Package 'conquestr' reference manual

Title:	An R Package to Extend 'ACER ConQuest'
Description:	Extends 'ACER ConQuest' through a family of functions designed to improve graphical outputs and help with advanced analysis (e.g., differential item functioning). Allows R users to call 'ACER ConQuest' from within R and read 'ACER ConQuest' System Files (generated by the command `put` <https://conquestmanual.acer.org/s4-00.html#put>). Requires 'ACER ConQuest' version 5.39 or later. A demonstration version can be downloaded from <https://shop.acer.org/acer-conquest-5.html>.
Authors:	Dan Cloney [aut, cre] , Ray Adams [aut]
Maintainer:	Dan Cloney <[email protected]>
License:	GPL-3
Version:	1.3.4
Built:	2024-08-24 06:44:09 UTC
Source:	CRAN

checkItemRespValid

Description

Check that the item responses in raw data are: (1) valid, (2) each valid response mapped to an item appears at least once, and (3) each valid reponse mapped to an item has sufficently many responses (defaults to a minimum of 10 observations for each response category)

Usage

checkItemRespValid(data, caseID, validMap, varLabel, validLabel)
checkItemRespValid(data, caseID, validMap, varLabel, validLabel)

Arguments

`data`	Raw data, a data frame.
`caseID`	A string indicating the name of the case id variable in the data.
`validMap`	A data frame which contains a mapping of valid responses to item lables. This data frame shoudl be in long format, with each valis response * item conbination representing a row.
`varLabel`	A string indicating the name of the variable in validMap that identidies the valis items names/lables.
`validLabel`	A string indicating the name of the variable in validMap that contains the valid codes/responses for each item. This shoudl include missing values (e.g., "99")

Value

A list of lists: one list per item in validMap$varLabel. Within each list, there can be up to three dfs: (1) the case ids and invalid responses for the item, (2) the valid codes not observed in the data set, and (3) the valid codes observed fewer than 10 times in the data. NOTE: a wanring is thrown if the validMap$varLabel is not found in the data.

checkVars

Description

Check raw data: are all required variables preseent and ensure there are no extraneous variables.

Usage

checkVars(data, varNames, except = NULL)
checkVars(data, varNames, except = NULL)

Arguments

`data`	Raw data, a data frame.
`varNames`	Vector of valid variable names.
`except`	A vector of variable names to be excluded form the check.

Value

A list.

cnvrtItemParam

Description

takes an item in one model's parameterisation and returns it in another parameterisation.

Usage

cnvrtItemParam(item, from = "muraki", to = "conquest", D = 1)
cnvrtItemParam(item, from = "muraki", to = "conquest", D = 1)

Arguments

`item`	an item design matrix that is of size response categories (m) by four: column one is category values, usually from 0 to m. Sometimes referred to as 'x', and in this case, this value times the discrimination is the category score. column two is the delta dot parameter repeated m times (the average difficulty of the item) column three is the tau (step) parameter where for the first response category (x = 0) tau = 0, and for m >= 2, entries are deviations from delta dot. In the dichotomous case, all items in this column are zero. column four is the discrimination parameter ("a")
`from`	a string, either "muraki" or "conquest" (default) (see 10.1177/0146621697211001). Describing the parameterisation of item
`to`	a string, either "muraki" or "conquest" (default) (see 10.1177/0146621697211001). Describing the output parameterisation of the returned item parameter matrix Note that "muraki" assumes the scaling constant D = 1.7 (to give the normal ogive metric)
`D`	a number, giving the scaling constant. Default is 1 (logistic metric). Other common values are D = 1.7 (to give the normal ogive metric)

Value

an m x 4 matrix of item parameters. The same dimensions as the input, item

Examples

myTheta <- 0
myDelta <- 1.5
a <- 1.5
m <- 3
itemParamX <- seq(0, m-1, 1)
itemParamD <- rep(myDelta, m)
itemParamT <- c(0, -0.5, 0.5)
itemParamA <- rep(a, m)
itemParam <- cbind(itemParamX, itemParamD, itemParamT, itemParamA)
colnames(itemParam)<- c("x", "d", "t", "a")
myItem <- cnvrtItemParam(itemParam, from = "conquest", to = "muraki")
myTheta <- 0
myDelta <- 1.5
a <- 1.5
m <- 3
itemParamX <- seq(0, m-1, 1)
itemParamD <- rep(myDelta, m)
itemParamT <- c(0, -0.5, 0.5)
itemParamA <- rep(a, m)
itemParam <- cbind(itemParamX, itemParamD, itemParamT, itemParamA)
colnames(itemParam)<- c("x", "d", "t", "a")
myItem <- cnvrtItemParam(itemParam, from = "conquest", to = "muraki")

ConQuestCall

Description

Call an instance of 'ACER ConQuest' at the command line and run a control file (syntax).

Usage

ConQuestCall(cqc, cqExe, stdout = "")
ConQuestCall(cqc, cqExe, stdout = "")

Arguments

`cqc`	The location of the control file (syntax) to be run.
`cqExe`	The path to the 'ACER ConQuest' executable. Note, if this argument is missing, conquestr will find a local installation of ACER ConQuest by first searching the default installation locations (Program Files on Windows and Applications on Mac) then searching other local directories (Appdata and the HOME path).
`stdout`	On Mac only, can be toggled to NULL (or a connection) to suppress output to R console.

Value

prints 'ACER ConQuest' output to stdout.

Examples

## Not run: 
ConQuestCall()

## End(Not run)
## Not run: 
ConQuestCall()

## End(Not run)

ConQuestRout

Description

Read an ”ACER ConQuest” rout file created by a plot command in 'ACER ConQuest'.

Usage

ConQuestRout(myRout)
ConQuestRout(myRout)

Arguments

myRout

The location of an 'ACER ConQuest' rout file created by 'ACER ConQuest'

Value

A list containing the data objects created by 'ACER ConQuest' plot command.

Examples

myPlot <- ConQuestRout()
## Not run: 
# if you run the above example you will have the points from a plot ICC command.
str(myPlot)

## End(Not run)
myPlot <- ConQuestRout()
## Not run: 
# if you run the above example you will have the points from a plot ICC command.
str(myPlot)

## End(Not run)

ConQuestSys

Description

Read an ”ACER ConQuest” system file created by a put command in 'ACER ConQuest'. The system file must not be compressed. Use the option ‘compressed=no“ in the put command within ’ACER ConQuest'.

Usage

ConQuestSys(myCqs, isMini = FALSE)
ConQuestSys(myCqs, isMini = FALSE)

Arguments

`myCqs`	The location of an uncompressed 'ACER ConQuest' system file created by 'ACER ConQuest' > 4.35.
`isMini`	A boolean, set to TRUE if the system file is a mini system file created by 'ACER ConQuest' command put with option "mini = yes".

Value

A list containing the data objects created by 'ACER ConQuest'.

Examples

mySysData <- ConQuestSys()
myEx1SysData <- ConQuestSys(myCqs = system.file("extdata", "mysysfile.cqs", package = "conquestr"))
## Not run: 
# if you run the above example this will return your original 'ACER ConQuest' syntax.
cat(unlist(myEx1SysData$gCommandHistory))

## End(Not run)
mySysData <- ConQuestSys()
myEx1SysData <- ConQuestSys(myCqs = system.file("extdata", "mysysfile.cqs", package = "conquestr"))
## Not run: 
# if you run the above example this will return your original 'ACER ConQuest' syntax.
cat(unlist(myEx1SysData$gCommandHistory))

## End(Not run)

createConQuestProject

Description

creates a standard folder structure to work with 'ACER ConQuest' Projects.

Usage

createConQuestProject(prefix = getwd(), ...)
createConQuestProject(prefix = getwd(), ...)

Arguments

`prefix`	a valid file path where to create project folders.
`...`	optional params, including "setDebug"

Value

Boolean TRUE.

Examples

## Not run: 
createConQuestProject()

## End(Not run)
## Not run: 
createConQuestProject()

## End(Not run)

DecompressSys

Description

Internal function to decompress an 'ACER ConQuest' system file that has been compressed using zlib.

Usage

DecompressSys(myFile)
DecompressSys(myFile)

Arguments

myFile

An connection to a compressed 'ACER ConQuest' system file created by the put command in 'ACER ConQuest'.'

Value

A connection to an uncompressed system file in the users temp dir.

fisherTrnsfrm

Description

Helper function to apply Fisher's transformation to a correlation matrix.

Usage

fisherTrnsfrm(myCorMat)
fisherTrnsfrm(myCorMat)

Arguments

myCorMat

A correlation matrix.

Value

A correlation matrix with Fisher's transform applied to values -1 > x > 1.

fmtCqItanal

Description

helper function to produce nicely formatted summary tables from a ConQuest Itanal.

Usage

fmtCqItanal(
  cqItanal,
  itemNumber = "all",
  ptBisFlag = 0,
  textColHighlight = "red",
  valueDecPlace = 2
)
fmtCqItanal(
  cqItanal,
  itemNumber = "all",
  ptBisFlag = 0,
  textColHighlight = "red",
  valueDecPlace = 2
)

Arguments

`cqItanal`	An ACER ConQuest itanal list object returned by function `getCqItanal`.
`itemNumber`	a vector of generalised item numbers to format.
`ptBisFlag`	Something.
`textColHighlight`	Something.
`valueDecPlace`	Something.

Value

A list

Examples

myEx1Sys <- ConQuestSys()
myEx1Sys_itanal <- getCqItanal(myEx1Sys)
myItanalSummary <- fmtCqItanal(myEx1Sys_itanal)
print(myItanalSummary[[1]])
myEx1Sys <- ConQuestSys()
myEx1Sys_itanal <- getCqItanal(myEx1Sys)
myItanalSummary <- fmtCqItanal(myEx1Sys_itanal)
print(myItanalSummary[[1]])

genItems

Description

Generates a list of item parameter matrices for use in function like conquestr::genResponses and conquestr::informationWrightMap

Usage

genItems(n, scores = NULL, deltadots, taus = NULL, discrims = 1)
genItems(n, scores = NULL, deltadots, taus = NULL, discrims = 1)

Arguments

`n`	How many items?
`scores`	When NULL it is assumed that all items have integer scoring, increasing for each category k, and beginning from 0. Otherwise a list where the elements are, in order: a string naming a distribution function (for example `runif`, `rnorm`) to generate random deviates from (the scores). a list of parameters to pass to the distribution function (for example, for `runif`, a list of length 2 defining "min" and "max"). This list is assumed to be in order to be directly passed into the function. a boolean indicating whether the scores should be forced to be increasing across the response categories. optionally a vector of item numbers to apply scores too. If not provided it is assumed that all items will be scored.
`deltadots`	A list where the elements are, in order: a string naming a distribution function (for example `runif`, `rnorm`) to generate random deviates from (the delta dots). a list of parameters to pass to the distribution function (for example, for `runif`, a list of length 2 defining "min" and "max"). This list is assumed to be in order to be directly passed into the function.
`taus`	When NULL all items are assumed to be dichotomies. Otherwise a list where the elements are, in order: a string naming a distribution function (for example `runif`, `rnorm`) to generate random deviates from (the taus). Or the string "manual" to indicate that a user-defined list of the tau parameters will be provided. a list of parameters to pass to the distribution function (for example, for `runif`, a list of length 2 defining "min" and "max"). This list is assumed to be in order to be directly passed into the function. if the first element in `taus` is "manual" this should be a list of taus to be used this must be of the correct dimensions - for example, if there are 2 polytomous items being generated, each with 3 response categories (k), then the list should be of length 2: 2 * (k- 2) = 2 * (3 - 2) = 2 taus. This is because there are k-1 taus per item, and the last tau is always constrained to be the negative sum of the rest for identification purposes. a Boolean indicating whether the taus should be forced to be increasing across the response category boundaries (that is, enforce that no item exhibits disordered thresholds). optionally a vector of item numbers to produce taus for. If not provided it is assumed that all items are polytomous. optionally a vector of response categories to apply to each item. For example if the user indicates that 5 items are polytomous, then a vector of length 5 where the first elements describes the count of response categories for the first polytomous item, the second element describes the count of response categories for the second polytomous item, and so on
`discrims`	When NULL all items are assumed to have constant discrimination equal to 1. Otherwise a list where the elements are, in order: a string naming a distribution function (for example `runif`, `rnorm`) to generate random deviates from (the discriminations). Or the string "manual" to indicate that a user-defined list of the discrimination parameters will be provided. a list of parameters to pass to the distribution function (for example, for `runif`, a list of length 2 defining "min" and "max"). This list is assumed to be in order to be directly passed into the function. a Boolean indicating whether the discriminations are constant within items or whether each response category within an item will have its own score should be forced to be increasing across the response category boundaries (that is, this can be one way of specifying the Bock Nominal model). optionally a vector of items to apply a unique discrimination to. Otherwise it is assumed that all items have unique discriminations.

Value

A list of item matrices.

Examples

  myItem <- matrix(c(0, 0, 0, 0, 1, 1, 0, 1), ncol = 4, byrow = TRUE)
  myItems <- list(myItem, myItem)
  myItems[[2]][2, 2] <- -1 # make the second item delta equal to -1
  myResponses <- genResponses(abilities = rnorm(100), itemParams = myItems)
myItem <- matrix(c(0, 0, 0, 0, 1, 1, 0, 1), ncol = 4, byrow = TRUE)
  myItems <- list(myItem, myItem)
  myItems[[2]][2, 2] <- -1 # make the second item delta equal to -1
  myResponses <- genResponses(abilities = rnorm(100), itemParams = myItems)

genResponses

Description

Generates response vectors for n cases to i items given known item parameters, person abilities, and (optionally) other inputs.

Usage

genResponses(
  abilities,
  groups = NULL,
  itemParams,
  BMatrix = 1,
  mcarP = 0,
  perturbR = NULL
)
genResponses(
  abilities,
  groups = NULL,
  itemParams,
  BMatrix = 1,
  mcarP = 0,
  perturbR = NULL
)

Arguments

`abilities`	A person by latent-dimension matrix of abilities. One column per dimension.
`groups`	A vector of factors of the same length as `abilities` that allocates each case to a group. Used in perturbR. Defaults to NULL such that all cases are in the one group.
`itemParams`	A list of item parameters of the structure used in `simplef` (a matrix of k categories by four (category score, delta dot, tau, discrimination)). See conquestr::makeItemList for a helper to generate this list.
`BMatrix`	A simplified B-matrix mapping dimensions (columns) to items (rows). Or the integer "1" if items are dichotomous and ability is uni-dimensional.
`mcarP`	A double indicating the proportion of missing data under the MCAR assumption.
`perturbR`	A list of lists, where each element of the list refers to one item and contains a list of elements describing how responses to that item should be perturbed to model misfit. Each element of the list should contain, in order: item number (int). Which item in `itemParams` is affected, type of perturbation (string) to apply. One of "discrimination" - increases or decreases the discrimination of the item at a location specified by the user. "shift" - increases or decreases the location of the item as to create a uniform shift in the CCC. ...more to come, scoring perturbation factor (double). When the type is "discrimination", this defines the scale that the discrimination is increased or decreased. For example, if the item has discrimination of 1, and the perturbation factor is 1.2, the resulting probabilities will be calculated assuming the discrimination is 1 * 1.2 = 1.2. Note that is the value given here is 1, than this kind of perturbation is the same as "shift". When the type is "shift" this value is always ignored. pivot point (double), When the type is "discrimination", this defines the location around which the perturbation is applied relative to the delta dot. That is, when the type is "discrimination" and the "perturbation factor" is > 1, probabilities above the pivot point will be overestimated (generated responses will higher than expectation) and probabilities below the pivot point will be underestimated (generated responses will lower than expectation). When the pivot point is 0, this calculation happens at the item location parameter (e.g., at the category boundary). When the type is "shift", this is the value added to the item location (delta dot) as to create a uniform shift (DIF) for the group. group (string). The group found in `⁠groups that should be perturbed. note that if ⁠`groups`⁠is not used in call to⁠`genResponses' than this value is ignored and all cases' responses are perturbed.

Value

A matrix, n cases by i items, of scored item responses.

Examples

  myItem <- matrix(c(0, 0, 0, 0, 1, 1, 0, 1), ncol = 4, byrow = TRUE)
  myItems <- list(myItem, myItem)
  myItems[[2]][2, 2] <- -1 # make the second item delta equal to -1
  myResponses <- genResponses(abilities = rnorm(100), itemParams = myItems)
myItem <- matrix(c(0, 0, 0, 0, 1, 1, 0, 1), ncol = 4, byrow = TRUE)
  myItems <- list(myItem, myItem)
  myItems[[2]][2, 2] <- -1 # make the second item delta equal to -1
  myResponses <- genResponses(abilities = rnorm(100), itemParams = myItems)

getCqChain

Description

creates a data frame representation of the estimation chain from an MCMC model. The burn is discarded and only the unskipped itterations in MCMC chain are retained.

Usage

getCqChain(myCqs)
getCqChain(myCqs)

Arguments

myCqs

A system file.

Value

A data frame.

Examples

## Not run: 
getCqChain(ConQuestSys())

## End(Not run)
## Not run: 
getCqChain(ConQuestSys())

## End(Not run)

getCqData

Description

Get data objects from an R object of class ConQuestSys. This function returns person IDs, response data, case estimates, regression and weight data. Each data type is stored as a data frame, and each data frame is a named element of a list.

PID,
Responses,
Estimates,
Regression.

Usage

getCqData(mySys)
getCqData(mySys)

Arguments

mySys

An R object of class ConQuestSys, returned by the function conquestr::ConQuestSys

Value

A List of data frames.

Examples

  mySys <- ConQuestSys()
  myData <- getCqData(mySys)
mySys <- ConQuestSys()
  myData <- getCqData(mySys)

getCqDataDf

Description

Takes a list object returned by conquestr::getCqData and coerces it to a wide data frame. This can sometimes cause issues in complex data, for example where there are multiple response vectors for each case (for example a many-facets model). This is because it is assumed that the data can be reduced to a matrix of gNCases x m variables (where m is the number of id, item, estimate and regression variables in the analysis). For more complex data, the user should use the outputs of conquestr::getCqData to manually merge together a data frame.

Usage

getCqDataDf(cqData)
getCqDataDf(cqData)

Arguments

cqData

An R object of class list, returned by the function conquestr::getCqData

Value

A data frame containing R data frames based on the list objects in the ConQuest system file that has been read in.

Examples

  mySys <- ConQuestSys()
  myData <- getCqData(mySys)
  myDataDf <- getCqDataDf(myData)
mySys <- ConQuestSys()
  myData <- getCqData(mySys)
  myDataDf <- getCqDataDf(myData)

getCqFit

Description

creates a data frame representation of the fit of parameters in the item reponse model

Usage

getCqFit(myCqs)
getCqFit(myCqs)

Arguments

myCqs

A system file.

Value

A data frame.

Examples

## Not run: 
getCqFit(ConQuestSys())

## End(Not run)
## Not run: 
getCqFit(ConQuestSys())

## End(Not run)

getCqHist

Description

creates a data frame representation of the iteration history for all parameters.

Usage

getCqHist(myCqs)
getCqHist(myCqs)

Arguments

myCqs

A system file.

Value

A data frame.

Examples

## Not run: 
getCqHist(ConQuestSys())

## End(Not run)
## Not run: 
getCqHist(ConQuestSys())

## End(Not run)

getCqItanal

Description

helper function to return list of lists, each list relates to one generalised item from an ACER ConQuest itanal output. Each list contains: (1) item-total and item-rest correlations ....

Usage

getCqItanal(sysFile, matrixPrefix = "", isDebug = FALSE)
getCqItanal(sysFile, matrixPrefix = "", isDebug = FALSE)

Arguments

`sysFile`	An ACER ConQuest system file.
`matrixPrefix`	to define which itanal to process
`isDebug`	report debug output

Value

A list.

Examples

myItanal <- getCqItanal()
print(myItanal[[1]])
myItanal <- getCqItanal()
print(myItanal[[1]])

getCqItanalFacility

Description

returns an item facility for each item in itanal object created by ACER ConQuest. For a dichotomously scored Rasch-like item, facility is the percent correct. For a polytomously scored item, or with estimated scores, facility is given by: the sum of the number of cases in each response category, multiplied by the score for that category divided by the sum of all cases responding to the items times the maximum score for the item.

Usage

getCqItanalFacility(itan)
getCqItanalFacility(itan)

Arguments

itan

A list of class "cqItanal" created by conquestr::getCqItanal()

Value

A list.

Examples

mySys <- ConQuestSys()
myItan <- getCqItanal(mySys)
getCqItanalFacility(myItan)
mySys <- ConQuestSys()
myItan <- getCqItanal(mySys)
getCqItanalFacility(myItan)

getCqItanalSummary

Description

returns an itanal as a data frame in summary format: one row per generalised item with:

item label
valid N
facility (see conquestr::getCqItanalFacility)
item-rest correlation
item-total correlation
fit (infit/weighted MNSQ) if available
item locations (deltas)

Usage

getCqItanalSummary(itan)
getCqItanalSummary(itan)

Arguments

itan

A list of class "cqItanal" created by conquestr::getCqItanal()

Value

A data frame.

Examples

mySys <- ConQuestSys()
myItan <- getCqItanal(mySys)
getCqItanalSummary(myItan)
mySys <- ConQuestSys()
myItan <- getCqItanal(mySys)
getCqItanalSummary(myItan)

getCqRespModel

Description

produces a table of model parameter estimates, errors, fits, and scaled 2PL estimates if available.

Usage

getCqRespModel(sysFile)
getCqRespModel(sysFile)

Arguments

sysFile

An ACER ConQuest system file read into R using conquestr::ConQuestSys

Value

A List of data frames. Each data frame is a term in the response model

Examples

## Not run: 
myShowRespMod <- getCqRespModel(conquestr::ConQuestSys())

## End(Not run)
## Not run: 
myShowRespMod <- getCqRespModel(conquestr::ConQuestSys())

## End(Not run)

getCqTerms

Description

creates a data frame representation of the terms of the model statement, including interactions.

Usage

getCqTerms(myCqs)
getCqTerms(myCqs)

Arguments

myCqs

A system file.

Value

A data frame.

Examples

## Not run: 
getCqTerms(ConQuestSys())

## End(Not run)
## Not run: 
getCqTerms(ConQuestSys())

## End(Not run)

getCqVars

Description

creates a data frame representation of the variables in the model statement. Note that steps are not variables.

Usage

getCqVars(myCqs)
getCqVars(myCqs)

Arguments

myCqs

A system file.

Value

A data frame.

Examples

## Not run: 
getCqVars(ConQuestSys())

## End(Not run)
## Not run: 
getCqVars(ConQuestSys())

## End(Not run)

ginsOnDims

Description

returns a list of length gNDims. Each element of the list contains a vector of the gins on this dim.

Usage

ginsOnDims(sysFile)
ginsOnDims(sysFile)

Arguments

sysFile

An ACER ConQuest system file read into R using conquestr::ConQuestSys

Value

a list

Examples

## Not run: 
myResult <- ginsOnDims(conquestr::ConQuestSys())

## End(Not run)
## Not run: 
myResult <- ginsOnDims(conquestr::ConQuestSys())

## End(Not run)

informationWrightMap

Description

Plots test information function, relative to ability density, and item locations.

Usage

informationWrightMap(
  myItems,
  myAbilities,
  type = "empirical",
  minTheta = NA,
  maxTheta = NA,
  stepTheta = NA,
  scaleInfo = 1,
  plotItemPoints = "deltadots"
)
informationWrightMap(
  myItems,
  myAbilities,
  type = "empirical",
  minTheta = NA,
  maxTheta = NA,
  stepTheta = NA,
  scaleInfo = 1,
  plotItemPoints = "deltadots"
)

Arguments

`myItems`	A list of matricies describing item parameters.
`myAbilities`	A vector of person abilities on one dimension.
`type`	A character String. Should the test information be calculated empirically ("empirical" - default) or analytically using moments of distribution ("approx").
`minTheta`	The smallest value of ability PDF to plot.
`maxTheta`	The largest value of ability PDF to plot.
`stepTheta`	The increment to iterate over the ability PDF. Defaults to 0.01.
`scaleInfo`	A scaling factor to apply to the plot of tesr information. Because ability distribution is a PDF with area one, and a test information function has area L, this can make the plot more interpretable. Defaults to 1.
`plotItemPoints`	A character string indicating what item points should be plotted along the x-axis. similar to the histogram of item locations plotted on a Wrightmap. Can be "none", "deltadots", "thresholds".

Value

A ggplot2 object.

Examples

myDeltaDots <- data.frame(
  id = c(1:10),
  itemid = paste0("item", 1:10),
  delta = rnorm(10)
)
MyTaus <- data.frame(
  id = c(2L, 10L),
  itemId = NA,
  step = c(1L, 1L),
  tau = rnorm(2)
)
myItemList <- makeItemList(deltaDot = myDeltaDots, tau = MyTaus)
myInfoPlot <- informationWrightMap(myItemList, rnorm(1000, 0, 1), minTheta=-5, maxTheta=5)
myDeltaDots <- data.frame(
  id = c(1:10),
  itemid = paste0("item", 1:10),
  delta = rnorm(10)
)
MyTaus <- data.frame(
  id = c(2L, 10L),
  itemId = NA,
  step = c(1L, 1L),
  tau = rnorm(2)
)
myItemList <- makeItemList(deltaDot = myDeltaDots, tau = MyTaus)
myInfoPlot <- informationWrightMap(myItemList, rnorm(1000, 0, 1), minTheta=-5, maxTheta=5)

infoWI

Description

Calculates an index representing the product of a test information function and an ability distribution.

Usage

infoWI(myItems, myAbilities, type = "empirical")
infoWI(myItems, myAbilities, type = "empirical")

Arguments

`myItems`	A vector of item deltas.
`myAbilities`	A vector of person abilities.
`type`	A character String. Should the test information be calculated empirically ("empirical" - default) or analytically using moments of distribution ("approx").

Value

A double.

Examples

infoWIOut <- infoWI(runif (10, -2, 3), rnorm(1000, 0, 1))
infoWIOut <- infoWI(runif (10, -2, 3), rnorm(1000, 0, 1))

itemInfoAtTheta

Description

Calculates item information at a value of theta given a set of item parameters for one item.

Usage

itemInfoAtTheta(myItem, theta)
itemInfoAtTheta(myItem, theta)

Arguments

`myItem`	A matrix of item parameters of the structure used in `simplef`
`theta`	A number.

Examples

anItem <-  matrix(c(0,0,0,1,1,1,0,1), nrow = 2, byrow = TRUE)
itemInfoAtTheta(anItem, 0)
anItem <-  matrix(c(0,0,0,1,1,1,0,1), nrow = 2, byrow = TRUE)
itemInfoAtTheta(anItem, 0)

itemInfoOverTheta

Description

Calculates item information over a rage of theta given a set of item parameters. Returns a data frame with item information at a discrete set of values of theta. This is useful for plotting item information functions.

Note this function is redundant - use testInfoOverTheta and pass a single item as a list.

Usage

itemInfoOverTheta(myItem, minTheta = -6, maxTheta = 6, stepTheta = 0.1)
itemInfoOverTheta(myItem, minTheta = -6, maxTheta = 6, stepTheta = 0.1)

Arguments

`myItem`	A matrix of item parameters of the structure used in `simplef`
`minTheta`	The smallest value of ability PDF to calculate info and to plot. Defaults to -6.
`maxTheta`	The largest value of ability PDF to calculate info and to plot. Defaults to 6.
`stepTheta`	The increment to iterate over the ability PDF. Defaults to 0.01.

Examples

anItem <- matrix(c(0,0,0,1,1,1,0,1), nrow = 2, byrow = TRUE)
itemInfoOverTheta(anItem)
anItem <- matrix(c(0,0,0,1,1,1,0,1), nrow = 2, byrow = TRUE)
itemInfoOverTheta(anItem)

itemListToThresholds

Description

Taskes a list of item parameter matricies and returns a data frame containing Thurstonian Thresholds (gammas) for all items. Thurstonian thresholds are the location on the trait/scale at which the cumulative probability of being in category k, or any higher category equals some probability (usually 0.5, the default). Thurstonian thresholds are considered a way of describing the difficulty of polytomously scored items and are usually the value used in visualisations like Wright maps. Thurstonian thresholds can only be calculated for items where response categories are scored such that each category can be placed in an order increasing scores (e.g., no ties as per the Ordered Partition model)

Usage

itemListToThresholds(
  myItems,
  threshP = 0.5,
  minTheta = -20,
  maxTheta = 20,
  convC = 1e-05
)
itemListToThresholds(
  myItems,
  threshP = 0.5,
  minTheta = -20,
  maxTheta = 20,
  convC = 1e-05
)

Arguments

`myItems`	A list of item parameter matricies of the structure used in `simplef` (a matrix of k categories by four (category score, delta dot, tau, discrimination)).
`threshP`	The probability at which the thresholds are calculated (defaults to the usual value of 0.5)
`minTheta`	The lower-bound starting value of the split-half search used to find the threshold for the category.
`maxTheta`	The upper-bound starting value of the split-half search used to find the threshold for the category.
`convC`	The convergence criteria used to determine when the threshold has been found. The difference between `threshP` and the cumulative probability of the category and any higher category at the current value of theta (the current proposed value of threshold being tested).

Value

A data frame including 4 columns:

id, an integer index reflecting which item this is, in the same order as myItems
itemid, a string with the names from the items in myItems (NA if item list is not named)
step, which step does this threshold belong?
location, the value of the threshold

Examples

myItem <- matrix(
  c(
    0, -0.58    ,  0     , 1,  # delta+tau   thurst thresh (gamma)
    1, -0.58    ,  0.776 , 1,  # 0.196       -1.14
    2, -0.58    , -0.697 , 1,  # -1.277      -0.93
    3, -0.58    , -0.629 , 1,  # -1.209      -0.64
    4, -0.58    ,  0.55  , 1   # -0.03        0.25
    ), ncol =4, byrow=TRUE
 )
itemListToThresholds(list(myItem))
myItem <- matrix(
  c(
    0, -0.58    ,  0     , 1,  # delta+tau   thurst thresh (gamma)
    1, -0.58    ,  0.776 , 1,  # 0.196       -1.14
    2, -0.58    , -0.697 , 1,  # -1.277      -0.93
    3, -0.58    , -0.629 , 1,  # -1.209      -0.64
    4, -0.58    ,  0.55  , 1   # -0.03        0.25
    ), ncol =4, byrow=TRUE
 )
itemListToThresholds(list(myItem))

makeItemList

Description

creates a list of item matrices. Each matrix represent one item's set of item parameters. The structure of the matrix is the same as used in conquestr::simplef (a matrix of k categories by four (category score, delta dot, tau, discrimination)).

Usage

makeItemList(scores = NULL, deltaDot, tau = NULL, discrim = 1)
makeItemList(scores = NULL, deltaDot, tau = NULL, discrim = 1)

Arguments

`scores`	a data frame or matrix containing category scores for each item. If NULL, it is assumed increasing integer scoring starting at 0 is used for all items (that is, the first category is scored 0, the second category is scored 1, the $k^{th}$ category is scored k-1). If a data frame, column labels should be "id", "itemid", "step", "score". If a matrix, the column order should be: "id", a unique item ID for each item matched with values in `deltaDot`; "itemid", item labels for each item (or NA); "step", an indicator of which step/item category this score represents and "score" the value for the scoring parameter associated with this category. There must be one score for each category (i.e. 2 for dichotomies and one for each of k categories for polytomies). If a data frame, or a matrix: "id" is an integer "itemid" is a character string "step" is an integer "score" is numeric The original category scores (i.e., increasing integer scoring) is preserved in the rownames of the matrix.
`deltaDot`	a data frame or matrix of delta dots (average item location/difficulty for each item). If a data frame, column labels should be: "id", "itemid", "delta". "itemid" should be populated with an item label or be missing for all values. If a matrix, column order should be: "id", a unique item ID for each row; "itemid", item labels for each item (or NA); "delta", a delta dot. If a data frame, or a matrix: "id" is an integer "itemid" is a character string "delta" is numeric
`tau`	NULL if all items are dichotomies. A data frame or matrix of taus for polytomous items. Only polytomous items should be in this file. If an item ID in `deltaDot` in not in `tau` it is assumed that the item is dichotomous. The tau parameters represent the deviation from the delta dot to give the item parameters for adjacent category boundaries (e.g., delta one ( $\delta_{1} = \dot{\delta} + \tau_{1}$ ) is the boundary between $k_{1}$ and $k_{2}$ , delta two ( $\delta_{2} = \dot{\delta} + \tau_{2}$ ) is the category boundary between $k_{1}$ and $k_{2}$ ). Where a polytomous item has k categories, there should be k-2 rows for that item in `tau`. For example, a 3-category item has categories $k_{1}$ , $k_{2}$ and $k_{3}$ . There will be one value in `tau` for this item. The value in `tau` represents the the first category boundary. (e.g., between $k_{1}$ and $k_{2}$ ). The last (second in this case) category boundary is constrained to be the negative sum of the other tau values within this item (and is therefore not required in the file). If a data frame, column labels should be "id", "itemid", "step", "tau". If a matrix, the column order should be: "id", a unique item ID for each item matched with values in `deltaDot`; "itemid", item labels for each item (or NA); "step", an indicator of which step/item category this threshold represents (minimum value should be 1 and maximum value should be k-1); "tau" the value for the tau parameter associated with this step. If a data frame, or a matrix: "id" is an integer "itemid" is a character string "step" is an integer "tau" is numeric
`discrim`	a double, a data frame, or a matrix of item (or category) discrimination parameters. When a double is provided, the value is applied to all discrimination parameters. The default is 1. Setting the value to 1.7 is one approach to re-scale to the normal ogive metric. Otherwise a data.frame or matrix defining the discrimination parameter for each response category. If a data frame, column labels should be "id", "itemid", "step", "discrim". If step is NA and there is only one entry for an item "itemid", the discrimination is assumed to be constant for all response categories with the item. This is the case for names models like the GPCM and 2PL models, and can be a short hand way of defining the discrimination without specifying all categories. When discrimination varies across scoring categories, the bock-nominal model is implied. In the case of discrimination varying across scoring categories, all categories must be defined. If a data frame, or a matrix: "id" is an integer "itemid" is a character string "step" is an integer "discrim" is numeric

Value

a list.

Examples

nItems <- 10
myItemsDeltaDot <- data.frame(
  id= seq(nItems),
  itemid= NA,
  delta = runif (nItems, -4, 1) # nItems items in range -4,1
)
myItemsList <- conquestr::makeItemList(deltaDot = myItemsDeltaDot)
nItems <- 10
myItemsDeltaDot <- data.frame(
  id= seq(nItems),
  itemid= NA,
  delta = runif (nItems, -4, 1) # nItems items in range -4,1
)
myItemsList <- conquestr::makeItemList(deltaDot = myItemsDeltaDot)

plotCCC

Description

Creates a plot of an item characteristic curve (by response category). For a dichotomous item, this will yield a single curve, for polytomous items this will produce a curve for each response category. Note this is not for use with rout files. See the generic function plotRout for plotting rout files.

Usage

plotCCC(
  item,
  abilities,
  responses,
  weights = NULL,
  groups = NULL,
  range = c(-6, 6),
  by = 0.1,
  linetype = "bins",
  bins = 10,
  plotZero
)
plotCCC(
  item,
  abilities,
  responses,
  weights = NULL,
  groups = NULL,
  range = c(-6, 6),
  by = 0.1,
  linetype = "bins",
  bins = 10,
  plotZero
)

Arguments

`item`	A matrix of item parameters for a single item. Matrix should be of the form used in `simplef`
`abilities`	A vector of doubles estimated person abilities.
`responses`	A vector of integers giving the observed person responses to this item.
`weights`	A vector of doubles of sampling weights.
`groups`	A factor vector indicating groups.
`range`	Lower and upper bounds to plot over (defaults to `c(-6, 6)`).
`by`	A double. The increment to the sequence along `range` used to plot the model lines.
`linetype`	A string. Should the empirical lines be based on "bins", or "regression". Defaults to "bins"
`bins`	If linetype is "bins", how many bins should be used to chunk the empirical lines? defaults to 10. Ignored otherwise.
`plotZero`	Should the zero category be plotted? Defaults to `FALSE` when item is dichotomous and `TRUE` otherwise.

Value

A ggplot2 object.

Examples

myRout <- ConQuestRout()
myPlot <- plotRout(myRout)
## Not run: 
# if you run the above example you will have an ICC plot in the object `myPlot`.
plot(myPlot)

## End(Not run)
myRout <- ConQuestRout()
myPlot <- plotRout(myRout)
## Not run: 
# if you run the above example you will have an ICC plot in the object `myPlot`.
plot(myPlot)

## End(Not run)

plotCqHist

Description

generates a plot from a history object. Use getCqHist to create a history object from an 'ACER ConQuest' system file.

Usage

plotCqHist(
  myHist,
  centre = TRUE,
  params = c("all"),
  legend = FALSE,
  plotProblems = NULL
)
plotCqHist(
  myHist,
  centre = TRUE,
  params = c("all"),
  legend = FALSE,
  plotProblems = NULL
)

Arguments

`myHist`	an R object created by the `getCqHist` function.
`centre`	a Boolean representing whether the iteration history should be mean centred (within parameter). This is helpful for plots that include all parameters to ensure the Y axis is sensible. Consider, for example, the readability of a plot with raw values of the Likelihood and item parameters on it.
`params`	A string of which params to plot. Must be one or more of "all", "Likelihood", "Beta", Variance", "Xsi", "Tau". Note the match when using "Beta", Variance", "Xsi", "Tau" is by regular expression, so "Xsi1" will plot item location parameter 1, 10-19, 100-199 and so on.
`legend`	Should a legend be plotted?
`plotProblems`	an optional list defining which potential problem parameters to plot. Iters: The first element of the list is an integer defining how many of the final iterations to consider (e.g., identify parameters that are moving the most over the final 20 iterations). if NA, the default is to consider the last 10% of iterations. Magnitude: The second element of the list is number indicating the magnitude of change over the last n iterations. if NA, and Type is "relative", defaults to 30 times the largest change at the final iteration. if NA, and Type is "absolute", defaults to 0.05 logits. Type: The third element of the list is a string, either "relative" or "absolute": "relative" indicates that Magnitude is the multiple of the change between the final iteration and the second-to-last iteration that indicates a potential problem. "absolute" indicates that Magnitude refers to change between the the final iteration and the value in Iters that indicates a potential problem.

Value

A ggplot2 object.

Examples

## Not run: 
myHistPlot <- plotCqHist(getCqHist(ConQuestSys()))

## End(Not run)
## Not run: 
myHistPlot <- plotCqHist(getCqHist(ConQuestSys()))

## End(Not run)

plotDif

Description

Creates a plot (ggplot2 object) of item parameter estimates common to two system files (e.g., a DIF analysis).

Usage

plotDif(mySysToItemDifDf, myScale = "centred", mySuffixes)
plotDif(mySysToItemDifDf, myScale = "centred", mySuffixes)

Arguments

`mySysToItemDifDf`	An R object of class data frame returned from conquestr::sysToItemDifDf
`myScale`	A string specifying if the item parameter estimates displayed should be "centred" (default), "scaled" (z scores), or "none" (raw).
`mySuffixes`	a vector of strings specifying the names for the two groups being analysed, e.g., if the two system files are an analysis of boys and girls, the vector may be `⁠c(_male", "_female")⁠`.

Value

A ggplot2 object.

Examples

mySys1 <- ConQuestSys()
mySys2 <- ConQuestSys()
mySysList <- list(mySys1, mySys2)
myDifDf <- sysToItemDifDf(mySysList, mySuffixes = c("_male", "_female"), myDims = "all")
myDifPlot <- plotDif (myDifDf,myScale = "centred", mySuffixes = c("_male", "_female"))
## Not run: 
# if you run the above example you will have the plot in the object `myDifPlot`.
plot(myDifPlot)

## End(Not run)
mySys1 <- ConQuestSys()
mySys2 <- ConQuestSys()
mySysList <- list(mySys1, mySys2)
myDifDf <- sysToItemDifDf(mySysList, mySuffixes = c("_male", "_female"), myDims = "all")
myDifPlot <- plotDif (myDifDf,myScale = "centred", mySuffixes = c("_male", "_female"))
## Not run: 
# if you run the above example you will have the plot in the object `myDifPlot`.
plot(myDifPlot)

## End(Not run)

plotItemMap

Description

Creates a plot (ggplot2 object) of item parameter estimates and abilities on latent trait. Note this is not for use with rout files. See the method method plotRout.itemMap to the generic function plotRout

Usage

plotItemMap(mySys, myDims = "D1", ginLabs = "short", abilityType = "PV", ...)
plotItemMap(mySys, myDims = "D1", ginLabs = "short", abilityType = "PV", ...)

Arguments

`mySys`	An 'ACER ConQuest' system file object created using the conquestr::ConQuestSys function.
`myDims`	A string specifying which specific dimensions should be included. The default is "D1", Specific dimensions are specified by the label "D1" for dimensions 1 etc.
`ginLabs`	A string specifying whether short or long gin labels should be used. Default to "short".
`abilityType`	What kind of person ability estimate should be used? Defaults to plausible values. Alternatively WLE, MLE, EAP.
`...`	Optional arguments, mostly for debugging, e.g., `setDebug = TRUE` will print temporary data frames.

Value

A ggplot2 object.

Examples

mySys1 <- ConQuestSys()
myItemMap <- plotItemMap(mySys1)
## Not run: 
# if you run the above example you will have the plot in the object `myItemMap`.
plot(myItemMap)

## End(Not run)
mySys1 <- ConQuestSys()
myItemMap <- plotItemMap(mySys1)
## Not run: 
# if you run the above example you will have the plot in the object `myItemMap`.
plot(myItemMap)

## End(Not run)

plotModelCCC

Description

Creates a plot of a model implied category characteristic curve. Note this is not for use with rout files. See the generic function plotRout for plotting rout files.

Usage

plotModelCCC(item, range = c(-6, 6), by = 0.1, plotZero)
plotModelCCC(item, range = c(-6, 6), by = 0.1, plotZero)

Arguments

`item`	Item parameters for a single item.
`range`	Lower and upper bounds to plot over (defaults to c(-6, 6).
`by`	Increment to the sequence along 'range“.
`plotZero`	Should the zero category be plotted? Defaults to `FALSE` when item is dichotomous and `TRUE` otherwise.

Value

A ggplot2 object.

Examples

myItem <- matrix(
  c(
    0, 0, 0, 1,
    1, 1, 0, 1
  ),
  ncol = 4, byrow=TRUE
)
myPlot <- plotModelCCC(myItem)
myItem <- matrix(
  c(
    0, 0, 0, 1,
    1, 1, 0, 1
  ),
  ncol = 4, byrow=TRUE
)
myPlot <- plotModelCCC(myItem)

plotExpected

Description

Creates a plot of an item- or test- expected score curve. If ability estimates are provided, both empirical and model curves are produced. Can optionally handle weights and groups as required. Note this is not for use with rout files. See the generic function plotRout for plotting rout files.

Usage

plotModelExp(
  items,
  range = c(-6, 6),
  by = 0.1,
  bins = NULL,
  abilities = NULL,
  weights = NULL,
  group = NULL,
  scale = FALSE
)
plotModelExp(
  items,
  range = c(-6, 6),
  by = 0.1,
  bins = NULL,
  abilities = NULL,
  weights = NULL,
  group = NULL,
  scale = FALSE
)

Arguments

`items`	a list of one or more matrices of item parameters. Used in producing model-implied curves.
`range`	Lower and upper bounds to plot over (defaults to c(-6, 6). Used in producing model-implied curves. For empirical curves a range is chosen given the min and max values in abilities.
`by`	Increment to calculate expectation along `range`. Used in producing model-implied curves.
`bins`	A double. Optional. How many equally sized bins should abilities be broken up into? Used in producing empirical curves. If not provided and abilities are provided, a suitable value is chosen given the length of abilities.
`abilities`	A vector of doubles. Optional.
`weights`	A vector of doubles. Optional.
`group`	A vector of type factor. Optional.
`scale`	A Boolean. Whether plot should be scaled such that the Y-axis ranges from 0 to 1.

Value

A ggplot2 object.

Examples

myItem <- matrix(
  c(
    0, 0, 0, 1,
    1, 1, 0, 1
  ),
  ncol = 4, byrow=TRUE
)
myPlot <- plotModelExp(list(myItem))
myItem <- matrix(
  c(
    0, 0, 0, 1,
    1, 1, 0, 1
  ),
  ncol = 4, byrow=TRUE
)
myPlot <- plotModelExp(list(myItem))

plotRout

Description

generates a plot from an 'ACER ConQuest' Rout file. use ConQuestRout to read in an Rout file created by a plot command in 'ACER ConQuest'.

Usage

plotRout(myRout, ...)

## S3 method for class 'TestInfo'
plotRout(myRout, ...)

## S3 method for class 'InformationWithLatentDist'
plotRout(myRout, ...)

## S3 method for class 'ICC'
plotRout(myRout, ...)

## S3 method for class 'MCC'
plotRout(myRout, ...)

## S3 method for class 'TCC'
plotRout(myRout, ...)

## Default S3 method:
plotRout(myRout, ...)
plotRout(myRout, ...)

## S3 method for class 'TestInfo'
plotRout(myRout, ...)

## S3 method for class 'InformationWithLatentDist'
plotRout(myRout, ...)

## S3 method for class 'ICC'
plotRout(myRout, ...)

## S3 method for class 'MCC'
plotRout(myRout, ...)

## S3 method for class 'TCC'
plotRout(myRout, ...)

## Default S3 method:
plotRout(myRout, ...)

Arguments

`myRout`	an R object created by the `ConQuestRout` function.
`...`	additional arguments passed into plotting functions

Value

A ggplot2 object.

Examples

myRout <- ConQuestRout()
myPlot <- plotRout(myRout)
## to see why we import this, see https://ggplot2.tidyverse.org/articles/ggplot2-in-packages.html
myRout <- ConQuestRout()
myPlot <- plotRout(myRout)
## to see why we import this, see https://ggplot2.tidyverse.org/articles/ggplot2-in-packages.html

pvMeanVar

Description

Applies the law of total variance (EVEs law) to calculate the mean and variance of a set of PVs for one dimension.

Usage

pvMeanVar(myData)
pvMeanVar(myData)

Arguments

myData

A matrix of PVs for one dimension: m PVs by n cases.

Value

A list containing the mean and variance of the PVs.

q3ExpCorrect

Description

Helper function to apply correction to correlation matrix. When working with standardised residuals, the expectation of the correlations is -1/(L-1) rather than 0 See DOI: 10.1177/0013164410379322

Usage

q3ExpCorrect(myCorMat)
q3ExpCorrect(myCorMat)

Arguments

myCorMat

A correlation matrix.

Value

A correlation matrix with the Q3 statistic correction applied.

ReadSys

Description

Internal function to read an 'ACER ConQuest' system file. Called by conquestr::ConQuestSys.

Usage

ReadSys(myFile, isMini)
ReadSys(myFile, isMini)

Arguments

`myFile`	An connection to an 'ACER ConQuest' system file created by the `put` command in 'ACER ConQuest'. If the file is compressed, and uncompressed temporary file is created.
`isMini`	A boolean, set to TRUE if the system file is a mini system file created by 'ACER ConQuest' command put with option "mini = yes".

Value

A list containing the data objects created by 'ACER ConQuest'.

ReadSysMini

Description

Internal function to read an 'ACER ConQuest' system file. Called by conquestr::ConQuestSys.

Usage

ReadSysMini(myFile, Dimensions, N, NPlausibles, isDebug)
ReadSysMini(myFile, Dimensions, N, NPlausibles, isDebug)

Arguments

`myFile`	An 'ACER ConQuest' mini system file created by the `put` command in 'ACER ConQuest' with the option "mini = yes". The put command must use the option `compressed = no`.
`Dimensions`	.
`N`	.
`NPlausibles`	.
`isDebug`	.

Value

A list containing the data objects created by 'ACER ConQuest'.

recodeResps

Description

Recode raw item responses for analyses.

Usage

recodeResps(data, recodeMap, varLabel, rawLabel, recodeLabel)
recodeResps(data, recodeMap, varLabel, rawLabel, recodeLabel)

Arguments

`data`	Raw data, a data frame.
`recodeMap`	A data frame which contains the raw responses and corresponding recoded responses for each of the items in long form.
`varLabel`	A variable name in recodeMap that identifes the item label.
`rawLabel`	A variable name in recodeMap that identifies the raw item responses to be recoded.
`recodeLabel`	A variable name in recodeMap that idenitfies the new values to recode to.

Value

a data frame with raw data recoded according to recodeMap.

iterate through a data frame and use replaceInVector

Description

iterate through a data frame and use replaceInVector

Usage

replaceInDataFrame(d, r, x)
replaceInDataFrame(d, r, x)

Arguments

`d`	A DataFrame.
`r`	A double - the value to be replaced if it is < -1e300.
`x`	A double - the value to replace r with.

replace a very large neagtive number with something - usually NA_REAL

Description

replace a very large neagtive number with something - usually NA_REAL

Usage

replaceInVector(v, r, x)
replaceInVector(v, r, x)

Arguments

`v`	A NumericVector.
`r`	A double - the value to be replaced if it is < -1e300.
`x`	A double - the value to repalce r with.

searchConQuestSys

Description

Search for object names within a ConQuest System file object.

Usage

searchConQuestSys(searchString, mySys, value = TRUE, ignore.case = TRUE)
searchConQuestSys(searchString, mySys, value = TRUE, ignore.case = TRUE)

Arguments

`searchString`	A string to search within the names of mySys.
`mySys`	An 'ACER ConQuest' system file object created using the conquestr::ConQuestSys function.
`value`	Should searchConQuestSys return the name of the object or its index.
`ignore.case`	Should searchConQuestSys ignore the case of the search term.

Value

a string including object names mathching the search term

steigerStat

Description

Function to cacluate the Steiger statistic. The Steiger statistic is a test of independance of the standardised residuals ((O-E)/sqrt(Var(E))), where Var(E) = p(x)/(1-p(x)).

Usage

steigerStat(myDat, q3Adj = TRUE, fisher = TRUE, dfAdj = FALSE, tpm)
steigerStat(myDat, q3Adj = TRUE, fisher = TRUE, dfAdj = FALSE, tpm)

Arguments

`myDat`	A data frame or matrix containing standardised residuals.
`q3Adj`	A bool indicating whether the Q3 correction should be applied.
`fisher`	A bool indicating whether the Fisher Transform should be applied.
`dfAdj`	A bool indicating whether the df should be adjusted for sample size, L, and targeting. If dfAdj is TRUE, then you must pass in the optional argument `tmp` (test-person match)
`tpm`	A number indicating the test-person match, where 0 indicates that mean item difficulty is equal to mean person ability, and -1 indcates that mean item difficulty is 1 logit below mean person ability.

Value

A list of class "steigerStat" with the Steiger Statistic, correlation matrix, and chi square test.

summariseCqChain

Description

takes a data frame created by getCqChain and returns a list reporting the mean and variaince for each parameter

Usage

summariseCqChain(myChain)
summariseCqChain(myChain)

Arguments

myChain

A data frame returned from getCqChain.

Value

A list.

Examples

## Not run: 
summariseCqChain(getCqChain(ConQuestSys()))

## End(Not run)
## Not run: 
summariseCqChain(getCqChain(ConQuestSys()))

## End(Not run)

sysFileOk

Description

checks

Usage

sysFileOk(sysFile, defaultSys)
sysFileOk(sysFile, defaultSys)

Arguments

`sysFile`	An ACER ConQuest system file read into R using conquestr::ConQuestSys
`defaultSys`	A Boolean indicating if sysFile is the defualt system file created by an empty call to conquestr::ConQuestSys

Examples

## Not run: 
sysFileOkResult <- sysFileOk(conquestr::ConQuestSys())

## End(Not run)
## Not run: 
sysFileOkResult <- sysFileOk(conquestr::ConQuestSys())

## End(Not run)

sysToBMatrixDf

Description

Read an R object of class ConQuestSys and create a labelled representation of the B matrix (scoring matrix). This maps item response categories to items and dimensions. Returns long data frame, where items are duplicated if they are in many dimensions.

Usage

sysToBMatrixDf(mySys, applyLabels = TRUE)
sysToBMatrixDf(mySys, applyLabels = TRUE)

Arguments

`mySys`	An R object of class ConQuestSys, returned by the function conquestr::ConQuestSys
`applyLabels`	A bool indicating whether labels (e.g., dimension labels) should be appended.

Value

A data frame containing R the labelled B matrix.

Examples

myBMatrix <- sysToBMatrixDf(ConQuestSys())
## Not run: 
# if you run the above example you will have the B Matrix from the example system file.
str(myBMatrix)

## End(Not run)
myBMatrix <- sysToBMatrixDf(ConQuestSys())
## Not run: 
# if you run the above example you will have the B Matrix from the example system file.
str(myBMatrix)

## End(Not run)

sysToItemDifDf

Description

Creates a data frame that includes the common item parameter estimates from two (or more) system files (e.g., a DIF analysis).

Usage

sysToItemDifDf(listOfSysFiles, mySuffixes, myDims = "all")
sysToItemDifDf(listOfSysFiles, mySuffixes, myDims = "all")

Arguments

`listOfSysFiles`	A list of system files returned from conquestr::ConQuestSys
`mySuffixes`	a vector of strings specifying the names for the two groups being analysed, e.g., if the two system files are an analysis of boys and girls, the vector may be `⁠c(_male", "_female")⁠`.
`myDims`	A string specifying if all or specific dimensions should be included. The default is "all", Specific dimensions are specified by the label "D1" for dimensions 1 etc.

Value

A data frame object.

testInfoAtTheta

Description

Calculates test information at a value of theta given a list of matricies of item parameters for one or more items.

Usage

testInfoAtTheta(myItems, theta)
testInfoAtTheta(myItems, theta)

Arguments

`myItems`	A list of matrices of item parameters of the structure used in `simplef`
`theta`	a number.

Examples

anItem <- matrix(c(0,0,0,1,1,1,0,1), nrow = 2, byrow = TRUE)
testInfoAtTheta(list(anItem), 0)
anItem <- matrix(c(0,0,0,1,1,1,0,1), nrow = 2, byrow = TRUE)
testInfoAtTheta(list(anItem), 0)

testInfoOverTheta

Description

Calculates test information over a range of theta given a list of matricies of item parameters for one or more items. Returns a data frame with item information at a discrete set of values of theta. This is useful for plotting test information functions.

Usage

testInfoOverTheta(myItems, minTheta = -6, maxTheta = 6, stepTheta = 0.1)
testInfoOverTheta(myItems, minTheta = -6, maxTheta = 6, stepTheta = 0.1)

Arguments

`myItems`	a list of item parameters of the structure used in `simplef`
`minTheta`	The smallest value of ability PDF to calculate info and to plot. Defaults to -6.
`maxTheta`	The largest value of ability PDF to calculate info and to plot. Defaults to 6.
`stepTheta`	The increment to iterate over the ability PDF. Defaults to 0.01.

Examples

anItem <- matrix(c(0,0,0,1,1,1,0,1), nrow = 2, byrow = TRUE)
testInfoOverTheta(list(anItem))
anItem <- matrix(c(0,0,0,1,1,1,0,1), nrow = 2, byrow = TRUE)
testInfoOverTheta(list(anItem))

thrstThrsh

Description

Generates Thurstonian Thresholds (sometimes called gammas) to an item. Thurstonian thresholds are the location on the trait/scale at which the cumulative probability of being in category k, or any higher category equals some probability (usually 0.5, the default). Thurstonian thresholds are considered a way of describing the difficulty of polytomously scored items and are usually the value used in visualisations like Wright maps. Thurstonian thresholds can only be calculated for items where response categories are scored such that each category can be placed in an order increasing scores (e.g., no ties as per the Ordered Partition model)

Usage

thrstThrsh(myItem, threshP = 0.5, minTheta = -20, maxTheta = 20, convC = 1e-05)
thrstThrsh(myItem, threshP = 0.5, minTheta = -20, maxTheta = 20, convC = 1e-05)

Arguments

`myItem`	A matrix of parameters for a single item of the structure used in `simplef` (a matrix of k categories by four (category score, delta dot, tau, discrimination)).
`threshP`	The probability at which the threshold is calculated (defaults to the usual value of 0.5)
`minTheta`	The lower-bound starting value of the split-half search used to find the threshold for the category.
`maxTheta`	The upper-bound starting value of the split-half search used to find the threshold for the category.
`convC`	The convergence criteria used to determine when the threshold has been found. The difference between `threshP` and the cumulative probability of the category and any higher category at the current value of theta (the current threshold being tested).

Value

A k-1 by 1 matrix with Thurstonian thresholds for this item. Values are NA when the threshold cannot be calculated.

Examples

myItem <- matrix(
  c(
    0, -0.58    ,  0     , 1,  # delta+tau   thurst thresh (gamma)
    1, -0.58    ,  0.776 , 1,  # 0.196       -1.14
    2, -0.58    , -0.697 , 1,  # -1.277      -0.93
    3, -0.58    , -0.629 , 1,  # -1.209      -0.64
    4, -0.58    ,  0.55  , 1   # -0.03        0.25
    ), ncol =4, byrow=TRUE
 )
thrstThrsh(myItem)
myItem <- matrix(
  c(
    0, -0.58    ,  0     , 1,  # delta+tau   thurst thresh (gamma)
    1, -0.58    ,  0.776 , 1,  # 0.196       -1.14
    2, -0.58    , -0.697 , 1,  # -1.277      -0.93
    3, -0.58    , -0.629 , 1,  # -1.209      -0.64
    4, -0.58    ,  0.55  , 1   # -0.03        0.25
    ), ncol =4, byrow=TRUE
 )
thrstThrsh(myItem)

transformPvs

Description

Helper function to Transform PVs onto a new metric (e.g., PISA Mean = 500, SD = 100). Uses the method described in the PISA 2012 technical manual.

Usage

transformPvs(data, mT = 0, sdT = 1, weights = 1)
transformPvs(data, mT = 0, sdT = 1, weights = 1)

Arguments

`data`	A data frame or matrix that contains the PVs
`mT`	The desired mean of the PVs
`sdT`	The desired sd of the PVs
`weights`	a vector of weights, the same length as `data[1]` used to calculate the mean and SD across the PVs

Value

a List of transformed PVs with as many elements as PVs were listed in 'x'.

writeImportXsi

Description

Writes a fixed width text file in the format required for the ACER ConQuest command and argument ⁠import anchor_xsi⁠. Can also be used for initial values, though caution should be used with the interpretation of the argument lconstraint which should relate to the model of interest ACER ConQuest

Currently only works with implicit variables. Explicit variables may be added in the future.

Usage

writeImportXsi(items, bmatix = 1L, lconstraint = "none", file)
writeImportXsi(items, bmatix = 1L, lconstraint = "none", file)

Arguments

`items`	a list of item matrices
`bmatix`	either the integer 1L for a unidimensional model, or a matrix, items by dimensions with 1L representing that the item in on this dimension, and a 0 otherwise.
`lconstraint`	the identification constraint in use, one of "none", "items", or "cases".
`file`	a path and filename to write file to disk. #' @return invisibly returns path of file written to disk)

Examples

  myItem <- matrix(c(0, 0, 0, 0, 1, 1, 0, 1), ncol = 4, byrow = TRUE)
  myItems <- list(myItem, myItem)
  myItems[[2]][2, 2] <- -1 # make the second item delta equal to -1
  myResponses <- genResponses(abilities = rnorm(100), itemParams = myItems)
myItem <- matrix(c(0, 0, 0, 0, 1, 1, 0, 1), ncol = 4, byrow = TRUE)
  myItems <- list(myItem, myItem)
  myItems[[2]][2, 2] <- -1 # make the second item delta equal to -1
  myResponses <- genResponses(abilities = rnorm(100), itemParams = myItems)

Package 'conquestr'

Help Index

checkItemRespValid

Description

Usage

Arguments

Value

checkVars

Description

Usage

Arguments

Value

cnvrtItemParam

Description

Usage

Arguments

Value

Examples

ConQuestCall

Description

Usage

Arguments

Value

Examples

ConQuestRout

Description

Usage

Arguments

Value

Examples

ConQuestSys

Description

Usage

Arguments

Value

Examples

createConQuestProject

Description

Usage

Arguments

Value

Examples

DecompressSys

Description

Usage

Arguments

Value

See Also

fisherTrnsfrm

Description

Usage

Arguments

Value

fmtCqItanal

Description

Usage

Arguments

Value

Examples

genItems

Description

Usage

Arguments

Value

See Also

Examples

genResponses

Description

Usage

Arguments

Value

See Also

Examples

getCqChain

Description

Usage

Arguments

Value

Examples

getCqData