)
print(res)
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