R: Search for the global maximum of the log-likelihood
search.model
R Documentation
Search for the global maximum of the log-likelihood
Description
It search for the global maximum of the log-likelihood given a vector of possible number
of classes to try for.
Usage
search.model(S, yv = rep(1,ns), kv, X = NULL, link = 0, disc = 0,
difl = 0, multi = 1:J, fort = FALSE, tol = 10^-10,
nrep = 2, glob = FALSE, disp=FALSE)
Arguments
S
matrix of all response sequences observed at least once in the sample and listed row-by-row
(use 999 for missing response)
yv
vector of the frequencies of every response configuration in S
kv
vector of the possible numbers of latent classes
X
matrix of covariates that affects the weights
link
type of link function (1 = global logits, 2 = local logits);
with global logits the Graded Response model
results; with local logits the Partial Credit results (with dichotomous responses, global logits
is the same as using local logits resulting in the Rasch or the 2PL model depending on the value
assigned to disc)
disc
indicator of constraints on the discriminating indices (0 = all equal to one, 1 = free)
difl
indicator of constraints on the difficulty levels (0 = free, 1 = rating scale parametrization)
multi
matrix with a number of rows equal to the number of dimensions and elements in each row
equal to the indices of the items measuring the dimension corresponding to that row
fort
to use fortran routines when possible
tol
tolerance level for checking convergence of the algorithm as relative difference between
consecutive log-likelihoods
nrep
number of repetitions of each random initialization
glob
to use global logits in the covariates
disp
to dispaly partial output
Value
out.single
output of each single model (as from est_multi_poly) for each k in kv
bicv
value of BIC index for each k in kv
lkv
value of log-likelihood for each k in kv
Author(s)
Francesco Bartolucci, Silvia Bacci, Michela Gnaldi - University of Perugia (IT)
References
Bartolucci, F. (2007), A class of multidimensional IRT models for testing unidimensionality and clustering
items, Psychometrika, 72, 141-157.
Bacci, S., Bartolucci, F. and Gnaldi, M. (2012), A class of Multidimensional Latent Class IRT models for
ordinal polytomous item responses, Technical report, http://arxiv.org/abs/1201.4667.
Examples
## Not run:
## Search Multidimensional LC IRT models for binary responses
# Aggregate data
data(naep)
X = as.matrix(naep)
out = aggr_data(X)
S = out$data_dis
yv = out$freq
# Define matrix to allocate each item on one dimension
multi1 = rbind(c(1,2,9,10),c(3,5,8,11),c(4,6,7,12))
out2 = search.model(S, yv = yv, kv=c(1:4),multi=multi1)
## End(Not run)
providing 
.