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Extract various elements from estimated model objects

Source: R/extract.mirt.R
extract.mirt.Rd

A generic function to extract the internal objects from estimated models.

Usage

extract.mirt(x, what, item = 1)

Arguments

x

mirt model of class 'SingleGroupClass', 'MultipleGroupClass', 'MixedClass' or 'DiscreteGroupClass'

what

a string indicating what to extract

item

if necessary, which item to extract information from. Defaults to 1 if not specified

Details

Objects which can be extracted from mirt objects include:

logLik

observed log-likelihood

logPrior

log term contributed by prior parameter distributions

G2

goodness of fit statistic

df

degrees of freedom

p

p-value for G2 statistic

RMSEA

root mean-square error of approximation based on G2

CFI

CFI fit statistic

TLI

TLI fit statistic

AIC

AIC

BIC

BIC

SABIC

sample size adjusted BIC

HQ

HQ

LLhistory

EM log-likelihood history

tabdata

a tabular version of the raw response data input. Frequencies are stored in freq

freq

frequencies associated with tabdata

K

an integer vector indicating the number of unique elements for each item

mins

an integer vector indicating the lowest category found in the input data

model

input model syntax

method

estimation method used

itemtype

a vector of item types for each respective item (e.g., 'graded', '2PL', etc)

itemnames

a vector of item names from the input data

factorNames

a vector of factor names from the model definition

rowID

an integer vector indicating all valid row numbers used in the model estimation (when all cases are used this will be 1:nrow(data))

data

raw input data of item responses

covdata

raw input data of data used as covariates

tabdatalong

similar to tabdata, however the responses have been transformed into dummy coded variables

fulldatalong

analogous to tabdatafull, but for the raw input data instead of the tabulated frequencies

EMhistory

if saved, extract the EM iteration history

exp_resp

expected probability of the unique response patterns

survey.weights

if supplied, the vector of survey weights used during estimation (NULL if missing)

converged

a logical value indicating whether the model terminated within the convergence criteria

iterations

number of iterations it took to reach the convergence criteria

nest

number of freely estimated parameters

parvec

vector containing uniquely estimated parameters

vcov

parameter covariance matrix (associated with parvec)

condnum

the condition number of the Hessian (if computed). Otherwise NA

constrain

a list of item parameter constraints to indicate which item parameters were equal during estimation

Prior

prior density distribution for the latent traits

thetaPosterior

posterior distribution for latent traits when using EM algorithm

key

if supplied, the data scoring key

nfact

number of latent traits/factors

nitems

number of items

ngroups

number of groups

groupNames

character vector of unique group names

group

a character vector indicating the group membership

invariance

a character vector indicating invariance input from multipleGroup

secondordertest

a logical indicating whether the model passed the second-order test based on the Hessian matrix. Indicates whether model is a potential local maximum solution

SEMconv

logical; check whether the supplemented EM information matrix converged. Will be NA if not applicable

time

estimation time, broken into different sections

References

Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi:10.18637/jss.v048.i06

See also

extract.group, extract.item, mod2values

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples


# \donttest{
mod <- mirt(Science, 1)
#> 
Iteration: 1, Log-Lik: -1629.361, Max-Change: 0.50660
Iteration: 2, Log-Lik: -1617.374, Max-Change: 0.25442
Iteration: 3, Log-Lik: -1612.894, Max-Change: 0.16991
Iteration: 4, Log-Lik: -1610.306, Max-Change: 0.10461
Iteration: 5, Log-Lik: -1609.814, Max-Change: 0.09162
Iteration: 6, Log-Lik: -1609.534, Max-Change: 0.07363
Iteration: 7, Log-Lik: -1609.030, Max-Change: 0.03677
Iteration: 8, Log-Lik: -1608.988, Max-Change: 0.03200
Iteration: 9, Log-Lik: -1608.958, Max-Change: 0.02754
Iteration: 10, Log-Lik: -1608.878, Max-Change: 0.01443
Iteration: 11, Log-Lik: -1608.875, Max-Change: 0.00847
Iteration: 12, Log-Lik: -1608.873, Max-Change: 0.00515
Iteration: 13, Log-Lik: -1608.872, Max-Change: 0.00550
Iteration: 14, Log-Lik: -1608.872, Max-Change: 0.00318
Iteration: 15, Log-Lik: -1608.871, Max-Change: 0.00462
Iteration: 16, Log-Lik: -1608.871, Max-Change: 0.00277
Iteration: 17, Log-Lik: -1608.870, Max-Change: 0.00145
Iteration: 18, Log-Lik: -1608.870, Max-Change: 0.00175
Iteration: 19, Log-Lik: -1608.870, Max-Change: 0.00126
Iteration: 20, Log-Lik: -1608.870, Max-Change: 0.00025
Iteration: 21, Log-Lik: -1608.870, Max-Change: 0.00285
Iteration: 22, Log-Lik: -1608.870, Max-Change: 0.00108
Iteration: 23, Log-Lik: -1608.870, Max-Change: 0.00022
Iteration: 24, Log-Lik: -1608.870, Max-Change: 0.00059
Iteration: 25, Log-Lik: -1608.870, Max-Change: 0.00014
Iteration: 26, Log-Lik: -1608.870, Max-Change: 0.00068
Iteration: 27, Log-Lik: -1608.870, Max-Change: 0.00065
Iteration: 28, Log-Lik: -1608.870, Max-Change: 0.00019
Iteration: 29, Log-Lik: -1608.870, Max-Change: 0.00061
Iteration: 30, Log-Lik: -1608.870, Max-Change: 0.00012
Iteration: 31, Log-Lik: -1608.870, Max-Change: 0.00012
Iteration: 32, Log-Lik: -1608.870, Max-Change: 0.00058
Iteration: 33, Log-Lik: -1608.870, Max-Change: 0.00055
Iteration: 34, Log-Lik: -1608.870, Max-Change: 0.00015
Iteration: 35, Log-Lik: -1608.870, Max-Change: 0.00052
Iteration: 36, Log-Lik: -1608.870, Max-Change: 0.00010

extract.mirt(mod, 'logLik')
#> [1] -1608.87
extract.mirt(mod, 'K')  # unique categories for each item
#> [1] 4 4 4 4

#multiple group model
grp <- rep(c('G1', 'G2'), each = nrow(Science)/2)
mod2 <- multipleGroup(Science, 1, grp)
#> 
Iteration: 1, Log-Lik: -1629.361, Max-Change: 1.10657
Iteration: 2, Log-Lik: -1609.594, Max-Change: 0.44262
Iteration: 3, Log-Lik: -1603.139, Max-Change: 0.33923
Iteration: 4, Log-Lik: -1600.524, Max-Change: 0.23152
Iteration: 5, Log-Lik: -1599.314, Max-Change: 0.17794
Iteration: 6, Log-Lik: -1598.777, Max-Change: 0.13751
Iteration: 7, Log-Lik: -1598.257, Max-Change: 0.03517
Iteration: 8, Log-Lik: -1598.189, Max-Change: 0.03698
Iteration: 9, Log-Lik: -1598.136, Max-Change: 0.03742
Iteration: 10, Log-Lik: -1597.913, Max-Change: 0.04062
Iteration: 11, Log-Lik: -1597.889, Max-Change: 0.03424
Iteration: 12, Log-Lik: -1597.869, Max-Change: 0.03294
Iteration: 13, Log-Lik: -1597.778, Max-Change: 0.02383
Iteration: 14, Log-Lik: -1597.768, Max-Change: 0.02590
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Iteration: 30, Log-Lik: -1597.666, Max-Change: 0.00605
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grp1 <- extract.group(mod2, 1) #extract single group model
extract.mirt(mod2, 'parvec')
#>  [1]  0.8316800  4.8913029  2.5597789 -1.3592138  0.8289135  2.4772334
#>  [7]  0.7025447 -2.1135580  2.8809926  5.4820292  2.2126281 -2.5451325
#> [13]  0.6920437  3.0133748  0.8608953 -1.5516666  1.1955161  4.8900152
#> [19]  2.6929286 -1.5545256  1.7621713  3.6416308  1.1724290 -2.5499919
#> [25]  2.4324588  6.4188352  2.6336468 -1.8297283  1.4875546  3.8081350
#> [31]  1.1473714 -1.8343958
extract.mirt(grp1, 'parvec')
#>  [1]  0.8316800  4.8913029  2.5597789 -1.3592138  0.8289135  2.4772334
#>  [7]  0.7025447 -2.1135580  2.8809926  5.4820292  2.2126281 -2.5451325
#> [13]  0.6920437  3.0133748  0.8608953 -1.5516666

# }