logLik.difORD {difNLR} | R Documentation |
Log-likelihood and information criteria for an object of
"difORD"
class.
Description
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 |
item |
numeric or character: either character |
... |
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
hladka@cs.cas.cz
Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
martinkova@cs.cas.cz
See Also
difORD
for DIF detection among
ordinal data.
logLik
for generic function
extracting log-likelihood.
AIC
for generic
function calculating AIC and BIC.
Examples
## Not run:
# 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)
## End(Not run)