Extract various elements from estimated model objects

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
F: unrotated standardized loadings matrix
h2: factor communality estimates
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

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples


# \donttest{
mod <- mirt(Science, 1)

extract.mirt(mod, 'logLik')
#> [1] -1608.87
extract.mirt(mod, 'F')
#>                F1
#> Comfort 0.5220496
#> Work    0.5844686
#> Future  0.8030199
#> Benefit 0.5410276

#multiple group model
grp <- rep(c('G1', 'G2'), each = nrow(Science)/2)
mod2 <- multipleGroup(Science, 1, grp)

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

# }