Type:	Package
Title:	Item Selection and Exhaustive Search for Rasch Models
Version:	0.3.7
Date:	2024-12-16
Description:	Automation of the item selection processes for Rasch scales by means of exhaustive search for suitable Rasch models (dichotomous, partial credit, rating-scale) in a list of item-combinations. The item-combinations to test can be either all possible combinations or item-combinations can be defined by several rules (forced inclusion of specific items, exclusion of combinations, minimum/maximum items of a subset of items). Tests for model fit and item fit include ordering of the thresholds, item fit-indices, likelihood ratio test, Martin-Löf test, Wald-like test, person-item distribution, person separation index, principal components of Rasch residuals, empirical representation of all raw scores or Rasch trees for detecting differential item functioning. The tests, their ordering and their parameters can be defined by the user. For parameter estimation and model tests, functions of the packages 'eRm', 'psychotools' or 'pairwise' can be used.
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
Imports:	eRm, psychotree, parallel, stats, utils, psych, tictoc, methods, psychotools, pairwise, arrangements, pbapply
Depends:	R (≥ 3.5.0)
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
VignetteBuilder:	knitr
RoxygenNote:	7.3.2
URL:	https://github.com/chrgrebe/exhaustiveRasch
BugReports:	https://github.com/chrgrebe/exhaustiveRasch/issues
NeedsCompilation:	no
Packaged:	2024-12-16 21:25:53 UTC; Chris
Author:	Christian Grebe [cre, aut], Mirko Schürmann [aut], Joerg-Henrik Heine [ctb], Patrick Mair [ctb], Thomas Rusch [ctb], Reinhold Hatzinger [ctb], Marco J. Maier [ctb], Rudolf Debelak [ctb]
Maintainer:	Christian Grebe <grebe@hepcos.com>
Repository:	CRAN
Date/Publication:	2024-12-17 10:30:02 UTC

Activities of Daily Living - dichotomous example data

Description

ADL date from N=591 nursing home residents from Germany

Usage

ADL

Format

Activities of Daily Living (V06-V39: 1= yes; 0= no):

• V06: Transfer from bed independently.

• V07: Stand up from a chair/wheelchair independently.

• V08: Walk independently.

• V09: Stand independently.

• V11: micro-change positions in bed independently.

• V12: completely change position in bed independently.

• V22: Wash at a washbasin independently (without back and feet).

• V23: Wash back and feet independently

• V24: Take a shower independently

• V25: dress and undress the upper body independently

• V26: put on shoes and stockings independently

• V27: take a meal independently

• V28: drink independently from a prepared cup

• V36: use the toilet independently

• V39: intimate hygiene independently

• age: age (1= <65; 2= 65-74; 3= 75-84; 4= 85-94; 5= >=95)

• sex: age (1= female; 2= male)

Source

Data from Grebe (2013)

References

Grebe C (2013). Pflegeaufwand und Personalbemessung in der stationären Langzeitpflege: Entwicklung eines empirischen Fallgruppensystems auf der Basis von Bewohnercharakteristika. 3-Länder-Konferenz Pflege & Pflegewissenschaft, Konstanz.

InterprofessionalCollaboration - polytomous example data

Description

data for certainty of action measured with the Health Professionals Competence Scales (HePCoS-G). Dimension: interprofessional collaboration. sample: N=716 nurses, midwifes, occupational therapists, physiotherapists and speech therapists.

Usage

InterprofessionalCollaboration

Format

V01-V19: "How confident do you feel in the following situation?" (0= not confident; 1= hardly confident; 2= rather confident; 3=very confident)

• V01: I communicate with other professions as part of my job.

• V02: I use information about a client from other professions.

• V03: I work with other professions when I transfer a client to their responsibility.

• V04: I work with other professions when I take over a client into my responsibility.

• V05: I involve specialists from other professions in the care of a client.

• V06: I coordinate goals of a client with the goals of other professions.

• V07: I coordinate health-promoting interventions for a client with other professions.

• V08: I inform other professions about the current status of a client.

• V09: I contribute aspects of a client's care to interprofessional case conferences.

• V10: I contribute aspects of a client's care to interprofessional case conferences and justify them on the basis of current research findings.

• V11: I contribute ethical aspects to interprofessional case conferences

• V12: I coordinate processes within an interprofessional team.

• V13: I coordinate communication processes during a conflict in an interprofessional team.

• V14: I lead an interprofessional team.

• V15: I participate in the development of interprofessional care concepts.

• V16: I participate in the development of an interdisciplinary evidence-based guideline.

• V17: I contribute my profession's perspective to discussions with members of other professions.

• V18: I contribute ethical aspects of care to a discussion with members of other professions.

• V19: I discuss the use of a new technology with members of other professions.

• profession: profession (1= physiotherapist; 2= occupational therapist; 3= speech therapist; 4= nurse; 5= midwife)

• age: age (1= <20; 2= 20-24; 3= 25-29; 4= 30-34; 5= 35-39; 6= 40-44; 7= 45-49; 8= 50-54; 9= 55-59; 10= >=60)

• sex: sex (1= male; 2= female)

• job_experience: job experience in years (including education)

Source

Data from the construction sample of the Health Professionals Competence Scales (HePCoS)

References

Grebe C, Schürmann M, Latteck, ÄD (2021). Die HePCoS-Skalen zur Kompetenzerfassung in den Gesundheitsfachberufen. Berichte aus Forschung und Lehre (48). Bielefeld, Fachhochschule Bielefeld. DOI: http://dx.doi.org/10.13140/RG.2.2.13480.08967/1

itemfit statistics for 'psychotools' Anderson's likelihood ratio test for 'psychotools'

Description

itemfit statistics for 'psychotools' Anderson's likelihood ratio test for 'psychotools'

Usage

LRtest.psy(model, modelType, splitcr = "median", splitseed = NULL)

Arguments

Value

the p-value of the likelihood-ratio test.

Examples

 model <- psychotools::raschmodel(ADL[c(6,7,12,14,15)])
 LRtest.psy(model=model, modelType="RM", splitcr="random", splitseed=332)

adds information criteria to the @IC slot of an object of class passed_exRa-class

Description

adds information criteria to the @IC slot of an object of class passed_exRa-class

Usage

add_ICs(obj, ignoreCores = 1)

Arguments

Value

an object of passed_exRa-class.

Examples

  library(exhaustiveRasch)
  data(ADL)
  passed <- exhaustive_tests(dset= ADL[c(1:4,7,12,14)],
    modelType= "RM", scale_length= 5, alpha=0.05,
    tests=c("test_LR", "test_itemfit", "test_respca"),
    splitcr_LR = ADL[,17], itemfit_param =
      itemfit_control(use.pval=FALSE, upperMSQ=1.5, lowerMSQ=0.5),
    estimation_param= estimation_control(
      est="psychotools"), verbose=FALSE)
  passed <- add_ICs(passed)

checks if all possible raw scores occur in the data.

Description

checks if all possible raw scores occur in the data.

Usage

all_rawscores(
  items = NULL,
  dset = NULL,
  na.rm = TRUE,
  model = NULL,
  p.par = NULL,
  modelType = NULL,
  estimation_param = NULL
)

Arguments

Value

if all possible raw scores occur in dset, 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 at least one raw score does not occur in dset, NULL is returned.

selects item combinations based on defined rules

Description

selects item combinations based on defined rules

Usage

apply_combo_rules(
  full,
  combo_length = 4:length(full),
  forced_items = NULL,
  rules = NULL,
  ignoreCores = 1
)

Arguments

Value

a list of numeric vectors containing the selected item combinations that match the defined rules of forced_items and/or rules.

Examples

data(ADL)
forced <- c(1)
rules_object <- list() # rules-Object
rules_object[[1]] <- list("min", 1, 8:9)
rules_object[[2]] <- list("min", 1, 14:15)
rules_object[[3]] <- list("max", 2, 2:6)
rules_object[[4]] <- list("forbidden", c(8,9))
final_combos <- apply_combo_rules(combo_length = 5:7,
                                  full=1:length(ADL),
                                   forced_items = forced,
                                   rules= rules_object)

selects item combinations based on defined rules

Description

selects item combinations based on defined rules

Usage

check_combo_rules(full, rules = NULL)

Arguments

Value

a list of numeric vectors containing the selected item combinations that match the defined rules of forced_items and/or rules.

cognition - polytomous example data data measured with the FACT-cog (subscale perceived cognitive functioning) sample size: N=1009

Description

cognition - polytomous example data data measured with the FACT-cog (subscale perceived cognitive functioning) sample size: N=1009

Usage

cognition

Format

Items 1-20: FACT-cog Items. Questions starting with In the past 7 days... Answer categories: 0= never; 1= about once a week; 2= two to three times a week; 3=nearly every day; 4= several times a day

• A1_CogA1: I have had trouble forming thoughts

• A3_CogA3: My thinking has been slow

• C7_PC8: I have had trouble concentrating

• M9_PC10: I have had trouble finding my way to a familiar place

• M10_PC11: I have had trouble remembering where I put things, like my keys or my wallet

• M12_NQCOG68: I have had trouble remembering new information, like phone numbers or simple instructions

• V13_PC14: I have had trouble recalling the name of an object while talking to

• V15_PC16: I have had trouble finding the right word(s) to express

• V16_CogV16: I have used the wrong word when I referred to an object

• V17_CogV17b: I have had trouble saying what I mean in conversations with others

• F19_NQCOG69: I have walked into a room and forgot what I meant to get or do there

• F23_NQCOG77: I had to work really hard to pay attention or I would make a mistake.

• F24_PC26: I have forgotten names of people soon after being introduced

• F25_PC28: My reactions in everyday situations have been

• C31_PC36: I have had to work harder than usual to keep track of what I was

• C32_PC37: My thinking has been slower than usual

• C33a_PC38: I have had to work harder than usual to express myself clearly

• C33c_PC40: I have had to use written lists more often than usual so I would not forget things

• MT1_PC41: I have had trouble keeping track of what I was doing when interrupted

• MT2_PC42: I have had trouble shifting back and forth between different activities that require thinking

• CogPCI: Subscale Score: Percieved Cognitive Impairments (CogPCI), Range 0-72

• SD1: What is your age?

• SD2: What is your gender? (1= male; 2= female)

• SD3: Are you of Hispanic, Latino, or Spanish origin? (0= no; 1= yes)

• SD4: In which country were you born? (1= United States of America; 2= Mexico; 3= Canada; 4= United Kingdom; 5= other)

• SD6: What is the HIGHEST level of school or degree you have completed? (1= Less than High School Degree; 2= High School Diploma or General Education Diploma (GED); 3= Some college courses/Technical degree/ Vocational program/Associate degree; 4= College degree/advanced degree (Bachelors, Masters, Doctorate))

• SD7: In the past 30 days, have you used or taken medication for which a prescription is needed? Include only those products prescribed by a health professional such as a doctor or dentist. (0= no; 1= yes)

• SD8: How many different times did you stay in any hospital overnight or longer during the past 12 months?

• SD9: During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities such as self-care, work, or recreation?

Source

Data for perceived cognitive functioning from the PROsetta Stone Wave 2 dataset.

References

Cella D (2017). PROsetta Stone Wave 2. Harvard Dataverse. DOI: 10.7910/DVN/WZVYZI.

options for item parameter estimation

Description

options for item parameter estimation

Usage

estimation_control(
  est = "psychotools",
  resp.cat = NULL,
  use.thurst = TRUE,
  splitseed = NULL
)

Arguments

Value

a list containing the options

(main function) Runs exhaustive tests

Description

(main function) Runs exhaustive tests

Usage

exhaustive_tests(
  dset,
  modelType = "PCM",
  combos = NULL,
  scale_length = 4:length(dset),
  na.rm = TRUE,
  tests = c("no_test"),
  splitcr_mloef = NULL,
  splitcr_LR = NULL,
  splitcr_wald = NULL,
  icat_wald = FALSE,
  alpha = 0.1,
  bonf = FALSE,
  DIFvars = NULL,
  gap_prop = 0,
  extremes = TRUE,
  max_contrast = NULL,
  PSI = 0.8,
  ICs = FALSE,
  keep = FALSE,
  ignoreCores = 1,
  verbose = TRUE,
  ...,
  itemfit_param = NULL,
  estimation_param = NULL
)

Arguments

Value

an object of passed_exRa-class.

Examples

  library(exhaustiveRasch)
  data(ADL)
  passed <- exhaustive_tests(dset= ADL[c(1:4,7,12,14)],
    modelType= "RM", scale_length= 5, alpha=0.05,
    tests=c("test_LR", "test_itemfit", "test_respca"),
    splitcr_LR = ADL[,17], itemfit_param =
      itemfit_control(use.pval=FALSE, upperMSQ=1.5, lowerMSQ=0.5),
    estimation_param= estimation_control(
      est="psychotools"), verbose=FALSE)

returns a matrix with dims like resp matrix with expected scores and more ... func. by joerg-henrik heine jhheine(at)googlemail.com needs func. pvx in i.pvx.R and pvx.matrix in i.pvx.matrix.R Notation and formulas see: Wright & Masters 1982 p.100 in a revised form (korrigendum) see http://www.rasch.org/rmt/rmt34e.htm

Description

returns a matrix with dims like resp matrix with expected scores and more ... func. by joerg-henrik heine jhheine(at)googlemail.com needs func. pvx in i.pvx.R and pvx.matrix in i.pvx.matrix.R Notation and formulas see: Wright & Masters 1982 p.100 in a revised form (korrigendum) see http://www.rasch.org/rmt/rmt34e.htm

Usage

expscore(pers_obj, na_treat = NA)

Arguments

Value

internal

returns a matrix with dims like resp matrix with expected scores and more ... func. by joerg-henrik heine jhheine(at)googlemail.com needs func. pvx in i.pvx.R and pvx.matrix in i.pvx.matrix.R Notation and formulas see: Wright & Masters 1982 p.100 in a revised form (korrigendum) see http://www.rasch.org/rmt/rmt34e.htm

Description

returns a matrix with dims like resp matrix with expected scores and more ... func. by joerg-henrik heine jhheine(at)googlemail.com needs func. pvx in i.pvx.R and pvx.matrix in i.pvx.matrix.R Notation and formulas see: Wright & Masters 1982 p.100 in a revised form (korrigendum) see http://www.rasch.org/rmt/rmt34e.htm

Usage

expscore.psy(X, thres, ppar, na_treat = NA)

Arguments

Value

internal

parameter estimation for for rasch models.

Description

parameter estimation for for rasch models.

Usage

fit_rasch(X, modelType, estimation_param)

Arguments

Value

a model of the class depending of modelType and estimation_param$est.If no model can be fit, NULL is returened.

options for test_itemfit()

Description

options for test_itemfit()

Usage

itemfit_control(
  zstd = FALSE,
  msq = TRUE,
  outfits = TRUE,
  use.pval = TRUE,
  lowerMSQ = 0.7,
  upperMSQ = 1.3,
  lowerZ = -1.96,
  upperZ = 1.96,
  use.rel = FALSE
)

Arguments

Value

a list containing the options

S3 logLik for Object of class "pers"

Description

S3 logLik method to extract the log-likelihood for object of class pairwise::pers

Usage

## S3 method for class 'pers'
logLik(object, sat = FALSE, p = FALSE, ...)

Arguments

Value

an object of class 'logik', containing the likelihood an degrees of freedom

Martin-Loef test for psychotools

Description

Martin-Loef test for psychotools

Usage

mloef.psy(model, modelType, splitcr = "median", splitseed = NULL)

Arguments

Value

a list containing the test statistic, the degrees of freedom and the p-value of the Martin-Löf test.

Examples

 model <- psychotools::raschmodel(ADL[c(6,7,12,14,15)])
 mloef.psy(model=model, modelType="RM", splitcr="random", splitseed=332)

no test will be conducted, but rasch models (RM, PCM, RSM - depending on modelType) will be fit

Description

no test will be conducted, but rasch models (RM, PCM, RSM - depending on modelType) will be fit

Usage

no_test(
  items = NULL,
  dset = NULL,
  na.rm = TRUE,
  model = NULL,
  p.par = NULL,
  modelType = NULL,
  estimation_param = NULL
)

Arguments

Value

if there are no items with disordered thresholds in the model, a list containing two elements is returned: the pattern that was tested and a list of type RM, RCM or RSM (depending on modelType) with the fit model. If there is at least one item with disordered thresholds, NULL is returned.

conducts and controls the parallelisation of the tests, Intentionally, there are no defauklt values for the parameters, as this internal function is called by exhaustive_tests that also defines the default values for this function.

Description

conducts and controls the parallelisation of the tests, Intentionally, there are no defauklt values for the parameters, as this internal function is called by exhaustive_tests that also defines the default values for this function.

Usage

parallized_tests(
  dset,
  modelType,
  combos,
  models,
  p.par,
  na.rm,
  testfunction,
  itemfit_param,
  splitcr = NULL,
  icat_wald,
  alpha,
  bonf,
  DIFvars,
  gap_prop,
  max_contrast,
  PSI,
  extremes,
  ignoreCores,
  estimation_param,
  tests_count,
  verbose = TRUE,
  ...
)

Arguments

Value

a list containing 3 elements is returned: a list of the item combinations, a list of the models (depending on modelType and estimation_param$est) with the fit models and a list with the person parameter objects (depending on estimation_param$est).

class passed_exRa, an S4 class representing an a result of the exhaustive_tests function

Description

class passed_exRa, an S4 class representing an a result of the exhaustive_tests function

Slots

process

a data.frame containg process information from the call to exhaustive_tests

passed_combos

a list of vectors containing item combinations using the indices of the items

passed_models

a list of objects of the class the respective package used for estimation uses for its models

passed_p.par

a list of objects containing the person parameters. Depending on the package used for estimation, also residuals and/or the PSI value can be part of this list

IC

a data.frame containing information criteria for each of the models in passed_models

data

a data.frame containing the data used for the analyses.

timings

a data.frame containing the the timings of the analyses.

itemfit statistics for psychotools

Description

itemfit statistics for psychotools

Usage

ppar.psy(model = NULL)

Arguments

Value

an object containing person parameters, residuals and PSI

Examples

 model <- psychotools::raschmodel(ADL[c(6,7,12,14,15)])
 ppar.psy(model)

this is an internal function for expscore()

Description

this is an internal function for expscore()

Usage

pvx(theta, thres, xm = NULL)

Arguments

Value

internal

pvx matrix

Description

pvx matrix

Usage

pvx.matrix(theta_v, thres, xm_v = NULL)

Arguments

Value

internal

internal pvx.super function"

Description

internal

Usage

pvx.super(theta_v, thres = NULL, dat = NULL)

Arguments

Value

internal

removes subsets or supersets of item combinations

Description

removes subsets or supersets of item combinations

Usage

remove_subsets(obj, keep_longest = FALSE)

Arguments

Value

depending of the class of obj, either a list of numeric vectors containing the remaining item combinations or an object of passed_exRa-class with the remaining item combinations, fit models and information criteria, but without the process information.

Examples

  library(exhaustiveRasch)
  data(ADL)
  passed_ADL <- exhaustive_tests(dset=ADL[1:15], scale_length=4:5,
    modelType= "RM", na.rm=TRUE, tests= c("test_LR"),
    splitcr_LR = ADL[,17], estimation_param = estimation_control())
  passed_shortest <- remove_subsets(passed_ADL, keep_longest=FALSE)
 

S3 residuals for Object of class "pers"

Description

S3 residuals method to extract the (Rasch) residuals for object of class pairwise::pers

Usage

## S3 method for class 'pers'
residuals(object, res = "sr", na_treat = 0, ...)

Arguments

Value

a matrix containing the residuals

summary function for class passed_exRa

Description

summary function for class passed_exRa

Usage

## S4 method for signature 'passed_exRa'
summary(object)

Arguments

Value

no object is returned, the summary is printed to the console. This comprises: tested scale lengths, total number of tested item combinations, applied tests, number of passed item combinations that passed each test, item importance and runtimes.

builds a raschtree using the raschtree or rstree function of the psychotree Package.

Description

builds a raschtree using the raschtree or rstree function of the psychotree Package.

Usage

test_DIFtree(
  items = NULL,
  DIFvars = NULL,
  dset = NULL,
  na.rm = TRUE,
  model = NULL,
  p.par = NULL,
  modelType = NULL,
  alpha = 0.1,
  estimation_param = NULL
)

Arguments

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.

runs Anderson's likelihood ration test using the LRtest() function of eRm.

Description

runs Anderson's likelihood ration test using the LRtest() function of eRm.

Usage

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

Arguments

Value

if the p-value of the test is not significant AND if no items were excluded in the test due to missing patterns, 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 the test is significant, NULL is returned.

runs a principal component analysis (PCA) on the residuals of the rasch model.

Description

runs a principal component analysis (PCA) on the residuals of the rasch model.

Usage

test_PSI(
  items = NULL,
  dset = NULL,
  na.rm = TRUE,
  model = NULL,
  p.par = NULL,
  modelType = NULL,
  PSI = NULL,
  estimation_param = NULL
)

Arguments

Value

if the value of the PSI for the model is >= the PSI argument, 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). Else, NULL is returned.

checks the itemfit indices of a rasch model using the itemfit() function of eRm.

Description

checks the itemfit indices of a rasch model using the itemfit() function of eRm.

Usage

test_itemfit(
  items = NULL,
  dset = NULL,
  na.rm = TRUE,
  control,
  modelType = NULL,
  model = NULL,
  p.par = NULL,
  alpha = 0.1,
  bonf = FALSE,
  estimation_param = NULL
)

Arguments

Value

if all fit indices meet the given criteria, 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 at least one item's fit indices do not meet the given criteria, NULL is returned.

runs Martin-Loef Test using the MLoef() function of eRm.

Description

runs Martin-Loef Test using the MLoef() function of eRm.

Usage

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

Arguments

Value

if the p-value of the test is not 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 the test is significant, NULL is returned.

checks the relationship between the person parameter distribution and the item (or: threshold) locations for defined criteria

Description

checks the relationship between the person parameter distribution and the item (or: threshold) locations for defined criteria

Usage

test_personsItems(
  items = NULL,
  dset = NULL,
  na.rm = TRUE,
  model = NULL,
  p.par = NULL,
  modelType = NULL,
  gap_prop = 0,
  extremes = TRUE,
  estimation_param = NULL
)

Arguments

Value

if the criteria are met, 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 the criteria are not met, NULL is returned.

runs a principal component analysis (PCA) on the residuals of the rasch model.

Description

runs a principal component analysis (PCA) on the residuals of the rasch model.

Usage

test_respca(
  items = NULL,
  dset = NULL,
  na.rm = TRUE,
  model = NULL,
  p.par = NULL,
  modelType = NULL,
  max_contrast = 1.5,
  estimation_param = NULL
)

Arguments

Value

if the maximum eigenvalue of the contrasts of the pca is < max_contrast, 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). Else, NULL is returned.

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

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.

checks for disordered thresholds in rasch models

Description

checks for disordered thresholds in rasch models

Usage

threshold_order(
  items = NULL,
  dset = NULL,
  na.rm = TRUE,
  model = NULL,
  p.par = NULL,
  modelType = NULL,
  estimation_param = NULL
)

Arguments

Value

if there are no items with disordered thresholds in the model, 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$est) with the fit modeland a list with a person parameter object (depending on estimation_param$est). If there is at least one item with disordered thresholds, NULL is returned.

Fischer and Scheiblechner's "wald-like" S-statistic for psychotools

Description

Fischer and Scheiblechner's "wald-like" S-statistic for psychotools

Usage

waldtest.psy(
  model,
  modelType,
  splitcr = "median",
  splitseed = NULL,
  icat = FALSE
)

Arguments

Value

a vector containing the p-values of the Scheiblechner's "wald-like" S-statistic for the items (if icat=FALSE) or for the item categories (if icat=TRUE).

Examples

 model <- psychotools::raschmodel(ADL[c(6,7,12,14,15)])
   waldtest.psy(model=model, modelType="RM", splitcr="random", splitseed=332,
   icat=FALSE)