vcdExtra (version 0.8-5)

logLik.loglm: Log-Likelihood of a loglm Object

Description

Calculates the log-likelihood value of the loglm model represented by object evaluated at the estimated coefficients.

It allows the use of AIC and BIC, which require that a logLik method exists to extract the corresponding log-likelihood for the model.

Usage

# S3 method for loglm
logLik(object, ..., zero=1E-10)

Value

Returns an object of class logLik. This is a number with one attribute, "df" (degrees of freedom), giving the number of (estimated) parameters in the model.

Arguments

object

A loglm object

...

For compatibility with the S3 generic; not used here

zero

value used to replace zero frequencies in calculating the log-likelihood

Author

Achim Zeileis

Details

If cell frequencies have not been stored with the loglm object (via the argument keep.frequencies = TRUE), they are obtained using update.

This function calculates the log-likelihood in a way that allows for non-integer frequencies, such as the case where 0.5 has been added to all cell frequencies to allow for sampling zeros. If the frequencies still contain zero values, those are replaced by the value of start.

For integer frequencies, it gives the same result as the corresponding model fit using glm, whereas glm returns -Inf if there are any non-integer frequencies.

See Also

loglm, AIC, BIC,

Examples

data(Titanic, package="datasets")  

require(MASS)
titanic.mod1 <- loglm(~ (Class * Age * Sex) + Survived, data=Titanic)
titanic.mod2 <- loglm(~ (Class * Age * Sex) + Survived*(Class + Age + Sex), data=Titanic)
titanic.mod3 <- loglm(~ (Class * Age * Sex) + Survived*(Class + Age * Sex), data=Titanic)

logLik(titanic.mod1)
AIC(titanic.mod1, titanic.mod2, titanic.mod3)
BIC(titanic.mod1, titanic.mod2, titanic.mod3)

# compare with models fit using glm()
titanic <- as.data.frame(Titanic)
titanic.glm1 <- glm(Freq ~ (Class * Age * Sex) + Survived, 
                    data=titanic, family=poisson)
titanic.glm2 <- glm(Freq ~ (Class * Age * Sex) + Survived*(Class + Age + Sex), 
                    data=titanic, family=poisson)
titanic.glm3 <- glm(Freq ~ (Class * Age * Sex) + Survived*(Class + Age * Sex), 
                    data=titanic, family=poisson)

logLik(titanic.glm1)
AIC(titanic.glm1, titanic.glm2, titanic.glm3)
BIC(titanic.glm1, titanic.glm2, titanic.glm3)