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

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

Usage

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

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

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

Arguments

object

an object of "difORD" 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.

See also

difORD for DIF detection among ordinal data.
logLik for generic function extracting log-likelihood.
AIC for generic function calculating AIC and BIC.

Author

Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
hladka@cs.cas.cz

Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
martinkova@cs.cas.cz

Examples

if (FALSE) { # \dontrun{
# loading data
data(Anxiety, package = "ShinyItemAnalysis")
Data <- Anxiety[, paste0("R", 1:29)] # items
group <- Anxiety[, "gender"] # group membership variable

# testing both DIF effects with adjacent category logit model
(x <- difORD(Data, group, focal.name = 1, model = "adjacent"))

# 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)
} # }