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Some Functions for Wrapping with the mirt Package

mirt.wrapper.Rd

Some functions for wrapping with the mirt package.

Usage

# extract coefficients
mirt.wrapper.coef(mirt.obj)

# summary output
mirt_summary(object, digits=4, file=NULL, ...)

# extract posterior, likelihood, ...
mirt.wrapper.posterior(mirt.obj, weights=NULL, group=NULL)
# S3 method for SingleGroupClass
IRT.likelihood(object, ...)
# S3 method for MultipleGroupClass
IRT.likelihood(object, ...)
# S3 method for SingleGroupClass
IRT.posterior(object, ...)
# S3 method for MultipleGroupClass
IRT.posterior(object, ...)
# S3 method for SingleGroupClass
IRT.expectedCounts(object, ...)
# S3 method for MultipleGroupClass
IRT.expectedCounts(object, ...)

# S3 method for extracting item response functions
# S3 method for SingleGroupClass
IRT.irfprob(object, ...)
# S3 method for MultipleGroupClass
IRT.irfprob(object, group=1, ...)

# 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.

group

Group index for IRT.irfprob (only applicable for object of class MultipleGroupClass)

digits

Number of digits after decimal used for rounding

file

File name for sinking summary output

weights

Optional vector of student weights

ask

Optional logical indicating whether each new plot should be confirmed.

...

Further arguments to be passed.

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.

Value

Function mirt.wrapper.coef -- List with entries

coef

Data frame with item parameters

GroupPars

Data frame or list with distribution parameters

Function mirt.wrapper.posterior -- List with entries

theta.k

Grid of theta points

pi.k

Trait distribution on theta.k

f.yi.qk

Individual likelihood

f.qk.yi

Individual posterior

n.ik

Expected counts

data

Used dataset

Function mirt.wrapper.fscores -- List with entries

person

Data frame with person parameter estimates (factor scores) EAP, MAP and MLE for all dimensions.

EAP.rel

EAP reliabilities

See also

See the mirt package manual for more information.

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

See mirt::coef-method for 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 for the mirt 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. Multidimensional 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)

Examples

if (FALSE) {
# 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
# summary output
mirt_summary(mod1)
# extract parameter values in mirt
mirt::mod2values(mod1)
# extract posterior
post1 <- sirt::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 <- sirt::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 <- sirt::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", "free_means", "free_var"), 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 <- sirt::lavaan2mirt( dat, lavmodel, verbose=TRUE, technical=list(NCYCLES=3) )
# estimated coefficients
mirt.wrapper.coef(res$mirt)
# extract factor scores
fres <- sirt::mirt.wrapper.fscores(res$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
}