sirt (version 1.14-0)

mirt.wrapper: Some Functions for Wrapping with the mirt Package

Description

Some functions for wrapping with the mirt package.

Usage

# extract coefficients	
mirt.wrapper.coef(mirt.obj)
# extract posterior, likelihood, ...
mirt.wrapper.posterior(mirt.obj,weights=NULL)
"IRT.likelihood"(object, ...)
"IRT.posterior"(object, ...)
# S3 method for extracting item response functions 
"IRT.irfprob"(object, ...)
# compute factor scores
mirt.wrapper.fscores(mirt.obj,weights=NULL)
# convenience function for itemplot
mirt.wrapper.itemplot( mirt.obj , ask=TRUE , ...)

Arguments

mirt.obj

A fitted model in mirt package

object

A fitted object in mirt package of class SingleGroupClass or MultipleGroupClass.

weights

Optional vector of student weights

ask

Optional logical indicating whether each new plot should be confirmed.

...

Further arguments to be passed.

Value

Function mirt.wrapper.coef -- List with entriesFunction mirt.wrapper.posterior -- List with entriesFunction mirt.wrapper.fscores -- List with entries

Examples for the <span class="pkg">mirt</span> Package

1. Latent class analysis (data.read, Model 7)
2. Mixed Rasch model (data.read, Model 8)
3. Located unidimensional and multidimensional latent class models / Multidimensional latent class IRT models (data.read, Model 12; rasch.mirtlc, Example 4)
4. Multimensional IRT model with discrete latent traits (data.read, Model 13)
5. DINA model (data.read, Model 14; data.dcm, CDM, Model 1m)
6. Unidimensional IRT model with non-normal distribution (data.read, Model 15)
7. Grade of membership model (gom.em, Example 2)
8. Rasch copula model (rasch.copula2, Example 5)
9. Additive GDINA model (data.dcm, CDM, Model 6m)
10. Longitudinal Rasch model (data.long, Model 3)
11. Normally distributed residuals (data.big5, Example 1, Model 5)
12. Nedelsky model (nedelsky.irf, Examples 1, 2)
13. Beta item response model (brm.irf, Example 1)

Details

The function mirt.wrapper.coef collects all item parameters in a data frame.

The function mirt.wrapper.posterior extracts the individual likelihood, individual likelihood and expected counts. This function does not yet cover the case of multiple groups.

The function mirt.wrapper.fscores computes factor scores EAP, MAP and MLE. The factor scores are computed on the discrete grid of latent traits (contrary to the computation in mirt) as specified in mirt.obj@Theta. This function does also not work for multiple groups.

The function mirt.wrapper.itemplot displays all item plots after each other.

See Also

See the mirt package on CRAN https://CRAN.R-project.org/package=mirt and on GitHub https://github.com/philchalmers/mirt.

See https://groups.google.com/forum/#!forum/mirt-package for discussion about the mirt package.

See for the main estimation functions in mirt: mirt::mirt, mirt::multipleGroup and mirt::bfactor.

See mirt::coef-methodfor extracting coefficients.

See mirt::mod2values for collecting parameter values in a mirt parameter table.

See lavaan2mirt for converting lavaan syntax to mirt syntax.

See tam2mirt for converting fitted tam models into mirt objects.

See also CDM::IRT.likelihood, CDM::IRT.posterior and CDM::IRT.irfprob for general extractor functions.

Examples

## Not run: 
# # A development version can be installed from GitHub
# if (FALSE){ # default is set to FALSE, use the installed version
#    library(devtools)
#    devtools::install_github("philchalmers/mirt")
#           }
# # now, load mirt
# library(mirt)
# 
# #############################################################################
# # EXAMPLE 1: Extracting item parameters and posterior LSAT data
# #############################################################################
# 
# data(LSAT7, package="mirt")
# data <- mirt::expand.table(LSAT7)
# 
# #*** Model 1: 3PL model for item 5 only, other items 2PL
# mod1 <- mirt::mirt(data, 1, itemtype=c("2PL","2PL","2PL","2PL","3PL") , verbose=TRUE)
# print(mod1)
# summary(mod1)
# # extracting coefficients
# coef(mod1)
# mirt.wrapper.coef(mod1)$coef
# # extract parameter values in mirt
# mirt::mod2values(mod1)
# # extract posterior
# post1 <- mirt.wrapper.posterior(mod1)
# # extract item response functions
# probs1 <- IRT.irfprob( mod1 )
# str(probs1)
# # extract individual likelihood
# likemod1 <- IRT.likelihood( mod1 )
# str(likemod1)
# # extract individual posterior
# postmod1 <- IRT.posterior( mod1 )
# str(postmod1)
# 
# #*** Model 2: Confirmatory model with two factors
# cmodel <- mirt::mirt.model("
#         F1 = 1,4,5
#         F2 = 2,3
#         ")
# mod2 <- mirt::mirt(data, cmodel , verbose=TRUE)
# print(mod2)
# summary(mod2)
# # extract coefficients
# coef(mod2)
# mirt.wrapper.coef(mod2)$coef
# # extract posterior
# post2 <- mirt.wrapper.posterior(mod2)
# 
# #############################################################################
# # EXAMPLE 2: Extracting item parameters and posterior for differering 
# #            number of response catagories | Dataset Science
# #############################################################################
# 
# data(Science,package="mirt")
# library(psych)
# psych::describe(Science)
# 
# # modify dataset
# dat <- Science
# dat[ dat[,1] > 3 ,1] <- 3
# psych::describe(dat)
# 
# # estimate generalized partial credit model
# mod1 <- mirt::mirt(dat, 1, itemtype="gpcm")
# print(mod1)
# # extract coefficients
# coef(mod1)
# mirt.wrapper.coef(mod1)$coef
# # extract posterior
# post1 <- mirt.wrapper.posterior(mod1)
# 
# #############################################################################
# # EXAMPLE 3: Multiple group model; simulated dataset from mirt package
# #############################################################################
# 
# #*** simulate data (copy from the multipleGroup manual site in mirt package)
# set.seed(1234)
# a <- matrix(c(abs( stats::rnorm(5,1,.3)), rep(0,15),abs( stats::rnorm(5,1,.3)),
#           rep(0,15),abs( stats::rnorm(5,1,.3))), 15, 3)
# d <- matrix( stats::rnorm(15,0,.7),ncol=1)
# mu <- c(-.4, -.7, .1)
# sigma <- matrix(c(1.21,.297,1.232,.297,.81,.252,1.232,.252,1.96),3,3)
# itemtype <- rep("dich", nrow(a))
# N <- 1000
# dataset1 <- mirt::simdata(a, d, N, itemtype)
# dataset2 <- mirt::simdata(a, d, N, itemtype, mu = mu, sigma = sigma)
# dat <- rbind(dataset1, dataset2)
# group <- c(rep("D1", N), rep("D2", N))
# 
# #group models
# model <- mirt::mirt.model("
#    F1 = 1-5
#    F2 = 6-10
#    F3 = 11-15
#       ")
# 
# # separate analysis
# mod_configural <- mirt::multipleGroup(dat, model, group = group , verbose=TRUE)
# mirt.wrapper.coef(mod_configural)
# 
# # equal slopes (metric invariance)
# mod_metric <- mirt::multipleGroup(dat, model, group = group, invariance=c("slopes") , 
#                 verbose=TRUE)
# mirt.wrapper.coef(mod_metric)
#                   
# # equal slopes and intercepts (scalar invariance)
# mod_scalar <- mirt::multipleGroup(dat, model, group = group,  
#           invariance=c("slopes","intercepts","free_means","free_varcov"), verbose=TRUE)
# mirt.wrapper.coef(mod_scalar)
# 
# # full constraint
# mod_fullconstrain <- mirt::multipleGroup(dat, model, group = group,  
#              invariance=c("slopes", "intercepts") , verbose=TRUE )
# mirt.wrapper.coef(mod_fullconstrain)
# 
# #############################################################################
# # EXAMPLE 4: Nonlinear item response model
# #############################################################################
# 
# data(data.read)
# dat <- data.read
# # specify mirt model with some interactions
# mirtmodel <- mirt.model("
#    A = 1-4
#    B = 5-8
#    C = 9-12
#    (A*B) = 4,8
#    (C*C) = 9
#    (A*B*C) = 12
#    " )
# # estimate model
# res <- mirt::mirt( dat , mirtmodel , verbose=TRUE , technical=list(NCYCLES=3) )
# # look at estimated parameters
# mirt.wrapper.coef(res)
# coef(res)
# mirt::mod2values(res)
# # model specification
# res@model
# 
# #############################################################################
# # EXAMPLE 5: Extracting factor scores
# #############################################################################
# 
# data(data.read)
# dat <- data.read
# # define lavaan model and convert syntax to mirt
# lavmodel <- "
#     A=~ a*A1+a*A2+1.3*A3+A4       # set loading of A3 to 1.3
#     B=~ B1+1*B2+b3*B3+B4
#     C=~ c*C1+C2+c*C3+C4
#     A1 | da*t1
#     A3 | da*t1
#     C4 | dg*t1
#     B1 | 0*t1
#     B3 | -1.4*t1                  # fix item threshold of B3 to -1.4
#     A ~~ B                        # estimate covariance between A and B
#     A ~~ .6 * C                   # fix covariance to .6
#     B ~~ B                        # estimate variance of B
#     A ~ .5*1                      # set mean of A to .5
#     B ~ 1                         # estimate mean of B
#     "
# res <- lavaan2mirt( dat , lavmodel , verbose=TRUE , technical=list(NCYCLES=3) )
# # estimated coefficients
# mirt.wrapper.coef(res$mirt)
# # extract factor scores
# fres <- mirt.wrapper.fscores(res$mirt)
# # look at factor scores
# head( round(fres$person,2))
#   ##     case    M EAP.Var1 SE.EAP.Var1 EAP.Var2 SE.EAP.Var2 EAP.Var3 SE.EAP.Var3 MLE.Var1
#   ##   1    1 0.92     1.26        0.67     1.61        0.60     0.05        0.69     2.65
#   ##   2    2 0.58     0.06        0.59     1.14        0.55    -0.80        0.56     0.00
#   ##   3    3 0.83     0.86        0.66     1.15        0.55     0.48        0.74     0.53
#   ##   4    4 1.00     1.52        0.67     1.57        0.60     0.73        0.76     2.65
#   ##   5    5 0.50    -0.13        0.58     0.85        0.48    -0.82        0.55    -0.53
#   ##   6    6 0.75     0.41        0.63     1.09        0.54     0.27        0.71     0.00
#   ##     MLE.Var2 MLE.Var3 MAP.Var1 MAP.Var2 MAP.Var3
#   ##   1     2.65    -0.53     1.06     1.59     0.00
#   ##   2     1.06    -1.06     0.00     1.06    -1.06
#   ##   3     1.06     2.65     1.06     1.06     0.53
#   ##   4     2.65     2.65     1.59     1.59     0.53
#   ##   5     0.53    -1.06    -0.53     0.53    -1.06
#   ##   6     1.06     2.65     0.53     1.06     0.00
# # EAP reliabilities
# round(fres$EAP.rel,3)
#   ##    Var1  Var2  Var3 
#   ##   0.574 0.452 0.541 
# ## End(Not run)