Description Usage Arguments Details Value Examples

`enriched_glm`

fits generalized linear models using
`glm`

and then enriches the resulting object with all
enrichment options.

1 | ```
enriched_glm(formula, family = gaussian, ...)
``` |

`formula` |
an object of class |

`family` |
a description of the error distribution and link
function to be used in the model. For |

`...` |
other arguments passed to |

`enriched_glm`

has the same interface as `glm`

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | ```
## 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)
``` |

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