make_scoring_matrix_aem()
has a new argument eType
allowing to prepare scoring matrices with distnict processes regarding the extremity trait in different nodes of the decision tree, as desribed in (Merhof & Meiser, 2023).make_mplus_irtree_model_syntax()
and make_mplus_gpcm_model_syntax()
collapses ANALYSIS element to be a single string what makes MplusAutomation::mplusModeler()
not failing when run in R >=4.3.0.make_mplus_irtree_model_syntax()
and make_mplus_gpcm_model_syntax()
works with items
provided as a simple character vector.make_mplus_irtree_model_syntax()
and make_mplus_gpcm_model_syntax()
enabling to conveniently prepare the Mplus syntax specifying response-style models.read_mplus_object_results()
enabling convenient reading results from an estimated mplusObject.expand_responses()
deals with data with responses being a tibble.generate_intercepts_sqn()
and thus generate_intercepts()
accepts argsd
containing elements that are not atomic vectors, e.g. list of matrices.make_scoring_matrix_rt()
and make_scoring_matrix_stz()
enabling convenient construction of scoring matrices using random thresholds and sum to zero approaches.generate_item_expected_scores()
that allows to generate (by numerical integration) expected probabilities of responses (response categories) given an item object or test object and covariance matrix of latent traits.thresholds2intercepts()
and intercepts2thresholds()
allowing conversion between thresholds and intercepts parameterizations of GPCM items.expand_responses()
do not take into account missing values in the data while performing assertion that all values in the data are members of the set of values defined by rownames of the scoringMatrix
.generate_intercepts()
correctly transforms parameters generated in a parameterization involving item difficulty and thresholds relative to his difficulty into parametrization of intercepts in case of GPCM items (i.e. it cumulatively sums up thresholds and subtracts difficulty instead of adding difficulty to thresholds).make_item()
allows to get non-zero first element of the intercepts
argument if length of intercepts
equals to the number of rows of the scoringMatrix
(previously it returned an error), but still warns that this is atypical specification.generate_intercepts()
.make_test()
assigns names to the created items by default and provides additional names
argument if user wants to provide names himself/herself.generate_test_responses()
uses items' names (if there are any) to name columns of the returned matrix.generate_test_responses()
converts matrix it returns to numeric one (if only this is possible without loss of information); it also provides additional argument tryConvertToNumeric
that allows to bring back its former behavior (i.e. returning a character matrix).generate_intercepts_sml()
, and consequently generate_intercepts()
when called with FUNt
argument, returns intercepts matrix with additional first columns of zeros to make it compatible with the format that uses function simdata()
from mirt package (generate_test_responses()
was, and still is, able to deal with providing it intercepts either with or without such additional zeros).simdata()
from package mirt may be used to speed up generation of GPCM responses.generate_slopes()
, generate_intercepts()
and make_test()
.lnorm_mean()
, lnorm_sd()
and find_pars_lnorm()
.make_scoring_matrix_trivial()
. It is useful if one wants to use rstyles for simulations that don't involve response styles.Add the following code to your website.
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