Log-Likelihood of a loglm Object

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 class 'loglm'
logLik(object, ..., zero=1E-10)

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

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.

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.

Author

Achim Zeileis

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)
#> 'log Lik.' -399.6822 (df=17)
AIC(titanic.mod1, titanic.mod2, titanic.mod3)
#>              df      AIC
#> titanic.mod1 17 833.3644
#> titanic.mod2 22 283.9687
#> titanic.mod3 23 267.9503
BIC(titanic.mod1, titanic.mod2, titanic.mod3)
#>              df      BIC
#> titanic.mod1 17 858.2819
#> titanic.mod2 22 316.2149
#> titanic.mod3 23 301.6622

# 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)
#> 'log Lik.' -399.6822 (df=17)
AIC(titanic.glm1, titanic.glm2, titanic.glm3)
#>              df      AIC
#> titanic.glm1 17 833.3644
#> titanic.glm2 22 283.9687
#> titanic.glm3 23 267.9503
BIC(titanic.glm1, titanic.glm2, titanic.glm3)
#>              df      BIC
#> titanic.glm1 17 858.2819
#> titanic.glm2 22 316.2149
#> titanic.glm3 23 301.6622