| Date: | 2022-01-19 |
| Title: | Toolkit for Item Factor Analysis with 'OpenMx' |
| Version: | 0.23 |
| Description: | Tools, tutorials, and demos of Item Factor Analysis using 'OpenMx'. This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>. |
| License: | AGPL (≥ 3) |
| VignetteBuilder: | knitr |
| Imports: | methods |
| Suggests: | testthat, roxygen2, knitr (≥ 1.8), gridExtra, plyr, xtable |
| Depends: | shiny (≥ 0.10), ggplot2, reshape2, rpf (≥ 0.48), OpenMx (≥ 2.3.1), R (≥ 2.14.0) |
| URL: | https://github.com/jpritikin/ifaTools |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2022-01-19 21:06:57 UTC; joshua |
| Author: | Joshua N. Pritikin [cre, aut] |
| Maintainer: | Joshua N. Pritikin <jpritikin@pobox.com> |
| Repository: | CRAN |
| Date/Publication: | 2022-01-19 22:12:42 UTC |
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 |
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) |
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?) |
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)
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.
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)
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) |
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) |
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
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