ltm. Provides access to the most widely used options
in these programs.est(resp, model = "2PL", engine = "icl", nqp = 20, est.distr = FALSE,
logistic = TRUE, nch = 5, a.prior = TRUE, b.prior = FALSE, c.prior = TRUE,
bilog.defaults = TRUE, rasch = FALSE, run.name = "mymodel")engine="ltm".logistic=T sets the constant D in the IRT model to 1 ("logistic metric"), logistic=F sets it to 1.7. Default is T. When engine="ltm" and logistic=T, the estimated item discriminations are simply divided engine="bilog", model="3PL", and c.prior=T. Default is 5.model="1PL" or engine="ltm". Default is T.engine="ltm". Default is F.model="1PL" or model="2PL" or engine="ltm". Default is T.engine="icl" and a prior is used, use the default priors in BILOG rather than the default priors in ICL. Ignored when engine="ltm". Default is T.engine="bilog" and model="1PL" and "rasch"=T, the common value for discriminations is forced to 1, and the sum of the difficulties is 0. When engine="ltm" and model="1PL" and "ras"mymodel". Change to something else to keep the outputs of ICL of BILOG for further use. Ignored when engine="ltm"model="2PL"), all asymptotes $c_j$ are 0.
In the 1PL model (model="1PL"), all asymptotes $c_j$ are 0
and the discriminations $a_j$ are equal for all items (and sometimes to 1).
Package irtoys provides a simple common interface to the
estimation of item parameters with three different programs. It
only accesses the most basic and widely used options in these
programs. Each of the three programs has a much wider choice of
options and cababilities, and serious users must still learn the
corresponding syntax in order to access the advanced features.
Even when models are fit "by hand", irtoys may be
useful in plotting results, doing comparisons across programs etc.
Estimation of the more complex IRT models (2PL and 3PL) for some
"difficult" data sets often has to use prior distributions for the
item parameters. irtoys adopts the default behaviour of BILOG:
no priors for $b$ in any model, priors for $a$ in the 2PL and
3PL models, priors for $c$ in the 3PL model. This can be overriden
by changing the values of a.prior, b.prior, and c.prior.
If priors are used at all, they will be the same for all items. Note
that both ICL and BILOG can, at some additional effort, set different
priors for any individual item. At default, the common priors are the
BILOG defaults: normal(0,2) for $b$,
lognormal (0, 0.5) for $a$, and
beta(20*p+1, 20(1-p)+1) for $c$;
$p$ is 1 over the number of choices in the original item
formulations, which can be set with the parameter nch, and
is again assumed the same for all items.
When engine="icl" and bilog.defaults=F, any priors
used will be the ICL default ones, and based on the 4-parameter
beta distribution: beta(1.01, 1.01, -6, 6) for $b$,
beta(1.75, 3, 0, 3) for $a$, and
beta(3.5, 4, 0, 0.5) for $c$.
When engine="ltm", all commands involving priors
are ignored.
est only works when some IRT software is installed.
Package ltm is automatically loaded. ICL can be downloaded
from icl.exe
for ICL, blm1.exe, blm2.exe, and blm3.exe,
for BILOG) are located in directories that are included in the
PATH variable.data(Scored)
p.1pl <- est(Scored, model="1PL", engine="ltm")
p.2pl <- est(Scored, model="2PL", engine="ltm")Run the code above in your browser using DataLab