enriched_glm {enrichwith}

Fitting generalized linear models enriched with useful components

Description

enriched_glm fits generalized linear models using glm and then enriches the resulting object with all enrichment options.

Usage

enriched_glm(formula, family = gaussian, ...)

Arguments

formula

an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under ‘Details’.

family

a description of the error distribution and link function to be used in the model. For glm this can be a character string naming a family function, a family function or the result of a call to a family function. For glm.fit only the third option is supported. (See family for details of family functions.)

...

other arguments passed to glm

Details

enriched_glm has the same interface as glm

Value

An object of class enriched_glm that contains all the components of a glm object, along with a set of auxiliary functions (score function, information matrix, a simulate method, first term in the expansion of the bias of the maximum likelihood estimator, and dmodel, pmodel, qmodel), the maximum likelihood estimate of the dispersion parameter, the expected or observed information at the maximum likelihood estimator, and the first term in the expansion of the bias of the maximum likelihood estimator.

See enrich.glm for more details and links for the auxiliary functions.

Examples

## Not run: 
# A Gamma example, from McCullagh & Nelder (1989, pp. 300-2)
clotting <- data.frame(
   u = c(5,10,15,20,30,40,60,80,100, 5,10,15,20,30,40,60,80,100),
   time = c(118,58,42,35,27,25,21,19,18,69,35,26,21,18,16,13,12,12),
   lot = factor(c(rep(1, 9), rep(2, 9))))

# Fit a generalized linear model
cML <- enriched_glm(time ~ lot*log(u), data = clotting, family = Gamma("log"))

# Evaluate the densities at the data points in clotting at the
# maximum likelihood estimates
cML_dmodel <- get_dmodel_function(cML) # same as cML$auxiliary_functions$dmodel
cML_dmodel()

# Evaluate the densities at supplied data points
new_data <- data.frame(u = c(15:17, 15:17),
                       time = c(30:32, 15:17),
                       lot = factor(c(1, 1, 1, 2, 2, 2)))
cML_dmodel(data = new_data)

# Get pmodel and qmodel function
cML_pmodel <- get_pmodel_function(cML) # same as cML$auxiliary_functions$pmodel
cML_qmodel <- get_qmodel_function(cML) # same as cML$auxiliary_functions$qmodel

# The following should return c(30:32, 15:17)
probs <- cML_pmodel(data = new_data)
cML_qmodel(probs, data = new_data)

# Evaluate the observed information matrix at the MLE
cML_info <- get_information_function(cML)
cML_info(type = "observed")

# Wald tests based on the observed information at the
# moment based esimator of the dispersion
dispersion <- summary(cML)$dispersion
cML_vcov_observed <- solve(cML_info(dispersion = dispersion, type = "observed"))
lmtest::coeftest(cML, vcov = cML_vcov_observed)

# Wald tests based on the expected information at the
# moment based esimator of the dispersion
cML_vcov_expected <- solve(cML_info(dispersion = dispersion, type = "expected"))
lmtest::coeftest(cML, vcov = cML_vcov_expected)
# Same statistics as coef(summary(cML))[, "t value"]


## End(Not run)