Changes in mirt 1.5
MAJOR CHANGES
- for efficiency, the Hessian is no longer computed in `fscores()` unless
it is required in the returned object
- estimation with `method = 'MHRM'` now requires and explicity `SE=TRUE`
call to compute the information matrix. The matrix is now computed using
the ML estimates rather than approximated sequentially after each iteration
(very unstable), and therefore a seperate stage is performed. This provides
much better accuracy in the computations
NEW FEATURES
- new `extract.group()` function to extract a single group object from an
objects previously returned by `multipleGroup()`
- return the SRMSR statistic in `M2()` along with the residual matrix
(suggested by Dave Flora)
- accept `Etable` default input in `customPriorFun` (suggested by Alexander
Robitzsch)
- vignette files for the package examples are now hosted on Github and
can be accessed by following the link mentioned in the vignette location
in the index or `?mirt` helpfile
- E-step is now computed in parallel (if available) following a
`mirtCluster()` definition
- run no M-step optimizations by passing `TOL = NaN`. Useful to have
the model converge instantly with all parameters exactly equal to the
starting values
- confidence envelope plots in `itemplot()` generate shaded regions instead
of dotted lines, and confidence interval plots added to `plot()` generic
through the `MI` input
- passes to `fscores()` slightly more optimized for upcoming mirtCAT
package release
- `method = 'EAPsum'` argument to `fscores()` support for multidimensional
models
BUG FIXES
- fix forcing all SEs MHRM information matrix computations to be positive
- `imputeMissing()` crash fix for multiple-group models
- fix divide-by-0 bug in the E-step when number of items is large
- fix crash in EM estimation with `SE.type = 'MHRM'`
Changes in mirt 1.4
MAJOR CHANGES
- calculating the information matrix for exploratory item factor analysis
models has been disabled since the rotational indeterminacy of the model
results in improper parameter variation
- changed default `theta_lim` to `c(-6,6)` and number of quadrature defaults
increased as well
- `@Data` slot added for organizing data based arguments. Removed several
data slots from estimated objects as a consequence
- removed 'Freq' column when passing a `response.pattern` argument to
`fscores()`
- increase number of Mstep iterations proportionally in quasi-Newton
algorithms as the estimation approaches the ML location
- 'rsm' itemtype removed for now until optimized version is implemented
NEW FEATURES
- link to `mirt` vignettes on Github have been registered with the `knitr`
package and are now available through the package index
- `optimizer` argument added to estimation function to switch the default
optimizer. Multiple optimizers are now available, including the BFGS
(EM default), L-BFGS-B, Newton-Raphson, Nelder-Mead, and SANN
- new `survey.weights` argument can be passed to parameter estimation
functions (i.e., `mirt()`) to apply so-called stratification/survey-weights
during estimation
- `returnList` argument added to `simdata()` to return a list containing
the S4 item objects, Theta matrix, and simulated data
- support custom item type `fscores()` computations when `response.pattern`
is passed instead of the original data
- `impute` option for `itemfit()` and `M2()` to estimate statistics via
plausible imputation when missing data are present
- multidimensional ideal-point models added for dichotomous items
- M2* statistic added for polytomous item types
- Bock and Lieberman (`'BL'`) method argument added (not recommend for
serious use)
BUG FIXES
- large bias correction in information matrix and standard errors for
models that contain equality constraints (standard errors were too high)
- drop dimensions fix for nested logit models
Changes in mirt 1.3
MAJOR CHANGES
- default `SE.type` changed to `crossprod` since it is better at detecting
when models are not identified compared to `SEM`, and is generally much
cheaper to compute for larger models
- M-step optimizer now automatically selected to be 'BFGS' if there are
no bounded parameters, and 'L-BFGS-B' otherwise. Some models will have
notably different parameter estimates because of this, but should have
nearly identical model log-likelihoods
- better shiny UI which adapts to the itemtype specifically, and allows
for classical parameter inputs (special thanks to Jonathan Lehrfeld for
providing code that inspired both these changes)
- scores.only option now set to `TRUE` in `fscores()`
- `type = 'score'` for plot generics no longer adjusts the categories for
expected test scores
- M-step optimizer in EM now deters out-of-order graded response model
intercepts (was a problem if the startvalues were too far from the ML
estimate in graded models)
NEW FEATURES
- `return.acov` logical added to `fscores()` to return a list of matrices
containing the ACOV theta values used to compute the SEs (suggested by
Shiyang Su)
- `printCI` logical option to `summary()` to print confidence intervals
for standardized loadings
- new `expected.test()` function, which is an extension of `expected.item()`
but for the whole test
- `mirt.model()` syntax supports multiple * combinations in `COV = ` for
more easily specifying covariation blocks between factors. Also allows
variances to be freed by specifying the same factor name, e.g., `F*F`
- `full.scores.SE` logical option for `fscores()` to return standard errors
for each respondent
- multiple imputation (MI) option in `fscores()`, useful for obtaining less
biased factor score estimates when model parameter variability is large
(usually due to smaller sample size)
- group-level (i.e., means/covariances) equality constrains are now
available for the EM algorithm
- `theta_lim` input to `plot()`, `itemplot()`, and `fscores()` for modifying
range of latent values evaluated
BUG FIXES
- `personfit()` crash for multipleGroup objects since itemtype slot was
not filled (reported by Michael Hunter)
- fix crash in two-tier models when correlations are estimated (reported
by David Wu)
- R 3.1.0 appears to evaluate List objects differently at the c level
causing strange behaviour, therefore slower R versions of some internal
function (such as mirt:::reloadPars()) will be used until a patch is formed
- behaviour of `mvtnorm::dmvnorm` changed as of version 0.9-9999, causing
widely different convergence results. Similar versions of older mvtnorm
functions are now implemented instead
Changes in mirt 1.2.1
MAJOR CHANGES
- `fitIndices()` replaced with `M2()` function, and currently limited to
only dichotomous items of class 'dich'
- `bfactor()` default SE.type set to 'crossprod' rather than 'SEM'
- generalized partial credit models now display fixed scoring coefs
- `TOL` convergence criteria moved outside of the `technical` input to
its own argument
- `restype` argument to `residuals()` changed to `type` to be more consistent
with the package
- removed `fitted()` since `residuals(model, type = 'exp')` gives essentially
the same output
- mixedmirt has `SE` set to `TRUE` by default to help construct a more
accurate information matrix
- if not specified, S-EM `TOL` dropped to `1e-6` in the EM, and `SEtol =
.001` for each parameter to better approximate the information matrix
NEW FEATURES
- two new `SE.type` inputs: 'Louis' and 'sandwich' for computing Louis'
1982 computation of the observed information matrix, and for the sandwich
estimate of the covariance matrix
- `as.data.frame` logical option for `coef()` to convert list to a
row-stacked data.frame
- `type = 'scorecontour'` added to `plot()` for a contour plot with the
expected total scores
- `type = 'infotrace'` added to `itemplot()` to plot trace lines and
information on the same plot, and `type = 'tracecontour'` for a contour
plot using trace lines (suggested by Armi Lantano)
- `mirt.model()` support for multi-line inputs
- new `type = 'LDG2'` input for `residuals()` to compute local dependence
stat based on G2 instead of X2, and `type = 'Q3'` added as well
- S-EM computation of the information matrix support for latent parameters,
which previously was only effective when estimation item-level parameters. A
technical option has also been added to force the information matrix to
be symmetric (default is set to `TRUE` for better numerical stability)
- new `empirical.CI` argument in `itemfit()` used when plotting confidence
intervals for dichotomous items (suggested by Okan Bulut)
- `printSE` argument can now be passed to `coef()` for printing the
standard errors instead of confidence intervals. As a consequence, `rawug`
is automatically set to `TRUE` (suggested by Olivia Bertelli)
- second-order test and condition number added to estimated objects when
an information matrix is computed
- `tables` argument can be passed to `residuals()` to return all observed
and expected tables used in computing the LD statistics
BUG FIXES
- using `scores.only = TRUE` for multipleGroup objects returns the correct
person ordering (reported by Mateusz Zoltak)
- `read.mirt()` crash fix for multiple group analyses objects (reported
by Felix Hansen)
- updated math for `SE.type = 'crossprod'`
Changes in mirt 1.1
NEW FEATURES
- `facet_items` argument added to plot() to control whether separate plots
should be constructed for each item or to merge them onto a single plot
- three dimensional models supported in `itemplot()` for types `trace`,
`score`, `info`, and `SE`
- new DIF() function to quicky calculate common differential item functioning
routines, similar to how IRTLRDIF worked. Supports likelihood ratio testings
as well as the Wald approach, and includes forward and backword sequential
DIF searching methods
- added a `shiny = TRUE` option to `itemplot()` to run the interactive
shiny applet. Useful for instructive purposes, as well as understanding
how the internal parameters of mirt behave
- `type = 'trace'` and `type = 'infotrace'` support added to `plot`
generic for multiple group objects
- `fscores(..., method = 'EAPsum')` returns observed and expected values,
along with general fit statistics that are printed to the console and
returned as a 'fit' attribute
- removed multinomial constant in log-likelihood since it has no influence
on nested model comparisons
- `SE.type = 'crossprod'` and `Fisher` added for computing the parameter
information matrix based on the variance of the Fisher scoring vector and
complete Fisher information matrix, respectively
- `customPriorFun` input to technical list now available for utilizing
user defined prior distribution functions in the EM algorithm
- empirical histogram estimation now available in `mirt()` and
`multipleGroup()` for unidimensional models. Additional plot `type =
'empiricalhist'` also added to the `plot()` generic
- re-implement `read.mirt()` with better consistency checking between the
`plink` package
BUG FIXES
- starting values for `multipleGroup()` now returns proper estimated
parameter information from the `invariance` input argument
- remove `as.integer()` in MultipleGroup df slot
- pass proper item type when using custom pattern calles in `fscores()`
- return proper object in personfit when gpcm models used
Changes in mirt 1.0
NEW FEATURES
- `GenRandomPars` logical argument now supported in the `technical = list()`
input. This will generate random starting values for freely estimated
parameters, and can be helpful to determine if obtained solutions are
local minimums
- seperate `free_var` and `free_cov` invariance options available in
multipleGroup
- new `CONSTRAIN` and `CONSTRAINB` arguments in `mirt.model()` syntax for
specifying equality constraints explicitly for parameters accross items
and groups. Also the `PRIOR = ...` specification was brought back and uses
a similar format as the new CONSTRAIN options
- `plot(..., type = 'trace')` now supports polytomous and dichotomous
tracelines, and `type = 'infotrace'` has a better y-axis range
- removed the '1PL' itemtype since the name was too ambiguous. Still
possible to obtain however by applying slope constraints to the 2PL/graded
response models
- `plot()` contains a which.items argument to specify which items to plot
in aggregate type, such as `'infotrace'` and `'trace'`
- `fitIndicies()` will return `CFI.M2` and `TLI.M2` if the argument
`calcNull = TRUE` is passed. CFI stats also normed to fall between 0 and 1
- data.frame returned from `mod2values()` and `pars = 'values'` now contains
a column indicating the internal item class
- use `ginv()` from MASS package to improve accuracy in `fitIndices()`
calculation of M2
BUG FIXES
- fix error thrown in `PLCI.mirt` when parameter value is equal to the bound
- fix the global df values, and restrict G2 statistic when tables are
too sparse
Changes in mirt 0.9.0
NEW FEATURES
- `PLCI.mirt()` function added for computing profiled likelihood standard
errors. Currently only applicable to unidimensional models
- prior distributions returned in the `pars = 'values'` data.frame along
with the input parameters, and can be edited and returned as well
- full.scores option for `residuals()` to compute residuals for each row
in the original data
- `bfactor()` can include an additional model argument for modeling two-tier
structures introduced by Cai (2010), and now supports a `'group'` input
for multiple group analyses
- added a general Ramsey (1975) acceleration to EM estimation by default. Can
be disable with `accelerate = FALSE` (and is done so automatically when
estimating SEM standard errors)
- renamed response.vector to response.pattern in `fscores()`, and now
supports matrix input for computing factor scores on larger data sets
(suggested by Felix Hansen)
- total.info logical added to `iteminfo()` to return either total item
information or information from each category
- `mirt.model()` supports the so-called Q-matrix input format, along with
a matrix input for the covariance terms
- MH-RM algorithm now accessible by passing `mirt(..., method = 'MHRM')`,
and `confmirt()` function removed completely. `confmirt.model()` also
renamed to `mirt.model()`
- support for polynomial and interaction terms in EM estimation
- lognormal priors may now be passed to parprior
- iterative computations in `fscores()` can now be run in parallel
automatically following a `mirtCluster()` definition
- `mirtCluster()` function added to make utilizing parallel cores more
convenient. Globally removed the cl argument for multi-core objects
- updated documentation for data sets by adding relevant examples, and
added Bock1997 data set for replicating table 3 in van der Linden, W. J. &
Hambleton, R. K. (1997) Handbook of modern item response theory
- general speed improvements in all functions
BUG FIXES
- WLE estimation fixed and now estimates extreme response patterns
- exploratory starting values no longer crash in datasets with a huge
number of NAs, which caused standard deviations to be zero
- math fix for beta priors
Changes in mirt 0.8.0
NEW FEATURES
- support for random effect predictors now available in `mixedmirt()`,
along with a `randef()` function for computing MAP predictions for the
random parameters
- EAPsum support in `fscores()` for mixed item types
- for consistency with current IRT software (rather than TESTFACT and
POLYFACT), the scaling constant has been set to D = 1 and fixed at this value
- nominal.highlow option added to specify which categories are the highest
and lowest in nominal models. Often provide better numerical stability
when utilized. Default is still to use the highest and lowest categories
- increase number of draws in the Monte Carlo calculation of the
log-likelihood from 3000 to 5000
- when itemtype all equal 'Rasch' or 'rsm' models the latent variance
parameter(s) are automatically freed and estimated
- `mixedmirt()` more supportive of user defined R formulas, and now includes
an internal 'items' argument to create the item design matrix used to
estimate the intercepts. More closely mirrors the results from lme4 for Rasch
models as well (special thanks to Kevin Joldersma for testing and debugging)
- `drop.zeros` option added to extract.item and itemplot to reduce
dimensionality of factor structures that contain slopes equal to zero
- EM tolerance (TOL argument) default dropped to .0001 (originally .001)
- `type = 'score'` and `type = 'infoSE'` added to `plot()` generic for
expected total score and joint test standard error/information
- custom latent mean and covariance matrix can be passed to `fscores()`
for EAP, MAP, and EAPsum methods. Also applies to `personfit()` and
`itemfit()` diagnostics
- scores.only option to `fscores()` for returning just the estimated
factor scores
- bfactor can include NA values in the model to omit the estimation of
specific factors for the corresponding item
BUG FIXES
- limiting values in z.outfit and z.infit statistics for small sample sizes
(fix suggested by Mike Linacre)
- missing data gradient bug fix in MH-RM for dichotomous item models
- global df fix for multidimensional confirmatory models
- SEM information matrix computed with more accuracy (M-step was not
identical to original EM), and fixed when equality constrains are imposed
Changes in mirt 0.7.0
NEW FEATURES
- new `'#PLNRM'` models to fit Suh & Bolt (2010) nested logistic models
- `'large'` option added to estimation functions. Useful when the
datasets being analysed are very large and organizing the data becomes a
computationally burdensome task that should be avoided when fitting new
models. Also, overall faster handling of datasets
- `plot()`, `fitted()`, and `residuals()` generic support added for
MultipleGroup objects
- CFI and X2 model statistics added, and output now includes fit stats
w.r.t. both G2 and X2
- z stats added for itemfit/personfit infit and outfit statistics
- supplemented EM ('SEM') added for calculating information matrix from EM
history. By default the TOL value is dropped to help make the EM iterations
longer and more stable. Supports parallel computing
- added return empirical reliability (`returnER`) option to `fscores()`
- `plot()` supports individual item information trace lines on the same
graph (dichotomous items only) with the option `type = 'infotrace'`
- `createItem()` function available for defining item types that can be
passed to estimation functions. This can be used to model items not
available in the package (or anywhere for that matter) with the EM or
MHRM. Derivatives are computed numerically by default using the numDeriv
package for defining item types on the fly
- Mstep in EM moved to quasi-Newton instead of my home grown MV
Newton-Raphson approach. Gives more stability during estimation when
the Hessian is ill-conditioned, and will provide an easier front-end for
defining user rolled IRT models
BUG FIXES
- small bias fix in Hessian and gradients in `mirt()` implementation
causing the likelihood to not always be increasing near maximum
- fix input to `itemplot()` when object is a list of model objects
- fixed implementation of infit and outfit Rasch statistics
- order of nominal category intercepts were sometimes backwards. Fixed now
- S_X2 collapsed cells too much and caused negative df
- `response.vector` input now supports NA inputs (reported by Neil Rubens)
Changes in mirt 0.6.0
NEW FEATURES
- S-X2 statistic computed automatically for unidimensional models via
itemfit()
- EAP for sum-scores added to fscores() with method = 'EAPsum'. Works with
full.scores option as well
- improve speed of estimation in multipleGroup() when latent means/variances
are estimated
- multipleGroup(invariance = '') can include item names to specify which
items are to be considered invariant across groups. Useful for anchoring
and DIF testing
- type = 'trace' option added to plot() to display all item trace lines
on a single graph (dichotomous items only)
- default estimation method in multipleGroup() switched to 'EM'
- boot.mirt() function added for computing bootstrapped standard errors
with via the boot package (which supports parallel computing as well), as
well as a new option SE.type = '' for choosing between Bock and Lieberman
or MHRM type information matrix computations
- indexing items in itemplot, itemfit, and extract.item can be called
using either a number or the original item name
- added probtrace() function for front end users to generate probability
trace functions from models
- plotting item tracelines with only two categories now omits the lowest
category (as is more common)
- parallel option passed to calcLogLik to compute Monte Carlo log-likelihood
more quickly. Can also be passed down the call stack from confmirt,
multipleGroup, and mixedmirt
- Confidence envelopes option added to itemplot() for trace lines and
information plots
- lbound and ubound parameter bounds are now available to the user for
restricting the parameter estimation space
- mod2values() function added to convert an estimated mirt model into the
appropriate data.frame used to determine parameter estimation characteristics
(starting values, group names, etc)
- added imputeMissing() function to impute missing values given an
estimated mirt model. Useful for checking item and person fit diagnostics
and obtaining overall model fit statistics
- allow for Rasch itemtype in multidimensional confirmatory models
- oblimin the new default exploratory rotation (suggested by Dave Flora)
- more flexible calculation of M2 statistic in fitIndicies(), with user
prompt option if the internal variables grow too large and cause time/RAM
problems
BUG FIXES
- read.mirt() fixed when objects contain standard errors (didn't properly
line up before)
- mixedmirt() fix when COV argument supplied (reported by Aaron Kaat)
- fix for multipleGroup when independent groups don't contain all potential
response options (reported by Scot McNary)
- prevent only using 'free_means' and 'free_varcov' in multipleGroup
since this would not be identified without further constraints (reported
by Ken Beath)
Changes in mirt 0.5.0
- all dichotomous, graded rating scale, (generalized) partial credit,
rating scale, and nominal models have been better optimized
- wald() will now support information matrices that contain constrained
parameters
- confmirt.model() can accept a string inputs, which may be useful for
knitr/sweave documents since the scan() function tends to hang
- multipleGroup() now has the logical options bfactor = TRUE to use the
dimensional reduction algorithm for when the factor pattern is structured
like a bifactor model
- new fitIndices() function added to compute additional model fit statistics
such as M2
- testinfo() function added for test information
- lower bound parameters under more stringent control during estimation
and are bounded to never be higher than .6
- infit and outfit stats in itemfit() now work for Rasch partial credit
and rating scale models
- Rasch rating scale models can now be estimated with potential rsm.blocks
(same as grsm model). "Generalized" rating scale models can also be
estimated, though this requires manipulating the starting values directly
- added AICc and sample size adjusted BIC (SABIC) information statistics
- new mixedmirt() function for estimating IRT models with person and
item level (e.g., LLTM) covariates. Currently only supports fixed effect
predictors, but random effect predictors are being developed
- more structured output when using the anova() generic
Changes in mirt 0.4.2
- item probability functions now only permit permissible values, and models
may converge even when the log-likelihood decreases during estimation. In
the EM if the model does not have a strictly increasing log-likelihood
then a warning message will be printed
- infit and outfit statistics are now only applicable to Rasch models
(as they should be), and in itemfit/personfit() a 'method' argument has
been added to specify which factor score estimates should be used
- read.mirt() re-added into the package to allow for translating estimated
models into a format usable by the plink package
- test standard error added to plot() generic using type = 'SE', and
expected score plot added to itemplot() using type = 'score'
- weighted likelihood estimation (WLE) factor scores now available (without
standard errors)
- removed the allpars option to coef() generics and only return a named
list with the (possibly rotated) item and group coefficients
- information functions slightly positively biased due to logistic constant
adjustment, fixed for all models. Also, information functions are now
available for almost all item response models (mcm items missing)
- constant (D) used in estimating logistic functions can now be modified
(default is still 1.702)
- partcomp models recently broken, fixed now
- more than one parameter can now be passed to parprior to make specifying
identical priors more convenient
Changes in mirt 0.4.1
- relative efficiency plots added to itemplot(). Works directly for
multipleGroup analysis and for comparing different item types (e.g.,
1PL vs 2PL) can be wrapped into a named list
- infit and outfit statistics added to personfit() and itemfit()
- empirical reliability printed for each dimension when fscores(...,
fulldata = FALSE) called
- better system to specify fixed/free parameters and starting values using
pars = 'values'. Should allow for much better simulation based work
- graded model type rating scale added (Muraki, 1990) with optional
estimation 'blocks'. Use itemtype = 'grsm', and the grsm.block option
- for multipleGroup(), optional input added to change the current freely
estimated parameters to values of a previously computed model. This will
save needless iterations in the EM and MHRM since these parameters should
be much closer to the new ML estimates
- itemplot() supports multipleGroup objects now
- analytical derivatives much more stable, although some are not yet
optimized
- estimation bug fix in bfactor(), and slight bias fix in mirt() estimation
(introduced in version 0.4.0 when multipleGroup() added)
- updated documentation and beamer slide show included for some background
on MIRT and some of the packages capabilities
- labels added to coef() when standard errors not computed. Also allpars =
TRUE is now the default
- kernel estimation moved entirely to one method. Much easier to maintain
and guarantees consistency across methods (i.e., no more quasi-Newton
algorithms used)
Changes in mirt 0.4.0
- Added itemfit() and personfit() functions for uni and multidimensional
models. Within itemfit empirical response curves can also be plotted for
unidimensional models
- Wrapped itemplot() and fscores() into S3 function for better
documentation. Also response curve now are all contained in individual plots
- Added free.start list option for all estimation functions. Allows a
quicker way to specify free and fixed parameters
- Added iteminfo() and extract.item() to calculate the item information
and extract desired items
- Multiple group estimation available with the multipleGroup() function. Uses
the EM and MHRM as the estimation engines. The MHRM seems to be faster at
two factors+ though and naturally should be more accurate, therefore it
is set as the default
- wald() function added for testing linear constraints. Useful in situations
for testing sets of parameters rather than estimating a new model for a
likelihood ratio test
- Methods that use the MHRM can now estimate the nominal, gpcm, mcm,
and 4PL models
- fscores computable for multiple group objects and in general play nicer
with missing data (reported by Judith Conijn). Also, using the options
full.scores = TRUE has been optimized with Rcpp
- Oblique rotation bug fix for fscores and coef (reported by Pedro
A. Barbetta)
- Added the item probability equations in the ?mirt documentation for
reference
- General bug fixes as usual that were spawned from all the added
features. Overall, stay frosty.
Changes in mirt 0.3.1
- Individual classes now correspond to the type of methods: ExploratoryClass,
ConfirmatoryClass, and MultipleGroupClass
- plot and itemplot now works for confmirt objects
- mirt can now make use of confmirt.model specified objects and hence be
confirmatory as well
- stochastic estimation of factor scores removed entirely, now only
quadrature based methods for all objects. Also, bfactor returned objects now
will estimate all the factors scores instead of just the general dimension
- Standard errors for mirt now automatically calculated (borrowed from
running a tweaked MHRM run)
Changes in mirt 0.3.0
- radically changed the underlying mechanisms for the estimation functions
and in doing so have decided that polymirt() was redundant and could be
replaced completely by calling confmirt(data, number_of_factors). The
reason for the change was to facilitate a wider range or MIRT models
and to allow for easier extensions to future multiple group analysis and
multilevel modelling
- new univariate and MV models are available, including the
1-4 parameter logistic generalized partial credit, nominal,
and multiple choice models. These are called by specifying a
character vector called 'itemtype' of length nitems with the options
'2PL','3PL','4PL','graded','gpcm', 'nominal', or 'mcm'; use 'PC2PL' and
'PC3PL' for partially-compensatory items. If itemtype = '1PL' or 'Rasch',
then the 1-parameter logistic/1-parameter ordinal or Rasch/partial credit
models are estimated for all the data. The default assumes that items are
either '2PL' or 'graded', as before.
- flexible user defined linear equality restrictions may be imposed on
all estimation functions, so too can prior parameter distributions, start
values, and choice of which parameters to estimate. These all follow these
general 2 steps:
1) Call the function as you would normally would but use, for example,
mirt(data, 1, startvalues = 'index') to return the start values as
they are indexed
2) Edit them as you please (without changing the structure), then input
them back into
the function as mirt(data, 1, startvalues = editedstartvalues).
This is true for the parprior (MAP priors), constrain (linear equality
constraints), and freepars (parameters freely estimated), each with
their own little quirk. All inputs are lists with named parameters for
easy identification and manipulation. Note that this means that the partial
credit model and Rasch models may be calculated as well by modifying either
the start values and constraints accordingly (e.g., constrain all slopes
to be equal to 1/1.702 and not freely estimated for the classical Rasch
model, or all equal but estimated for the 1PL model)
- number of confmirt.model() options decreased due to the new way to specify
item types, startvalues, prior parameter distributions, and constraints
- plink package has not kept up with item information curves, so I'll
implement my own for now. Replaced plink item plots from 'itemplots'
function with ones that I rolled
- package descriptions and documentation updated
- coef() now prints slightly different output, with the new option 'allpars
= TRUE' to display all the item and group parameters, returned as a list
- simdata() updated to support new item types
- more accurate standard errors for MAP and ML factor scores, and specific
factors in bfactorClass objects can now be estimated for all methods
Changes in mirt 0.2.6-1
- dropped the ball and had lots of bug fixes this round. Future commits
will avoid this problem by utilizing the testthat package to test code
extensively before release
- internal change in confmirt function to move MHRM engine outside the
function for better maintenance
- theta_angle added to mirt and polymirt plots for changing the viewing
angle w.r.t theta_1
- null model no longer calculated when missing data present
- fixed item slope models estimated in mirt() with associated standard errors
Changes in mirt 0.2.6
- null model computed, allowing for model statistics such as TLI
- documentation changes
- many back end technical details about estimation moved to technical lists
- support for all GPArotation methods and options, including Target rotations
- polymirt() uses confmirt() estimation engine
- 4PL support for mirt() and bfactor(), treating the upper bound as fixed
- coef() now has a rotate option for returning rotated IRT parameters
Changes in mirt 0.2.5
- Fixed translation bug in the C++ code from bfactor() causing illegal
vector length throw
- Fixed fscores() bug when using polychotomous items for mirt() and bfactor()
- pass rotate='rotation' from mirt and polymirt to override default
'varimax' rotation at estimation time (suggested by Niels Waller)
- RMSEA, G^2, and p set to NaN instead of internal placeholder when there
are missing data
- df adjusted when missing data present
- oblique rotations return invisible factor correlation matrix
Changes in mirt 0.2.4
- degrees of freedom correctly adjusted when using noncompensatory items
- confmirtClass reorganized to work with S4 methods, now work more
consistently with methods.
- fixed G^2 and log-likelihood in logLik() when product terms included
- bugfix in drawThetas when noncompensatory items used
Changes in mirt 0.2.3
- bugfixes for fscores, itemplot, and generic functions
- read.mirt() added for creating a suitable plink object
- mirt() and bfactor() can now accommodate polychotomous items using an
ordinal IRT scheme
- itemplot() now makes use of the handy plink package plots, giving a good
deal of flexibility.
- Generic plot()'s now use lattice plots extensively
Changes in mirt 0.2.2
- Ported src code into Rcpp for future tweaking.
- Added better fitted() function when missing data exist (noticed by
Erin Horn)
Changes in mirt 0.2.1
- ML estimation of factor scores for mirt and bfactor
- RMSEA statistic added for all fitted models
- Nonlinear polynomial estimation specification for confmirt models,
now with more consistent returned labels
- Provide better identification criteria for confmirt() (suggested by
Hendrik Lohse)
Changes in mirt 0.2.0
- parameter standard errors added for mirt() (1 factor only) and bfactor()
models
- bfactor() values that are ommited are recoded to NA in summary and coef
for better viewing
- 'technical' added for confmirt function, allowing for various tweaks
and varying beta prior weights
- product relations added for confmirt.model(). Specified by enclosing in
brackets and using an asterisk
- documentation fixes with roxygenize
Changes in mirt 0.1.20
- allow lower bound beta priors to vary over items (suggested by James Lee)
Changes in mirt 0.1.6
- bias fix for mirt() function (noticed by Pedro Barbetta)