logLik.difORD {difNLR} | R Documentation |
"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.
## 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", ...)
object |
an object of |
item |
numeric or character: either character |
... |
other generic parameters for S3 methods. |
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
difORD
for DIF detection among
ordinal data.
logLik
for generic function
extracting log-likelihood.
AIC
for generic
function calculating AIC and BIC.
## Not run: # loading data data(dataMedicalgraded, package = "ShinyItemAnalysis") Data <- dataMedicalgraded[, 1:5] # items group <- dataMedicalgraded[, 101] # 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)