To search itemset that give maximum value of the criterion
stepwise_search(
X,
criterion = c("ipoqll", "ipoqlldif"),
incl_set = c(),
groups_map = c(),
cores = NULL,
isContinued = FALSE,
prevData = c(),
fileOutput = FALSE,
tempFile = "temp_stepSearch.RData",
isConvert = FALSE,
setting_par_iq = c(),
setting_par_oq = c(),
method = c("fast", "novel"),
isTraced = FALSE
)backward_search(
X,
criterion = c("ipoqll", "ipoqlldif"),
incl_set = c(),
groups_map = c(),
cores = NULL,
isContinued = FALSE,
prevData = c(),
isConvert = FALSE,
setting_par_iq = c(),
fileOutput = FALSE,
setting_par_oq = c(),
method = c("fast", "novel"),
tempFile = "temp_backSearch.RData",
isTraced = FALSE
)
# S3 method for search
summary(object, ...)
# S3 method for search
print(x, ...)
plot_search(obj, remOrdered = TRUE, locateMax = TRUE, ...)
Matrix of the highest scores (IQ-LL, OQ-LL, and IPOQ-LL) for every number of items in the included set in the set along with the corresponding itemset.
A matrix or data.frame of the observed responses (ordinal or binary response).
The criterion that should be used. The default is ipoqll.
A vector of initial items in the included set to start the search. The default is to start with full items.
A matrix or vector to map the subject to the DIFs groups.
An integer value of number of cores should be used for computation. The default is 2.
A logical value whether this search is continuing another unfinished search.
The filename of the temporary .RData file of the unfinished search.
The filename if it is wished to save the output results in file (.RData and .csv) and FALSE if not.
The filename of the temporary file to track the search progress. The default is "temp_stepSearch.RData"
which also automatically produces "temp_stepSearch.csv"
.
A logical value whether it is wanted to recompute the score of the search results using IPOQ-LL-DIF criterion.
a list of the optimization control setting parameters for the included set. See setting
parameter in autoRaschOptions()
.
a list of the optimization control setting parameters for the included set. See setting
parameter in autoRaschOptions()
.
The implementation option of log likelihood function. fast
using a c++
implementation and novel
using an R
implementation.
A logical value whether the progress need to be tracked or not.
The object of class 'search'
.
Further arguments to be passed.
The object of class 'search'
.
An object of class "search".
A logical statement whether show the order of the items removal or not.
A logical statement whether the location of the maximum score is needed to be marked or not.
To search the itemset that give the maximum score.
if (FALSE) {
search_res <- backward_search(shortDIF,criterion = "ipoqll", incl_set = c(1:4), cores = 2)
plot_search(search_res, type="l")
}
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