Summary of Item Calibration Results

Summarizes the item calibration results in an object of class est_irt, est_mg, or est_item, returned by est_irt(), est_mg(), and est_item(), respectively.

Usage

summary(object, ...)

# S3 method for class 'est_irt'
summary(object, ...)

# S3 method for class 'est_mg'
summary(object, ...)

# S3 method for class 'est_item'
summary(object, ...)

# S3 method for class 'est_irt'
print(x, digits = max(2L, getOption("digits") - 5L), ...)

# S3 method for class 'est_mg'
print(x, digits = max(2L, getOption("digits") - 5L), ...)

# S3 method for class 'est_item'
print(x, digits = max(2L, getOption("digits") - 5L), ...)

# S3 method for class 'summary.est_irt'
print(x, digits = max(2L, getOption("digits") - 5L), ...)

# S3 method for class 'summary.est_mg'
print(x, digits = max(2L, getOption("digits") - 5L), ...)

# S3 method for class 'summary.est_item'
print(x, digits = max(2L, getOption("digits") - 5L), ...)

Arguments

object: An object of class est_irt, est_mg, or est_item.
...: Additional arguments passed to or from other methods (currently not used).
x: An object of class est_irt, est_mg, or est_item, or the summary of one of them, to be printed.
digits: For a summary object, the number of decimal places used to round the tables of item and group parameter estimates. Not used when an est_irt, est_mg, or est_item object itself is printed.

Value

An object of class summary.est_irt, summary.est_mg, or summary.est_item: a list of selected components of object (the call, the numbers of items and examinees, the estimation settings and convergence messages, the log-likelihood, AIC and BIC (not for est_item), and the item and group parameter estimates). Printing the object displays an overview of the calibration results.

Details

irtQ exports its own generic summary(), which calls the method of the class of object and therefore works as base::summary() for other objects. Printing a summary object rounds the tables of item and group parameter estimates to the number of decimal places given in the digits argument of the print method (default max(2L, getOption("digits") - 5L)).

Methods (by class)

summary(est_irt): An object created by the function est_irt().
summary(est_mg): An object created by the function est_mg().
summary(est_item): An object created by the function est_item().

Functions

print(est_irt): Print method for an object of class est_irt (given as x). It prints the number of EM cycles, the number of quadrature points, the two convergence checks, a note on the computation of the variance-covariance matrix, and the log-likelihood.
print(est_mg): Print method for an object of class est_mg (given as x). It prints the number of EM cycles, the number of quadrature points, the two convergence checks, a note on the computation of the variance-covariance matrix, and the log-likelihood.
print(est_item): Print method for an object of class est_item (given as x). It prints the convergence message of the fixed ability parameter calibration and the log-likelihood.
print(summary.est_irt): Print method for the summary of an est_irt object (given as x). It prints the full calibration summary with the tables rounded to digits decimal places.
print(summary.est_mg): Print method for the summary of an est_mg object (given as x). It prints the full calibration summary with the tables rounded to digits decimal places.
print(summary.est_item): Print method for the summary of an est_item object (given as x). It prints the full calibration summary with the tables rounded to digits decimal places.

Author

Hwanggyu Lim hglim83@gmail.com

Examples

# \donttest{
# Fit the 1PL model to the LSAT6 data, constraining the slopes to be equal
fit.1pl <- est_irt(data = LSAT6, D = 1, model = "1PLM", cats = 2,
                   fix.a.1pl = FALSE, verbose = FALSE)

# Display the calibration summary
summary(fit.1pl)
#> 
#> Call:
#> est_irt(data = LSAT6, D = 1, model = "1PLM", cats = 2, fix.a.1pl = FALSE, 
#>     verbose = FALSE)
#> 
#> Summary of the Data 
#>  Number of Items: 5
#>  Number of Cases: 1000
#> 
#> Summary of Estimation Process 
#>  Maximum number of EM cycles: 500
#>  Convergence criterion of E-step: 1e-04
#>  Number of rectangular quadrature points: 49
#>  Minimum & Maximum quadrature points: -6, 6
#>  Number of free parameters: 6
#>    - Item parameters: 6
#>    - Latent distribution parameters (mean and variance): 0
#>    - Latent distribution weights (empirical histogram): 0
#>  Number of fixed items: 0
#>  Number of E-step cycles completed: 22
#>  Maximum parameter change: 6.905886e-05
#> 
#> Processing time (in seconds) 
#>  EM algorithm: 0.09
#>  Standard error computation: 0
#>  Total computation: 0.1
#> 
#> Convergence and Stability of Solution 
#>  First-order test: Convergence criteria are satisfied.
#>  Second-order test: Solution is a possible local maximum.
#>  Computation of variance-covariance matrix: 
#>   Variance-covariance matrix of item parameter estimates is obtainable.
#> 
#> Summary of Estimation Results 
#>  -2loglikelihood: 4933.875
#>  Akaike Information Criterion (AIC): 4945.875
#>  Bayesian Information Criterion (BIC): 4975.322
#>  Item Parameters: 
#>    id  cats  model  par.1  se.1  par.2  se.2  par.3  se.3
#> 1  V1     2   1PLM   0.76  0.07  -3.62  0.32     NA    NA
#> 2  V2     2   1PLM   0.76    NA  -1.32  0.14     NA    NA
#> 3  V3     2   1PLM   0.76    NA  -0.32  0.10     NA    NA
#> 4  V4     2   1PLM   0.76    NA  -1.73  0.17     NA    NA
#> 5  V5     2   1PLM   0.76    NA  -2.78  0.25     NA    NA
#>  Group Parameters: 
#>            mu  sigma2  sigma
#> estimates   0       1      1
#> se         NA      NA     NA
#> 
# }