test_waldtest: runs a Wald test using the Waldtest() function of eRm.

In exhaustiveRasch: Item Selection and Exhaustive Search for Rasch Models

runs a Wald test using the Waldtest() function of eRm.

Description

runs a Wald test using the Waldtest() function of eRm.

Usage

test_waldtest(
  items = NULL,
  dset = NULL,
  na.rm = TRUE,
  model = NULL,
  p.par = NULL,
  modelType = NULL,
  splitcr = "median",
  icat = FALSE,
  alpha = 0.1,
  bonf = FALSE,
  estimation_param = NULL
)

Arguments

items	a numeric vector containing the index numbers of the items in dset that are used to fit the model
dset	a data.frame containing the data
na.rm	a boolean value. If TRUE, all cases with any NA are removed (na.omit). If FALSE, only cases with full NA responses are removed
model	on object of a fit Rasch model, estimated with the packages 'eRm' (classes 'RM', 'PCM' or 'RSM'), 'psychotools' (classes raschmodel, 'pcmodel' or 'rsmodel') or 'pairwise' (class 'pers'), matching the value of modelType. If 'model' is provided, this model is used. If NULL, a model is fit using 'dset' and 'items'.
p.par	a person parameter object matching the class of 'model'. If NULL, the person parameters will be estimated.
modelType	a character value defining the rasch model to fit. Possible values: "RM", "PCM", "RSM".
splitcr	as defined by eRm::Waldtest: Split criterion for subject raw score splitting. median uses the median as split criterion, mean performs a mean-split. Optionally splitcr can also be a dichotomous vector which assigns each person to a certain subgroup (e.g., following an external criterion). This vector can be numeric, character or a factor.
icat	a boolean value indicating if the waldtest will be conducted on item level (TRUE, default value) or on item category level. This parameter only effects estimations using psychotools or pairwise and will be ignored for eRm estimations.
alpha	a numeric value for the alpha level. Will be ignored if use.pval is FALSE
bonf	a boolean value wheter to use a Bonferroni correction. Will be ignored if use.pval is FALSE
estimation_param	options for parameter estimation using estimation_control

Value

if none of the p-values is significant, a list containing 3 elements is returned: the item combination that was tested, a list of the class the model was estimated with (depending on modelType and estimation_param$est) with the fit model and a list with a person parameter object (depending on estimation_param$est). If there is at least one item with a significant p-value, NULL is returned.

exhaustiveRasch documentation built on April 3, 2025, 6:18 p.m.