Loglikelihood adjustments for fitted models

This function adjusts the loglikelihood of fitted model objects based on Chandler and Bate (2007). It is a generic function for different types of models, which are listed in Supported models. This section also contains links to function-specific help pages.

adj_loglik(x, cluster = NULL, use_vcov = TRUE, use_mle = TRUE, ...)

Arguments

x	A supported fitted model object, see Supported models
cluster	A vector or factor indicating the cluster the corresponding loglikelihood contribution belongs to. It is required to have the same length as the vector returned by `logLik_vec()`. If `cluster` is not supplied or `NULL`, then it is assumed that each observation forms its own cluster.
use_vcov	A logical scalar. By default, the `vcov()` method for `x` is used to estimate the Hessian of the independence loglikelihood, if the function exists. Otherwise, or if `use_vcov = FALSE`, `H` is estimated using `stats::optimHess()` inside `chandwich::adjust_loglik()`.
use_mle	A logical scalar. By default, the MLE from `x` is taken as given, and is not reestimated. By setting `use_mle` to `FALSE`, the parameters are reestimated in the function `chandwich::adjust_loglik()` using `stats::optim()`.This feature is currently for development purposes only, may return misleading/false results and may be removed without notice.
...	Further arguments to be passed to `sandwich::meatCL()` if `cluster` is defined, if `cluster = NULL`, they are passed into `sandwich::meat()`.

Value

An object of class "chantrics" inheriting from class "chandwich". See the documentation provided with chandwich::adjust_loglik().

Details

If use_vcov = TRUE, the current default, the function will test whether a vcov S3 method exists for x, and will take the variance-covariance matrix from there. Otherwise, or if use_vcov = FALSE the variance-covariance matrix of the MLE is estimated inside chandwich::adjust_loglik() using stats::optimHess().

Supported models

glm
glm.nb

Available methods

"chantrics" objects have the following methods available to them:

AIC
anova
alrtest - Adjusted Likelihood ratio tests
coef
lmtest::coeftest - \(z\) tests for all coefficients
confint and plot.confint - confidence intervals for all coefficients, and diagnostics plots for confint().
conf_intervals - enhanced confidence interval reports
conf_region - two-dimensional confidence regions
df.residual
fitted
logLik
logLik_vec
nobs
plot
predict
print
residuals
summary
terms
vcov

Examples

See the model-specific pages in the supported models section.

References

R. E. Chandler and S. Bate, Inference for clustered data using the independence loglikelihood, Biometrika, 94 (2007), pp. 167–183. doi: 10.1093/biomet/asm015 .