Package 'ifaTools'

Adds exploratory factors to a single factor model

Description

Adds exploratory factors to a single factor model

Usage

addExploratoryFactors(model, toAdd, ..., addUniquenessPrior = TRUE)

Arguments

model	a single factor (possibly multigroup) model
toAdd	the number of factors to add
...	Not used. Forces remaining arguments to be specified by name.
addUniquenessPrior	whether to add a uniqueness prior to the model (default TRUE)

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Plot expected and observed table from SitemFit

Description

WARNING: This function is under development. The API may change in a future release.

Usage

iccPlot(grp, itemName, ..., width = 3, dataBins = 11, basis = c(1), factor = 1)

Arguments

grp	an IFA group
itemName	name of item to plot
...	Not used. Forces remaining arguments to be specified by name.
width	sets the x axis to [-width,width]
dataBins	number of partitions for the latent scores
basis	the basis vector in the latent space
factor	the score to use (TODO: should be a function of the basis vector?)

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A Shiny app to experiment with item models

Description

A Shiny app to experiment with item models

Usage

itemModelExplorer()

Examples

## Not run: 
itemModelExplorer()  # will launch a browser in RStudio

## End(Not run)

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Create item response map table

Description

Categories are placed at the mean score of the examinees who picked that category.

Usage

itemResponseMap(grp, ..., factor = 1)

Arguments

grp	an IFA group
...	Not used. Forces remaining arguments to be specified by name.
factor	which factor to plot (defaults to 1)

Value

A data.frame of the raw data backing the plot. Item outcomes without any observations are omitted.

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A Shiny app for building IFA models

Description

A Shiny app for building IFA models

Usage

modelBuilder()

Examples

## Not run: 
modelBuilder()  # will launch a browser in RStudio

## End(Not run)

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Plot item information in the latent distribution

Description

For multidimensional items, you will need to supply a basis vector. This vector is normalized to unit length.

Usage

plotInformation(grp, ..., width = 3, showTotal = FALSE, basis = c(1))

Arguments

grp	an IFA group
...	Not used. Forces remaining arguments to be specified by name.
width	the plot will span from -width to width
showTotal	whether to plot the total item information
basis	the basis vector (for multidimensional items)

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Replicate a model for each group of data

Description

The reference group is fixed to a zero mean and identity covariance matrix.

Usage

replicateModelBy(
  tmpl,
  fullData,
  mMat,
  covMat,
  ...,
  splitCol = "population",
  refGroup = "general",
  split = TRUE,
  compressData = TRUE
)

Arguments

tmpl	an OpenMx model
fullData	the complete data including the column indicating group membership
mMat	an MxMatrix for latent means
covMat	an MxMatrix for latent covariance
...	Not used. Forces remaining arguments to be specified by name.
splitCol	the name of the column used to indicate group membership
refGroup	the name of the reference group
split	whether to split the data (defaults to TRUE)
compressData	whether to apply compressDataFrame (defaults to TRUE)

---

Plot expected and observed table from SitemFit

Description

Plot expected and observed table from SitemFit

Usage

SitemPlot(sout, itemName, ..., showSampleSize = TRUE)

Arguments

sout	output from SitemFit
itemName	name of item to plot
...	Not used. Forces remaining arguments to be specified by name.
showSampleSize	whether to show the sample size at the top of the plot

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Uniqueness prior to assist in item factor analysis

Description

To prevent Heywood cases, Bock, Gibbons, & Muraki (1988) suggested a beta prior on the uniqueness (Equations 43-46). The analytic gradient and Hessian are included for quick optimization using Newton-Raphson.

Usage

uniquenessPrior(model, numFactors, strength = 0.1, name = "uniquenessPrior")

Arguments

model	an mxModel
numFactors	the number of factors. All items are assumed to have the same number of factors.
strength	the strength of the prior
name	the name of the mxModel that is returned

Details

To reproduce these derivatives in maxima for the case of 2 slopes (c and d), use the following code:

f(c,d) := -p*log(1-(c^2 / (c^2+d^2+1) + (d^2 / (c^2+d^2+1))));

diff(f(c,d), d),radcan;

diff(diff(f(c,d), d),d),radcan;

The general pattern is given in Bock, Gibbons, & Muraki.

Value

an mxModel that evaluates to the prior density in deviance units

References

Bock, R. D., Gibbons, R., & Muraki, E. (1988). Full-information item factor analysis. Applied Psychological Measurement, 12(3), 261-280.

Examples

numItems <- 6
spec <- list()
spec[1:numItems] <- list(rpf.drm(factors=2))
names(spec) <- paste0("i", 1:numItems)
item <- mxMatrix(name="item", free=TRUE,
                 values=mxSimplify2Array(lapply(spec, rpf.rparam)))
item$labels[1:2,] <- paste0('p',1:(numItems * 2))
data <- rpf.sample(100, spec, item$values)  # use a larger sample size
m1 <- mxModel(model="m1", item,
              mxData(observed=data, type="raw"),
              mxExpectationBA81(spec),
              mxFitFunctionML())
up <- uniquenessPrior(m1, 2)
container <- mxModel("container", m1, up,
                     mxFitFunctionMultigroup(c("m1", "uniquenessPrior")),
                     mxComputeSequence(list(
                       mxComputeOnce('fitfunction', c('fit','gradient')),
                       mxComputeReportDeriv())))
container <- mxRun(container)
container$output$fit
container$output$gradient

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Univariate priors commonly used in IFA models

Description

The returned model evaluates to the fit of the priors in deviance (-2 log likelihood) units. The analytic gradient and Hessian are included for quick optimization using Newton-Raphson.

Usage

univariatePrior(type, labels, mode, strength = NULL, name = "univariatePrior")

Arguments

type	one of c("lnorm","beta","logit-norm")
labels	a vector of parameters to which to apply the prior density
mode	the mode of the prior density
strength	a prior-specific strength (optional)
name	the name of the mxModel returned

Details

Priors of type 'beta' and 'logit-norm' are commonly used for the lower asymptote parameter of the 3PL model. Both of these priors assume that the parameter is in logit units. The 'lnorm' prior can be used for slope parameters.

Value

an mxModel that evaluates to the prior density in deviance units

Examples

model <- univariatePrior("logit-norm", "x1", -1)
model$priorParam$values[1,1] <- -.6
model <- mxRun(model)
model$output$fit
model$output$gradient
model$output$hessian

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