Analysis of dichotomous and polytomous response data using
unidimensional and multidimensional latent trait models under the Item
Response Theory paradigm (Chalmers (2012)
Multidimensional item response theory in R.
Analysis of dichotomous and polytomous response data using unidimensional and multidimensional latent trait models under the Item Response Theory paradigm. Exploratory and confirmatory models can be estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier analyses are available for modeling item testlets. Multiple group analysis and mixed effects designs also are available for detecting differential item functioning and modeling item and person covariates.
Various examples and worked help files have been compiled using the knitr
package to generate
HTML output, and are available on the package wiki.
User contributions are welcome!
It's recommended to use the development version of this package since it is more likely to be up to date than the version on CRAN. To install this package from source:
Obtain recent gcc, g++, and gfortran compilers. Windows users can install the Rtools suite while Mac users will have to download the necessary tools from the Xcode suite and its related command line tools (found within Xcode's Preference Pane under Downloads/Components); most Linux distributions should already have up to date compilers (or if not they can be updated easily). Windows users should include the checkbox option of installing Rtools to their path for easier command line usage.
Install the devtools
package (if necessary). In R, paste the following into the console:
install.packages('devtools')
devtools
package (requires version 1.4+) and install from the Github source code.library('devtools')install_github('philchalmers/mirt')
If the devtools
approach does not work on your system, then you can download and install the
repository directly.
Obtain recent gcc, g++, and gfortran compilers (see above instructions).
Install the git command line tools.
Open a terminal/command-line tool. The following code will download the repository code to your computer, and install the package directly using R tools (Windows users may also have to add R and git to their path)
git clone https://github.com/philchalmers/mirt
R CMD INSTALL mirt
In some reported cases XCode
does not install the appropriate gfortran
compilers in the correct location, therefore they have to be installed manually instead. This is accomplished by inputing the following instructions into the terminal:
curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2
sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /
This package is free and open source software, licensed under GPL (>= 3).
Bug reports are always welcome and the preferred way to address these bugs is through the Github 'issues'. Feel free to submit issues or feature requests on the site, and I'll address them ASAP. Also, if you have any questions about the package, or IRT in general, then feel free to create a 'New Topic' in the mirt-package Google group. Cheers!
empirical.poly.collapse
argument added to itemfit()
to plot expected score functions for polytomous
items (suggested by Keri Brady)
SRMSR now reported in M2()
for GGUMs (suggested by Bo on the mirt-package forum)
weights
argument added to estfun.AllModelClass
to allow for the inclusion
of survey.weights
to calculate the scores
DIF()
now simplifies the output by default rather than returning lists from anova()
. Wald tests are always simplified
Where applicable, RMSEA statistics are reported in itemfit()
for tests that return suitable
X2 and df components
Fix negative TLI and CFI values when using the C2 statistic from the M2()
function (reported
by Jake Kraska and Charlie Iaconangelo)
Fix delta method SEs for 'gpcm'
itemtype (reported by Lennart Schneider)
When lower/upper bounded parameters are included the default optimizer is now 'nlminb' rather than 'L-BFGS-B'. This is mainly due to the instability in the 'L-BFGS-B' algorithm which is prone to converging instantly for unknown reasons
mdirt()
gains a item.Q
list to specify Q-matrices at the item-category level for each item
createItem()
functions gain an optional argument to the function definitions to allow for
list-specified data from functions such as mirt()
via a silent mirt(..., customItemsData)
argument
lattice auto.key
default now reports lines rather than points. This is now more consistent
when, for example, color theme is changed to black and white in the trellis window
Added Differential Response Function (DRF) statistics from upcoming publication (Chalmers, accepted)
in a new function entitled DRF()
. These are related to compensatory and non-compensatory measures
of response bias for DIF, DBF, and DTF available from the SIBTEST framework but
for IRT model fitted within the multiple-group estimation framework
structure
argument added to mdirt()
function to allow log-linear models for
simplifying the profile probability model computations
export internally used traditional2mirt()
function to transform a small selection of
classical IRT parameterizations into the slope-intercept form
fix survey.weights
input for multiple group models (reported by Leigh Allison)
fix itemtype = "rsm"
block restriction when items contain unequal category lengths
(reported by Aiden Loe)
SIBTEST()
computation of beta coefficient changed to match Shealy and Stout's (1993)
form of p_k * (Y_R - Y_F)
(was previously p_k * (Y_F - Y_R)
; reported by Craig Wells).
As well, Jmin
default is increased to 5 to avoid conservative Type I error behavior in
longer tests
Fix negative chi-square differences in DIF()
function due to non-converged sub-models
(reported by Daniel McKelvey)
M2()
function gains a type
input to distinguish between the univariate-bivariate
collapsed M2* statistic and the bivariate only collapsed C2 statistic (Cai and Monro, 2014).
C2 can be useful for polyomous items when there are too few degrees of freedom to compute
the fully collapsed M2*
multipleGroup()
gains the dentype
argument to allow for mixture IRT models to be
fitted (e.g., dentype = 'mixture-3'
fits a three-class mixture model). This also
allow modifications such as the zero-inflated IRT model to be fitted
technical
gains a zeroExtreme
logical flag to assign survey weights of 0 to extreme
response patterns (FALSE by default). This may be required when
Woods' extrapolation-interpolation method is used with empirical histograms
to avoid ill defined extrapolated densities
fscores()
, itemfit()
, M2()
, and residuals()
gain a use_dentype_estimate
argument to
compute EAP-based scores whenever the latent trait density was estimated (e.g., via empirical histograms)
Empirical histograms can now be now scaled to [0,1] using Woods' extrapolation-interpolation method
via the input dentype = 'empiricalhist_Woods'
. Degrees of freedom updated to reflect this change,
and 121 quadrature points are used instead of the previous 199 for better stability
Semi-parametric Davidian curve estimation of the shape of the latent trait distribution in
unidimensional IRT models was contributed by Oguzhan Ogreden, as well the associated
components used within this framework (such as the interpolation-extrapolation method
described by Woods, 2006). This estimation method is available through the new dentype
input.
mirt also now links to the dcurver
package to obtain the associated computation functions
in the EM algorithm
M2()
, itemfit()
, SIBTEST()
, and fscores()
gain an na.rm
logical to remove rows of missing data
fscores()
gains a append_response.pattern
logical to indicate whether response patterns via the
response.pattern
input should be appended to the factor score results
new dentype
argument added to estimation-based functions to specify the density structure of the
latent traits (default is 'Gaussian'
). This update breaks the previous empiricalhist
logical option
anova()
will accept a single fitted model object and return information related to AIC, BIC,
log-likelihood, etc
Hannanâ€“Quinn (HQ) Criterion added to anova()
Added multidimensional version of sequential response model (e.g., Tutz, 1990). Includes
itemtype = 'sequential'
for the multidimensional 2PL variant, and itemtype = 'Tutz'
for the Rasch variant
Printing IRT parameters via coef(mod, IRTpars = TRUE)
now computes the delta method
for the g
and u
terms as well. Interpreting these is generally not recommended
due to their bounded parameter nature (CIs can be outside the range [0,1]),
but are included for posterity
createItem()
gains a bytecompile
flag to indicate whether the internal functions should
be byte-compiled before using (default is TRUE)
Special GROUP
location holder in mirt.model()
to index the group-level
hyper-parameter terms
key2binary()
gains a score_missing
flag to indicate whether missing values should be scored
as 0 or left as NA
createItem()
gains support for derivType = 'symbolic'
and
derivType.hss = 'symbolic'
to symbolically compute the gradient/Hessian
functions (template code-base contributed by Chen-Wei Liu)
createItem()
gains a derivType.hss
argument to distinguish gradient from
Hessian numerical computations
mdirt()
gains support for createItem()
inputs
More plotting points added to default plot()
and itemplot()
generics to create
smoother traceline functions
fix simdata()
bug for new ggum
itemtype
fix new grouping syntax specification in mirt.model()
when combining START and FIXED
(reported by Garron Gianopulos)
fix IRTpars = TRUE
input when itemtype was Rasch
(reported by Benjamin Shear)
mod2values()
and passing pars = 'values'
now return data.frame
objects without any
factor variables (previously the defaults to data.frame()
were used, which created factors
for all categorical variables by default)
Add monopoly
itemtype to fit unidimensional monotonic polynomial item response model
for polytomous data (see Falk and Cai, 2016)
Add ggum
itemtype to fit unidimensional/multidimensional graded unfolding model
(e.g., Roberts & Laughlin, 1996). Special thanks to David King for providing the
necessary C++ derivative functions and starting values
Square brackets can now be included in the mirt.model()
syntax to indicate
group-specific constraints, priors, starting/fixed values, and so on. These
are all of the general form "CONSTRAIN [group1, group2] = ..."
or
"FIXED [group1] = ..."
Added delta method for several classical IRT parameterization
(via coef(model, IRTpars = TRUE)
) when a suitable information matrix
was previously estimated
numDeriv
dependency removed because numerical_deriv()
now supports a local
Richardson extrapolation type. For best accuracy, this is now used as the default
throughout the package
createItem()
and lagrange()
now use Richardson extrapolation as default
instead of the less accurate forward/central difference method
estfun()
function added to extract gradient information directly from fitted objects
(contributed by Lennart Schneider)
simdata()
gains an equal.K
argument to redraw data until $K$ categories are
populated for a given item
Fix initialization of fscores()
when using 'MH' plausible value imputations (reported by
Charlie Iaconangelo)
Various other small bug fixes and performance improvements, fixes for Solaris compatibility, and run a small number of examples during R CMD check
mdirt()
now supports latent regression covariate predictors. Associated function
(e.g., fscores()
) also include the latent regression information for discrete
models by default
SIBTEST()
replaced with the asymptotic sampling distribution version of
CSIBTEST described by Chalmers (accepted)
calcNull
set to FALSE
by default
Sandwich ACOV estimate now uses the Oakes estimate in the computations rather than the intensive
Louis form (which require low-level coding of the item-level Hessian terms). Added a
new SE.type = 'sandwich.Louis'
for the original sandwich VCOV estimate in the previous version of
mirt
fix latent regression models with QMCEM and MCEM algorithms (reported by Seongho Bae)
fscores()
gains a max_theta
argument to apply upper/lower bounds to iterative searching
algorithms (issue reported by Sebastian Born), and a start
input to set the starting values
as well (primarily useful in mirtCAT to reduce iterations)
alabama
package optimizer no longer used. Replaced with generic interface from nloptr
package to support numerous optimizers with greater control instead. Associated inputs
(e.g., alabama_args
) replaced as well
Export missing S4 methods for external R packages to import
MDIFF and MDISC no longer in normal ogive metric (1.702 scaling value removed)
added QMC
option to residuals()
for LD
and LDG2
methods. Also globally set the number of QMC points to
5000 throughout the package for consistency
info_if_converged
and logLik_if_converged
added to technical
list to indicate whether the information matrix
and stochastic log-likelihood should be computed only when the model converges. Default is now TRUE
for both
added 'MCEM'
method for Monte Carlo EM. An associated MCEM_draws
function added to the technical
list
as well to control the number of draws throughout the EM cycles
support for information matrix computations for QMCEM method added (e.g., Oakes, crossprod, Louis)
globally improve numerical efficiency of QMC methods, including the QMCEM estimator
include missing data values in itemfit()
for parametric bootstrap methods to replicate missing
data pattern
ensure that nest-logit models have at least 3 categories (reported by Seongho Bae)
convergence set to FALSE if any g > u
is found in the 4PL model
in verbose console output the log-posterior is printed when priors are included in the EM (previously was only the marginal likelihood)
various bug fixes to SIBTEST, particularly for very small sample sizes
anova()
LRT comparison gains a bounded
logical to indicate whether a bounded parameter is being compared,
as well as a mix
argument to indicate the mixture of chi-squared distributions
MH-RM estimation optimizer
argument can now be modified to BFGS
, L-BFGS-B
, and NR
instead of the default NR1
a distinction between the NR
optimizer in the EM and MH-RM applications is included, where the MH-RM now
defaults to NR1
to indicate a single Newton-Raphson update that uses an RM filtered Hessian term
method = 'SEM'
added to perform the stochastic EM algorithm (first two stages of the MH-RM algorithm setup).
Alternatively, setting technical = list(NCYCLES = NA)
when using the MH-RM algorithm now returns
the stochastic EM results
added multidim_matrix
option to iteminfo()
to expose computation of information matrices
bounded parameter spaces handled better when using the NR optimizer
various bug fixes and performance improvements
SE.type = 'Oakes'
set as the new default when computing standard errors via the ACOV matrix when
using the EM algorithm
new SE.type = 'Oakes'
to compute Oakes' 1999 form of the observed information matrix using
central difference approximation. Applicable for all IRT models (including customized IRT types)
added support for gpcmIRT
and rsm
itemtypes for traditional generalized partial credit model and
Rasch rating scale model (which may be modified for a generalized rating scale model by freeing the
slope parameters)
SE.type = 'Fisher'
now supports the inclusion of latent distribution hyper-parameters.
Officially, all SE-types now provide proper hyper-parameter influence in the information matrices
wrapped various output objects as mirt_df
, mirt_matrix
, and mirt_list
class to
avoid the need for passing a digits
argument for rounding output in the console.
Now, returned objects are never rounded, which makes writing Monte Carlo
simulation code safer in that rounded results will not appear in the results
added Stone's (2000) fit statistics and forthcoming PV-Q1 fit statistics to itemfit()
fscores()
when EAP estimates were used in extremely long (1000+ item)
tests. Error now reported when this happens. Using MAP estimates in these extreme situations
is essentially equivalent and now recommendedadd information about the number of freely estimated parameters to print()
generic
in plot()
, auto.key
is only disabled when facet_items = FALSE
for dichotomous items. Also, adjusted
ordering of plot(mod, type = 'itemscore')
to reflect actual item ordering in the data
Stretched the theoretical bounds of the y-axis for score-based functions in plot()
and itemplot()
(e.g., 3PL models will now always stretch to S(theta) = 0)
plot(mod, type = 'score')
not supports the which.items
input to make expected score plots for
bundles of items
penalized term added to EM algorithm estimation subroutines to help keep the covariance matrix
of the latent trait parameters positive definite in the M-step (helps convergence
properties of the optimizers, especially 'L-BFGS-B'). To turn this penalized
term off use technical = list(keep_vcov_PD = FALSE)
added type = 'itemscore'
to plot()
generic to plot faceted version of the item
scoring functions. Particularly useful when investigating DIF with multipleGroup()
better support for splines
itemtype in multiple-group models
fix problem with 'EAPsum' in fscores()
when response.pattern
input
supplied (reported by Eva de Schipper)
plot(mod, type = 'rxx')
now uses the latent variance in the computations (reported by
Amin Mousavi)
fix syntax input when customized IRT models are supplied
df
adjustment for the S_X2
item-fit statistic for models where the latent trait
hyper-parameters have been estimated
itemfit()
and personfit()
properly detect dichotomous Rasch models which
have been defined with the constrained slopes approach
argument 'fit_stats'
now used in itemfit()
to replace longer list of logicals
(e.g., itemfit(mod, S_X2 = FALSE, X2 = TRUE, infit = FALSE, ...)
). Now fit stats
are explicitly requested through a character vector input. Default still uses the
S_X2 statistic
when using 'lnorm'
prior lower bound automatically set to 0, and with 'beta'
prior
the lower and upper bounds are set to [0,1]
mdirt()
now uses optimizer = 'nlminb'
by default
revert using default 'penalized version of the BFGS algorithm' instead of L-BFGS-B when box-constraints are used (introduced in version 1.19)
Neale & Miller 1997 approximation added to PLCI()
(default still computes exact PL CIs)
type = 'score'
supported for multiple group models in itemplot()
added poly2dich
function to quickly change polytomous response data to a comparable matrix of
dichotomous response data
a penalized version of the BFGS algorithm is now used instead of the L-BFGS-B when upper and lower bounds are included (provides more robust estimates)
the variances of the orthogonal factors in bfactor()
can now be freely
estimated. This allows modeling of designs such as the testlet response model (example included in
the documentation)
new spline
itemtype to model B-spline response functions for dichotomous models. Useful for
diagnostic purposes after detecting item-misfit. Additional arguments can be passed to the
spline_args
list input to control the behaviour of the splines for each item. Currently limited
to unidimensional models only
fscores()
gains a plausible.type
argument to select between normal approximation PVs or
Metropolis-Hastings samples (suggested by Yang Liu)
mdirt()
has been modified to support DINA, DINO, located latent class,
and other diagnostic classification models. Additionally, the customTheta
input required to build
customized latent class patterns has been changed from the previously cumbersome
mdirt(..., technical = list(customTheta = Theta))
to simply mdirt(..., customTheta = Theta)
simdata()
gains a prob.list
input to supply a list of matrices with probability values to be sampled
from (useful when specialized response functions outside the package are required)
simdata()
supports 'lca' itemtypes for latent class model generation
improved M2 accuracy when latent trait variances are estimated
corrected behaviour of M2()
when linear constraints are applied (M2 test was previously too conservative
when constraints were used). This affects single as well as multiple-group models (reported by Rudolf Debelak)
add plausible values for latent class and related models estimated from mdirt()
multipleGroup()
throws proper error when vertical scaling is not identified correctly due to NAs
S-X2 itemfit statistic fix when very rare expected categories appear (reported by Seongho Bae)
mdirt()
function now includes explicit parameters for the latent class intercepts (in log-form).
This implies that correct standard errors can be computed using various methods (e.g., SEM, Richardson,
etc)
new customGroup()
function to define hyper-parameter objects for the latent trait distributions
(generally assumed to be Gaussian with a mean and covariance structure)
new boot.LR()
function to perform a parametric bootstrap likelihood-ratio test between
nested models. Useful when testing nested models which contain bounded parameters (e.g.,
testing a 3PL versus a 2PL model)
adjust the lagrange()
function to use the full information matrix (was previously only a
quasi-lagrange approximation)
greatly improved speed in simdata()
, consequently changes the default seed
fix crash error in mirtmirt()
for multidimensional models with lr.random effects (reported
by Diah Wihardini)
expbeta
prior starting values fix by setting to the mean of the prior rather than the mode
(reported by Insu Paek)
itemfit()
function reworked so that all statistics have their own input flag (e.g., Zh = TRUE
,
infit = TRUE
, etc). Additionally, only S-X2 is computed by default and X2/G2 (and the associated
graphics and tables) are computed using 10 fixed bins
added empirical.table
argument to return tables of expected/observed values for X2
and G2
group.bins
and group.fun
argument added to itemfit()
to control the size of the
bins and the central tendancy function for X2
and G2
computations
'expbeta'
option added to implement a beta prior specifically for the g
and u
parameters which
internally have been transformed to logits (performes the back transformation before computing the
values)
check whether multiple-group models contain enough data to estimate parameters uniquely when no constraints are applied
set the starting values the same when using parprior
list or mirt.model()
syntax
(reported by Insu Paek)
empirical_ES()
function added for effect size estimates in DIF/DBF/DTF analyses (contributed by
Adam Meade)
standardized loadings not correct when factor correlations included in confirmatory models (reported by Seongho Bae)
MDISC
and MDIFF
values were missing the 1.702 multiplicitive constant (reported by
Yi-Ling Cheng)
fix information trace-lines in multiple-group plots (reported by Conal Monaghan)
suppress standard errors in exploratory models when rotate != 'none'
(suggested by Hao Wu)
sequential schemes in DIF()
generated the wrong results (reported by Adam Meade)
M2()
was not properly accounting for latent variance terms (reported by Ismail Cuhadar)
enable lr.random
input to mixedmirt()
for multilevel-IRT models which are not from the Rasch family
add common vcov()
and logLik()
methods
latent regression EM models now have standard error computation supporte with the 'complete', 'forward', 'central', and 'Richardson' methods
new areainfo()
function to compute the area under information curves within specified ranges
(suggested by Conal Monaghan)
method = 'BL'
supported for multiple-group models. As well, SE.type = 'numerical'
included to return
the observed-data ACOV matrix from the call to optim()
(can only be used when the BL
method is selected)
new SE.type = 'FMHRM'
to compute information matrix based on a fixed number of MHRM draws, and an
associated technical = list(MHRM_SE_draws)
argument has been added to control the number of draws
added lagrange
(i.e., score) test function for testing whether parameters should be freed in single
and multiple group models estimated with the EM algorithm
numerical_deriv
function made available for simple numerical derivatives, which may be useful when
defining fast custom itemtype derivative terms
SE.type
used to compute the ACOV matrix gained three numerical estimates for the
forward difference ('forward'), central difference ('central'), and Richardson extropolation ('Richardson')
added SIBTEST and crossed-SIBTEST procedures with the new function SIBTEST()
added empirical_plot
function for building empirical plots (with potential smoothing)
when conditioning on the total score
more low-level elements included in extract.mirt()
function
added grsmIRT
itemtype for classical graded rating scale form (contributed by KwonHyun Kim)
added missing analytic Hessian terms when gpcm_mats
are used (contributed by Carl Falk)
technical = list(removeEmptyRows = TRUE)
(reported by Aaron Kaat)Data
, Model
, Fit
, and so on. Additionally,
the information matrix has slot has been removed in favour of providing the asymptotic covariance
matrix (a.k.a., the inverse of the information matrix)added extract.mirt()
function to allow more convenient extracting of internal elements
crossprod
SE.type now incorporates latent variable information (replaces NA placeholders)
changed the default full.scores = FALSE
argument to TRUE
in fscores()
added profile
argument to plot()
for mdirt()
objects so that profile plots can be generated
converge_info
option added to fscores()
to return convergence information
add removeEmptyRows
option to technical
list
return a vector of NA
s when WLE estimation has a Fisher information matrix with a determinant of 0
(reported by Christopher Gess)
fix df in multiple-group models with crossed between/within constrains (reported by Leah Feuerstahler)
compute residuals when responses are sparse, and return NaN
when residual could not be computed
(reported by Aaron Kaat)
adjust plausible values format for multiple group objects
simdata()
gains a model
input to impute data from pre-organized models (useful in conjunction
with mirtCAT or to generate datasets from already converged models). Also gains a mins
argument
to specify what the lowest category should be for each item if model
is not supplied (default is 0)
number of SEMCYCLES
increased from 50 to 100 in the MH-RM algorithm, and RM gain rate changed from
c(.15, .65)
to c(.1, .75)
further improve item fit statistics when using imputations
facet plots now try to keep the items in their respective order
panel theme for lattice plots changed from default to a lighter blue colour, and legend now automatically placed on the right hand side rather than the top
when using prior distributions, starting values now automatically set equal to the mode of the prior distribution, and appropriate lower and upper parameter bounds are supplied
added NEXPLORE
term to mirt.model()
to specify exploratory models via the syntax
add itemGAM()
function to provide a non-linear smoother for better understanding mis-functioning
items (and without loosing established precision by reverting to purely non-parametric IRT methods)
category scores are now automatically recoded to have spaces of 1, and a message is printed if/when this occurs
added MDISC()
and MDIFF()
functions
the inclusion of prior parameter distributions will now report the log-posterior rather than
the log-likelihood. Functions such as anova()
will also report Bayesian criteria rather than
the previous likelihood-based model comparison statistics
impute
argument in itemfit()
and M2()
now use plausible values instead of point estimates
START
syntax element in mirt.model()
now supports multiple parameters, and FIXED
argument added to declare parameters as 'fixed' at their staring values
added LBOUND
and UBOUND
syntax support in mirt.model()
report proper lower and upper bounds in starting values data frame and from mod2values()
invariance
argument to bfactor()
now automatically indexes the second-tier factors to make
multiple-group testing with bfactor()
easier
remove rotate
and Target
arguments from model objects, and pass these only through axillary
functions such as summary()
, fscores()
, etc
model
based arguments now can be strings, which are passed to mirt.model()
. This is now
the preferred method for defining models syntactically, though the previous methods
will still work
integration range (theta_lim
) globally set to c(-6, 6)
, and number of default quadrature nodes
have systematically increased in parameter estimation functions. This will slightly change some
numerical results, but provides more consistence throughout the package
add theta_lim
arguments to various functions
better control of QMC grid, and more effective usage for higher dimensions
internal code organization now makes it easier to add user defined itemtype
s (which can be natively
added into the package, if requested)
fix conservative imputation standard errors in itemfit()
and M2()
(reported by Irshad Mujawar)
fixed plausible value draws for multidimensional latent regression models (reported by Tongyun Li)
don't allow crossprod, Louis, or sandwich information matrices when using custom item types (reported by Charlie Rutgers)
when using coef(mod, printSE=TRUE)
the g
and u
parameters are relabeled to logit(g)
and
logit(u)
to represent the internal labels
added various facet plots for three dimensional models to plot()
generic
support optimizer = 'nlminb'
, and pass optimizer control arguments to a contol
list
added fixef()
function to extract expected values implied by the fixed effect parameters
in latent regression models
added gpcm_mats
argument to estimation functions for specifying a customize scoring pattern
for multidimensional generalized partial credit models
added custom_theta
input to fscores()
for including customized integration grids
add a suppress
argument to residuals()
and M2()
to suppress local dependence
values less than this specific value
print a message in DIF()
and DTF()
when hyper-parameters are not freely
estimated in focal groups
constraits for hetorogenous item names added to mirt.model()
syntax
WLE support for multidimensional models added
added 'SEcontour'
argument to plot()
generic
use NA's in fscores()
when response patterns contain all NA responses (suggested by Tomasz Zoltak)
S-X2 in itemfit()
now returns appropriate values for multiple-group models
multidimensional plausible value imputation fix (reported by KK Sasa)
plot(..., type = 'infotrace')
for multiple group objects fixed (reported by Danilo Pereira)
fscores()
nows accepts method = "plausible"
to draw a single plausible value set
plot()
default type is now score
, and will accept rotation arguments for exploratory models
(default rotation is 'none'
)
imputeMissing()
supports a list of plausible values to generate multiple complete datasets
new custom_den
input to fscores()
to use custom prior density functions for Bayesian estimates
more optimized version of the 'WLE' estimator in fscores()
empirical reliability added when method = 'EAPsum'
in fscores()
new START
argument in mirt.model()
for specifying simple starting values one parameter at
a time
fix carryover print-out error in summary()
when confirmatory models were estimated
bound contraints not were not included for group hyper-parameters (reported by KK Sasa)
improved estimation efficiency when using MH-RM algorithm. As a result, the default seed was changed, therefore results from previous versions will be slightly different
objects of class 'ExploratoryClass' and 'ConfirmatoryClass' have been merged into a single class
'SingleGroupClass' with an exploratory
logical slot
the technical = list(SEtol)
criteria for approximating the information matrix
was lowered to 1e-4 in mixedmirt()
to provide better standard error estiamtes
boot.mirt
now uses the optimizer used to estimate the model (default previously was EM)
mixedmirt
now supports interaction effects in random intercepts, including cross-level
interactions
added averageMI()
function to compute multiple imputation averages for the plausible
values methodology using Rubin's 1987 method
plausible value imputation now available in fscores()
using the new plausible.draws
numeric input
add return.models
argument to DIF()
to return estimated models with free/constrained
parameters
latent regression models added to mixedmirt()
for non-Rasch models using the
new lr.formula
input
mirt.model()
syntax can now define within individual item equality constraints
by using more than 1 parameter specification name in the syntax
latent regression models added to mirt()
function by using the new covdata
and formula
inputs
added confidence envelope plots to PLCI.mirt
, and throw warnings when intervals could not be
located
coef()
now accepts a simplify
logical, indicating whether the items should be collapsed
to a matrix and returned as a list of length 2 (suggested by Michael Friendly)
bias correction in variance estimates mixedmirt
when random effects are
included (reported by KK Sasa)
fix missing data imputation bug in itemfit()
(reported by KK Sasa)
M2 statistic for bifactor/two-tier models was overly conservative
better checks for numerical underflow issues
use triangle 0's for identifying exploratory IFA models. As such, standard errors/condition numbers for exploratory models can be estimated again
sirt
package added to suggests list. Special thanks to Alexander Robitzsch (author of sirt
)
for developing useful wrapper functions for mirt such as mirt.wrapper.coef()
, tam2mirt()
, and
lavaan2mirt()
. As well, many examples in sirt
demonstrate the possibility of estimating
specialized IRT models with mirt
, such as the: Ramsay quotient, latent class,
mixed Rasch, located latent class, probabilistic Guttman, nonparametric, discrete graded
membership, and multidimensional IRT discrete traits, DINA, and Rasch copula models.
exploratory IRT models are no longer rotated by default in coef()
, and now requires an
explicit rotate
argument
computation of S_X2
statistic in itemfit
now much more stable for polytomous item types
support for the plink
package now unofficially dropped because it was removed from CRAN
data inputs are now required to have category spacing codings exactly equal to 1 (e.g., [0, 1, 2, ...]; patterns such as [0, 2, 3] which are implicitly missing spaces are now invalid)
mdirt
function added to model discrete latent variables such as latent class analysis for
dichotomous and polytomous items. Can be used to model several other discrete IRT models as well,
such as the located latent class model, multidimensional IRT with discrete traits, DINA models,
etc. See the examples and documentation for details
axillary support for DiscreteClass
objects added to itemfit()
, M2()
, fscores()
,
wald()
, and boot.mirt()
the S-X2 statistic available in itemfit()
has been generalized to include multidimensional
models
the method 'QMCEM'
has been added for quasi-Monte Carlo integration in mirt()
and multipleGroup()
for estimating higher dimensional models with greater accuracy
(suggested by Alexander Robitzsch). Several axillary function such as fscores()
,
itemfit()
, and M2()
also now contain an QMC
argument (or will accept one through the ...
argument) to use the same integration scheme for better accuracy in higher dimensional models
nonlinear parameter constraints for EM estimation can be specified by using the Rsolnp
and alabama
packages by passing optimizer = 'solnp'
and optimizer = 'alabama'
,
as well as the relevant package arguments through the solnp_ags
and alabama_ags
list inputs
itemnames
argument added to mirt.model()
to allow model specifications using raw
item names rather than location indicators
accelerate
argument changed from logical to character vector, now allowing three
potential options: 'Ramsay' (default), 'squarem', and 'none' for modifying the
EM acceleration approach
fixed bug in bfactor()
starting values when NAs were specified in the model
argument
adjust overly optimistic termination criteria in EM algorithm
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 explicitly 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
separate stage is performed. This provides much better accuracy in the computations
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
help file
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
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'
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
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)
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
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)
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
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
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
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
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'
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
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
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
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
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
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
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
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
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
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)
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
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)
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
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
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)
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.
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)
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:
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
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
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
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
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
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
Ported src code into Rcpp for future tweaking.
Added better fitted() function when missing data exist (noticed by Erin Horn)
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)
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