logLik.ddfMLR: Log-likelihood and information criteria for an object of...

In adelahladka/difNLR: DIF and DDF Detection by Non-Linear Regression Models

Log-likelihood and information criteria for an object of "ddfMLR" class.

Description

S3 methods for extracting log-likelihood, Akaike's information criterion (AIC) and Schwarz's Bayesian criterion (BIC) for an object of "ddfMLR" class.

Usage

## S3 method for class 'ddfMLR'
logLik(object, item = "all", ...)

## S3 method for class 'ddfMLR'
AIC(object, item = "all", ...)

## S3 method for class 'ddfMLR'
BIC(object, item = "all", ...)

Arguments

object	an object of "ddfMLR" class.
item	numeric or character: either character "all" to apply for all converged items (default), or a vector of item names (column names of Data), or item identifiers (integers specifying the column number).
...	other generic parameters for S3 methods.

Author(s)

Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
[e-mail]

Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
[e-mail]

See Also

ddfMLR for DDF detection among nominal data.
logLik for generic function extracting log-likelihood.
AIC for generic function calculating AIC and BIC.

Examples

## Not run: 
# loading data
data(GMATtest, GMATkey)
Data <- GMATtest[, 1:20] # items
group <- GMATtest[, "group"] # group membership variable
key <- GMATkey # correct answers

# testing both DDF effects
(x <- ddfMLR(Data, group, focal.name = 1, key))

# AIC, BIC, log-likelihood
AIC(x)
BIC(x)
logLik(x)

# AIC, BIC, log-likelihood for the first item
AIC(x, item = 1)
BIC(x, item = 1)
logLik(x, item = 1)

## End(Not run)

adelahladka/difNLR documentation built on Dec. 23, 2024, 2:20 a.m.