| Title: | Methods to Enrich R Objects with Extra Components |
|---|---|
| Description: | Provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class 'family', 'link-glm', 'lm', 'glm' and 'betareg'. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the 'enriched_glm' function that has the same interface as 'glm' but results in objects of class 'enriched_glm'. In addition to the usual components in a `glm` object, 'enriched_glm' objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects. |
| Authors: | Ioannis Kosmidis [aut, cre] |
| Maintainer: | Ioannis Kosmidis <[email protected]> |
| License: | GPL-2 | GPL-3 |
| Version: | 0.3 |
| Built: | 2025-01-24 04:57:34 UTC |
| Source: | https://github.com/ikosmidis/enrichwith |
enriched_glm
Function to extract model coefficients from objects of class enriched_glm
## S3 method for class 'enriched_glm' coef(object, model = c("mean", "full", "dispersion"), ...)## S3 method for class 'enriched_glm' coef(object, model = c("mean", "full", "dispersion"), ...)
object |
an object of class |
model |
either "mean" for the estimates of the parameters in the linear predictor, or "dispersion" for the estimate of the dispersion, or "full" for all estimates |
... |
currently unused |
enriched_lm
Function to extract model coefficients from objects of class enriched_lm
## S3 method for class 'enriched_lm' coef(object, model = c("mean", "full", "dispersion"), ...)## S3 method for class 'enriched_lm' coef(object, model = c("mean", "full", "dispersion"), ...)
object |
an object of class |
model |
either "mean" for the estimates of the parameters in the linear predictor, or "dispersion" for the estimate of the dispersion, or "full" for all estimates |
... |
currently unused |
Create an enrichwith skeleton file for the structured implementation of methods to compute new components for objects of a specific class
create_enrichwith_skeleton(class, option, description, component, path, filename = paste0(class, "_options.R"), attempt_rename = TRUE)create_enrichwith_skeleton(class, option, description, component, path, filename = paste0(class, "_options.R"), attempt_rename = TRUE)
class |
the class of the objects to be enriched |
option |
a character vector with components the enrichment options |
description |
a character vector of length
|
component |
a list of as many character vectors as
|
path |
the path where the skeleton file will be created |
filename |
the name of the skeleton file |
attempt_rename |
attempt to rename syntactically incorrect
component names? Default is |
A file with the necessary functions to use
enrichwith infrastructure. The skeleton consists of the
following functions
One compute_component.class function per component
name from unique(unlist(component)). The function takes as
input the object to be enriched and returns as output the component
to be added to the object.
The get_enrichment_options.class function, that takes
as input the object to be enriched and an enrichment option, and
returns the names of the components that will be appended to the
object for this option. This function can also be used to list the
available options and print their description.
The enrich.class function
## Not run: # Set the directory where the skeleton is placed my_path <- "~/Downloads" # This is the call that created the enrichment skeleton for glms # that ships with the package create_enrichwith_skeleton(class = "glm", option = c("auxiliary functions", "score vector", "mle of dispersion", "expected information", "observed information", "first-order bias"), description = c("various likelihood-based quantities (gradient of the log-likelihood, expected and observed information matrix and first term in the expansion of the bias of the mle) and a simulate method as functions of the model parameters", "gradient of the log-likelihood at the mle", "mle of the dispersion parameter", "expected information matrix evaluated at the mle", "observed information matrix evaluated at the mle", "first term in the expansion of the bias of the mle at the mle"), component = list("auxiliary_functions", "score_mle", "dispersion_mle", "expected_information_mle", "observed_information_mle", "bias_mle"), path = my_path, attempt_rename = FALSE) ## End(Not run)## Not run: # Set the directory where the skeleton is placed my_path <- "~/Downloads" # This is the call that created the enrichment skeleton for glms # that ships with the package create_enrichwith_skeleton(class = "glm", option = c("auxiliary functions", "score vector", "mle of dispersion", "expected information", "observed information", "first-order bias"), description = c("various likelihood-based quantities (gradient of the log-likelihood, expected and observed information matrix and first term in the expansion of the bias of the mle) and a simulate method as functions of the model parameters", "gradient of the log-likelihood at the mle", "mle of the dispersion parameter", "expected information matrix evaluated at the mle", "observed information matrix evaluated at the mle", "first term in the expansion of the bias of the mle at the mle"), component = list("auxiliary_functions", "score_mle", "dispersion_mle", "expected_information_mle", "observed_information_mle", "bias_mle"), path = my_path, attempt_rename = FALSE) ## End(Not run)
Histology grade and risk factors for 79 cases of endometrial cancer
endometrialendometrial
A data frame with 79 rows and 4 variables:
neovasculization with coding 0 for absent and 1 for present
pulsality index of arteria uterina
endometrium heigh
histology grade with coding 0 for low grade and 1 for high grade
The packaged data set was downloaded in .dat format
from http://www.stat.ufl.edu/~aa/glm/data. The latter
link provides the data sets used in Agresti (2015).
The endometrial data set was first analysed in Heinze and Schemper (2002), and was originally provided by Dr E. Asseryanis from the Medical University of Vienna.
Agresti, A. 2015. *Foundations of Linear and Generalized Linear Models*. Wiley Series in Probability and Statistics. Wiley.
Heinze, G., and M. Schemper. 2002. “A Solution to the Problem of Separation in Logistic Regression.” *Statistics in Medicine* 21:2409–19.
Generic method for enriching objects
enrich(object, with, ...)enrich(object, with, ...)
object |
the object to be enriched |
with |
a character vector with enrichment options for |
... |
Arguments to be passed to other methods |
enrich.glm, enriched_glm, enrich.family, enrich.link-glm, enrich.betareg
Enrich objects of class betareg with any or all of a
set of auxiliary functions, the expected information at the maximum
likelihood estimator, and the first term in the expansion of the
bias of the maximum likelihood estimator.
## S3 method for class 'betareg' enrich(object, with = "all", ...)## S3 method for class 'betareg' enrich(object, with = "all", ...)
object |
an object of class |
with |
a character vector of options for the enrichment of |
... |
extra arguments to be passed to the
|
The auxiliary_functions component consists of any or all of the following functions:
score: the log-likelihood derivatives as a function of the model parameters; see get_score_function.betareg
information: the expected information as a function of the model parameters; see get_information_function.betareg
bias: the first-order term in the expansion of the bias of the maximum likelihood estimator as a function of the model parameters; see get_bias_function.betareg
simulate: a simulate function for betareg objects that can simulate variates from the model at user-supplied parameter values for the regression parameters (default is the maximum likelihood estimates); see get_simulate_function.betareg
The object object of class betareg
with extra components. get_enrichment_options.betareg()
returns the components and their descriptions.
## Not run: if (require("betareg")) { data("GasolineYield", package = "betareg") gy <- betareg(yield ~ batch + temp, data = GasolineYield) # Get a function that evaluates the expected information for gy at supplied parameter values gy_info <- get_information_function(gy) . # compare standard errors with what `summary` returns all.equal(sqrt(diag(solve(gy_info())))[1:11], coef(summary(gy))$mean[, 2], check.attributes = FALSE) . # evaluating at different parameter values gy_info(rep(1, length = 12)) # Get a function that evaluates the first-order bias of gy at supplied parameter values gy_bias <- get_bias_function(gy) # compare with internal betareg implementation of bias correction gy_bc <- update(gy, type = "BC") all.equal(gy_bias(coef(gy)), gy_bc$bias, check.attributes = FALSE) } ## End(Not run)## Not run: if (require("betareg")) { data("GasolineYield", package = "betareg") gy <- betareg(yield ~ batch + temp, data = GasolineYield) # Get a function that evaluates the expected information for gy at supplied parameter values gy_info <- get_information_function(gy) . # compare standard errors with what `summary` returns all.equal(sqrt(diag(solve(gy_info())))[1:11], coef(summary(gy))$mean[, 2], check.attributes = FALSE) . # evaluating at different parameter values gy_info(rep(1, length = 12)) # Get a function that evaluates the first-order bias of gy at supplied parameter values gy_bias <- get_bias_function(gy) # compare with internal betareg implementation of bias correction gy_bc <- update(gy, type = "BC") all.equal(gy_bias(coef(gy)), gy_bc$bias, check.attributes = FALSE) } ## End(Not run)
family
Enrich objects of class family with family-specific
mathematical functions
## S3 method for class 'family' enrich(object, with = "all", ...)## S3 method for class 'family' enrich(object, with = "all", ...)
object |
an object of class |
with |
a character vector with enrichment options for |
... |
extra arguments to be passed to the |
family objects specify characteristics of the
models used by functions such as glm. The
families implemented in the stats package include
binomial, gaussian,
Gamma, inverse.gaussian,
and poisson, which are all special cases of
the exponential family of distributions that have probability mass
or density function of the form
where is an observation
weight, and , ,
and are sufficiently
smooth, real-valued functions.
The current implementation of family objects
includes the variance function (variance), the deviance
residuals (dev.resids), and the Akaike information criterion
(aic). See, also family.
The enrich method can further enrich exponential
family distributions with in
terms of (theta), the functions
(bfun),
(c1fun), (c2fun),
(fun), the first two derivatives of
(d1variance and d2variance,
respectively), and the first four derivatives of
(d1afun, d2afun,
d3afun, d4afun, respectively).
Corresponding enrichment options are also avaialble for
quasibinomial,
quasipoisson and wedderburn
families.
The quasi families are enriched with
d1variance and d2variance.
See enrich.link-glm for the enrichment of
link-glm objects.
The object object of class family with
extra components. get_enrichment_options.family()
returns the components and their descriptions.
## An example from ?glm to illustrate that things still work with ## enriched families counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9) treatment <- gl(3,3) print(d.AD <- data.frame(treatment, outcome, counts)) glm.D93 <- glm(counts ~ outcome + treatment, family = enrich(poisson())) anova(glm.D93) summary(glm.D93)## An example from ?glm to illustrate that things still work with ## enriched families counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9) treatment <- gl(3,3) print(d.AD <- data.frame(treatment, outcome, counts)) glm.D93 <- glm(counts ~ outcome + treatment, family = enrich(poisson())) anova(glm.D93) summary(glm.D93)
glm
Enrich objects of class glm with any or all of a set
of auxiliary functions, 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.
## S3 method for class 'glm' enrich(object, with = "all", ...)## S3 method for class 'glm' enrich(object, with = "all", ...)
object |
an object of class glm |
with |
a character vector of options for the enrichment of |
... |
extra arguments to be passed to the
|
The auxiliary_functions component consists of any or all of the following functions:
score: the log-likelihood derivatives as a function of the model parameters; see get_score_function.glm
information: the expected or observed information as a function of the model parameters; see get_information_function.glm
bias: the first-order term in the expansion of the bias of the maximum likelihood estimator as a function of the model parameters; see get_bias_function.glm
simulate: a simulate function for glm objects that can simulate variates from the model at user-supplied parameter values for the regression parameters and the dispersion (default is the maximum likelihood estimates); see get_simulate_function.glm
dmodel: computes densities or probability mass functions under the model at user-supplied data.frames and at user-supplied values for the regression parameters and the dispersion, if any (default is at the maximum likelihood estimates); see get_dmodel_function.glm
pmodel: computes distribution functions under the model at user-supplied data.frames and at user-supplied values for the regression parameters and the dispersion, if any (default is at the maximum likelihood estimates); see get_pmodel_function.glm
qmodel: computes quantile functions under the model at user-supplied data.frames and at user-supplied values for the regression parameters and the dispersion, if any (default is at the maximum likelihood estimates); see get_qmodel_function.glm
The object object of class glm with extra
components. See get_enrichment_options.glm() for the
components and their descriptions.
## 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)))) cML <- glm(time ~ lot*log(u), data = clotting, family = Gamma) # The simulate method for the above fit would simulate at coef(cML) # for the regression parameters and MASS::gamma.dispersion(cML) for # the dispersion. It is not possible to simulate at different # parameter values than those, at least not, without "hacking" the # cML object. # A general simulator for cML results via its enrichment with # auxiliary functions: cML_functions <- get_auxiliary_functions(cML) # which is a shorthand for # enriched_cML <- enrich(cML, with = "auxiliary functions") # cML_functions <- enriched_cML$auxiliary_functions # To simulate 2 samples at the maximum likelihood estimator do dispersion_mle <- MASS::gamma.dispersion(cML) cML_functions$simulate(coef = coef(cML), dispersion = dispersion_mle, nsim = 2, seed = 123) # To simulate 5 samples at c(0.1, 0.1, 0, 0) and dispersion 0.2 do cML_functions$simulate(coef = c(0.1, 0.1, 0, 0), dispersion = 0.2, nsim = 5, seed = 123) ## End(Not run) ## Not run: ## Reproduce left plot in Figure 4.1 in Kosimdis (2007) ## (see http://www.ucl.ac.uk/~ucakiko/files/ikosmidis_thesis.pdf) mod <- glm(1 ~ 1, weights = 10, family = binomial()) enriched_mod <- enrich(mod, with = "auxiliary functions") biasfun <- enriched_mod$auxiliary_functions$bias probabilities <- seq(1e-02, 1 - 1e-02, length = 100) biases <- Vectorize(biasfun)(qlogis(probabilities)) plot(probabilities, biases, type = "l", ylim = c(-0.5, 0.5), xlab = expression(pi), ylab = "first-order bias") abline(h = 0, lty = 2); abline(v = 0.5, lty = 2) title("First-order bias of the MLE of the log-odds", sub = "m = 10") ## End(Not run)## Not run: # A Gamma example, from McCullagh & Nelder (1989, pp. 300-2) clotting <- data.frame( u = c(5,10,15,20,30,40,60,80,100, 5,10,15,20,30,40,60,80,100), time = c(118,58,42,35,27,25,21,19,18,69,35,26,21,18,16,13,12,12), lot = factor(c(rep(1, 9), rep(2, 9)))) cML <- glm(time ~ lot*log(u), data = clotting, family = Gamma) # The simulate method for the above fit would simulate at coef(cML) # for the regression parameters and MASS::gamma.dispersion(cML) for # the dispersion. It is not possible to simulate at different # parameter values than those, at least not, without "hacking" the # cML object. # A general simulator for cML results via its enrichment with # auxiliary functions: cML_functions <- get_auxiliary_functions(cML) # which is a shorthand for # enriched_cML <- enrich(cML, with = "auxiliary functions") # cML_functions <- enriched_cML$auxiliary_functions # To simulate 2 samples at the maximum likelihood estimator do dispersion_mle <- MASS::gamma.dispersion(cML) cML_functions$simulate(coef = coef(cML), dispersion = dispersion_mle, nsim = 2, seed = 123) # To simulate 5 samples at c(0.1, 0.1, 0, 0) and dispersion 0.2 do cML_functions$simulate(coef = c(0.1, 0.1, 0, 0), dispersion = 0.2, nsim = 5, seed = 123) ## End(Not run) ## Not run: ## Reproduce left plot in Figure 4.1 in Kosimdis (2007) ## (see http://www.ucl.ac.uk/~ucakiko/files/ikosmidis_thesis.pdf) mod <- glm(1 ~ 1, weights = 10, family = binomial()) enriched_mod <- enrich(mod, with = "auxiliary functions") biasfun <- enriched_mod$auxiliary_functions$bias probabilities <- seq(1e-02, 1 - 1e-02, length = 100) biases <- Vectorize(biasfun)(qlogis(probabilities)) plot(probabilities, biases, type = "l", ylim = c(-0.5, 0.5), xlab = expression(pi), ylab = "first-order bias") abline(h = 0, lty = 2); abline(v = 0.5, lty = 2) title("First-order bias of the MLE of the log-odds", sub = "m = 10") ## End(Not run)
link-glm
Enrich objects of class link-glm with
further derivatives of linkinv with respect to eta.
## S3 method for class ''link-glm'' enrich(object, with = "all", ...)## S3 method for class ''link-glm'' enrich(object, with = "all", ...)
object |
an object of class |
with |
a character vector with enrichment options for |
... |
extra arguments to be passed to the |
The enrich.link-glm method supports logit,
probit, cauchit, cloglog, identity,
log, sqrt, 1/mu^2, inverse, as well as
the power family of links.
The object object of class link-glm
with extra components. get_enrichment_options.link-glm()
returns the components and their descriptions.
# Example elogit <- enrich(make.link("logit"), with = "inverse link derivatives") str(elogit) elogit$d2mu.deta elogit$d3mu.deta# Example elogit <- enrich(make.link("logit"), with = "inverse link derivatives") str(elogit) elogit$d2mu.deta elogit$d3mu.deta
lm
Enrich objects of class lm with any or all of a set
auxiliary functions, 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.
## S3 method for class 'lm' enrich(object, with = "all", ...)## S3 method for class 'lm' enrich(object, with = "all", ...)
object |
an object of class lm |
with |
a character vector of options for the enrichment of |
... |
extra arguments to be passed to the
|
The auxiliary functions consist of the score functions, the
expected or observed information, the first-order bias of the
maximum likelihood estimator as functions of the model parameters,
and a simulate function that takes as input the model
parameters (including the dispersion if any). The result from the
simulate auxiliary function has the same structure to that
of the simulate method for lm objects.
The object object of class lm with extra
components. get_enrichment_options.lm() returns the
components and their descriptions.
enriched_glm fits generalized linear models using
glm and then enriches the resulting object with all
enrichment options.
enriched_glm(formula, family = gaussian, ...)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.
## 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)$dispersion cML_vcov_observed <- solve(cML_info(dispersion = dispersion, type = "observed")) lmtest::coeftest(cML, vcov = cML_vcov_observed) # Wald tests based on the expected information at the # moment based esimator of the dispersion cML_vcov_expected <- solve(cML_info(dispersion = dispersion, type = "expected")) lmtest::coeftest(cML, vcov = cML_vcov_expected) # Same statistics as coef(summary(cML))[, "t value"] ## End(Not run)## Not run: # A Gamma example, from McCullagh & Nelder (1989, pp. 300-2) clotting <- data.frame( u = c(5,10,15,20,30,40,60,80,100, 5,10,15,20,30,40,60,80,100), time = c(118,58,42,35,27,25,21,19,18,69,35,26,21,18,16,13,12,12), lot = factor(c(rep(1, 9), rep(2, 9)))) # Fit a generalized linear model cML <- enriched_glm(time ~ lot*log(u), data = clotting, family = Gamma("log")) # Evaluate the densities at the data points in clotting at the # maximum likelihood estimates cML_dmodel <- get_dmodel_function(cML) # same as cML$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)
The enrichwith package provides the enrich method to
enrich list-like R objects with new, relevant components. The
resulting objects preserve their class, so all methods associated
with them still apply. The package can also be used to produce
customisable source code templates for the structured
implementation of methods to compute new components
Depending on the object, enriching it can be a tedious task. The enrichwith package aims to streamline the task into 3 simple steps:
Use create_enrichwith_skeleton to produce a
customisable enrichwith template.
Edit the compute_* functions by adding the specific
code that calculates the components.
Finalise the documentation and/or include more examples.
The first step results in a template that includes all necessary
functions to carry out the enrichment. The second step is where the
user edits the template and implements the calculation of the
components that the object will be enriched with. Specifically,
each compute_* function takes as input the object to be
enriched and returns the corresponding new component to be added to
the object.
Everything else (for example, mapping between the enrichment options and the components that the enriched object will have, checks that an enrichment option exists, listing enrichment options, enriching the object, and so on) is taken care of by the methods in enrichwith.
Developers can either put their enrichwith templates in their packages or are welcome to contribute their template to enrichwith, particularly if that extends core R objects.
enrich.glm and enriched_glm, enrich.family, enrich.link-glm, enrich.betareg
Generic method for extracting or computing auxiliary functions for objects
get_auxiliary_functions(object, ...)get_auxiliary_functions(object, ...)
object |
the object to be enriched or the enriched object |
... |
currently not used |
betreg/enriched_betareg
Function to compute/extract auxiliary functions from objects of
class betreg/enriched_betareg
## S3 method for class 'betareg' get_auxiliary_functions(object, ...)## S3 method for class 'betareg' get_auxiliary_functions(object, ...)
object |
an object of class |
... |
currently not used |
See enrich.betareg for details.
glm/enriched_glm
Function to compute/extract auxiliary functions from objects of
class glm/enriched_glm
## S3 method for class 'glm' get_auxiliary_functions(object, ...)## S3 method for class 'glm' get_auxiliary_functions(object, ...)
object |
an object of class |
... |
currently not used |
See enrich.glm for details.
lm/enriched_lm
Function to compute/extract auxiliary functions from objects of
class lm/enriched_lm
## S3 method for class 'lm' get_auxiliary_functions(object, ...)## S3 method for class 'lm' get_auxiliary_functions(object, ...)
object |
an object of class |
... |
currently not used |
See enrich.lm for details.
Generic method for extracting or computing a function that returns the bias for the parameters in modelling objects
get_bias_function(object, ...)get_bias_function(object, ...)
object |
the object to be enriched or the enriched object |
... |
currently not used |
betareg/enriched_betareg
Function to compute/extract a function that returns the first term
in the expansion of the bias of the MLE for the parameters of an
object of class betareg/enriched_betareg
## S3 method for class 'betareg' get_bias_function(object, ...)## S3 method for class 'betareg' get_bias_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted function has arguments
the regression coefficients at which the first-order bias is evacuated. If missing then the maximum likelihood estimates are used
glm/enriched_glm
Function to compute/extract a function that returns the first term
in the expansion of the bias of the MLE for the parameters of an
object of class glm/enriched_glm
## S3 method for class 'glm' get_bias_function(object, ...)## S3 method for class 'glm' get_bias_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted function has arguments
the regression coefficients at which the first-order bias is evacuated. If missing then the maximum likelihood estimates are used
the dispersion parameter at which the first-order bias is evaluated. If missing then the maximum likelihood estimate is used
lm/enriched_lm
Function to compute/extract a function that returns the first term
in the expansion of the bias of the MLE for the parameters of an
object of class lm/enriched_lm
## S3 method for class 'lm' get_bias_function(object, ...)## S3 method for class 'lm' get_bias_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted function has arguments
the regression coefficients at which the first-order bias is evacuated. If missing then the maximum likelihood estimates are used
the dispersion parameter at which the first-order bias is evaluated. If missing then the maximum likelihood estimate is used
Generic method for extracting or computing a dmodel function for modelling objects
get_dmodel_function(object, ...)get_dmodel_function(object, ...)
object |
the object to be enriched or the enriched object |
... |
currently not used |
dmodel functionFunction to compute/extract a dmodel function
## S3 method for class 'glm' get_dmodel_function(object, ...)## S3 method for class 'glm' get_dmodel_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted function has arguments
a data frame with observations at which to compute
densities. If missing then densities are computed at the model
frame extracted from the object (see glm)
the regression coefficients at which the densities are computed. If missing then the maximum likelihood estimates are used
the dispersion parameter at which the densities function is computed. If missing then the maximum likelihood estimate is used
logical; if TRUE, the logarithm of the density is
returned
Generic method for getting available options for the enrichment of objects
get_enrichment_options(object, option, all_options)get_enrichment_options(object, option, all_options)
object |
the object to be enriched |
option |
a character vector listing the options for enriching the object |
all_options |
if |
A check is being made whether the requested option is available. No check is being made on whether the functions that produce the components exist.
if all_options = TRUE then an object of class
enrichment_options is returned, otherwise if
option is specified the output is a character vector
with the names of the functions that compute the enrichment
components
enrich.glm, enrich.family, enrich.link-glm, enrich.betareg
Available options for the enrichment objects of class betareg
## S3 method for class 'betareg' get_enrichment_options(object, option, all_options = missing(option))## S3 method for class 'betareg' get_enrichment_options(object, option, all_options = missing(option))
object |
the object to be enriched |
option |
a character vector listing the options for enriching the object |
all_options |
if |
A check is being made whether the requested option is available. No check is being made on whether the functions that produce the components exist.
an object of class enrichment_options
## Not run: get_enrichment_options.betareg(option = "all") get_enrichment_options.betareg(all_options = TRUE) ## End(Not run)## Not run: get_enrichment_options.betareg(option = "all") get_enrichment_options.betareg(all_options = TRUE) ## End(Not run)
Available options for the enrichment objects of class family
## S3 method for class 'family' get_enrichment_options(object, option, all_options = missing(option))## S3 method for class 'family' get_enrichment_options(object, option, all_options = missing(option))
object |
the object to be enriched |
option |
a character vector listing the options for enriching the object |
all_options |
if |
A check is being made whether the requested option is available. No check is being made on whether the functions that produce the components exist.
an object of class enrichment_options
## Not run: get_enrichment_options.family(option = "all") get_enrichment_options.family(all_options = TRUE) ## End(Not run)## Not run: get_enrichment_options.family(option = "all") get_enrichment_options.family(all_options = TRUE) ## End(Not run)
glm
Available options for the enrichment objects of class
glm
## S3 method for class 'glm' get_enrichment_options(object, option, all_options = missing(option))## S3 method for class 'glm' get_enrichment_options(object, option, all_options = missing(option))
object |
the object to be enriched |
option |
a character vector listing the options for enriching the object |
all_options |
if |
A check is being made whether the requested option is available. No check is being made on whether the functions that produce the components exist.
an object of class enrichment_options
## Not run: get_enrichment_options.glm(option = "all") get_enrichment_options.glm(all_options = TRUE) ## End(Not run)## Not run: get_enrichment_options.glm(option = "all") get_enrichment_options.glm(all_options = TRUE) ## End(Not run)
Available options for the enrichment objects of class link-glm
## S3 method for class ''link-glm'' get_enrichment_options(object, option, all_options = missing(option))## S3 method for class ''link-glm'' get_enrichment_options(object, option, all_options = missing(option))
object |
the object to be enriched |
option |
a character vector listing the options for enriching the object |
all_options |
if |
A check is being made whether the requested option is available. No check is being made on whether the functions that produce the components exist.
an object of class enrichment_options
## Not run: `get_enrichment_options.link-glm`(option = "all") `get_enrichment_options.link-glm`(all_options = TRUE) ## End(Not run)## Not run: `get_enrichment_options.link-glm`(option = "all") `get_enrichment_options.link-glm`(all_options = TRUE) ## End(Not run)
lm
Available options for the enrichment objects of class
lm
## S3 method for class 'lm' get_enrichment_options(object, option, all_options = missing(option))## S3 method for class 'lm' get_enrichment_options(object, option, all_options = missing(option))
object |
the object to be enriched |
option |
a character vector listing the options for enriching the object |
all_options |
if |
A check is being made whether the requested option is available. No check is being made on whether the functions that produce the components exist.
an object of class enrichment_options
## Not run: get_enrichment_options.lm(option = "all") get_enrichment_options.lm(all_options = TRUE) ## End(Not run)## Not run: get_enrichment_options.lm(option = "all") get_enrichment_options.lm(all_options = TRUE) ## End(Not run)
Generic method for extracting or computing a function that returns the information matrix for modelling objects
get_information_function(object, ...)get_information_function(object, ...)
object |
the object to be enriched or the enriched object |
... |
currently not used |
betareg/enriched_betareg
Function to compute/extract a function that returns the information
matrix for an object of class betareg/enriched_betareg
## S3 method for class 'betareg' get_information_function(object, ...)## S3 method for class 'betareg' get_information_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted function has arguments
the regression coefficients at which the information matrix is evaluated. If missing then the maximum likelihood estimates are used
should the function return th 'expected' or 'observed' information? Default is expected
Currently not used
If TRUE, then the Cholesky decomposition of the information matrix at the coefficients is returned
glm/enriched_glm
Function to compute/extract a function that returns the information
matrix for an object of class glm/enriched_glm
## S3 method for class 'glm' get_information_function(object, ...)## S3 method for class 'glm' get_information_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted function has arguments
the regression coefficients at which the information matrix is evaluated. If missing then the maximum likelihood estimates are used
the dispersion parameter at which the information matrix is evaluated. If missing then the maximum likelihood estimate is used
should the function return th 'expected' or 'observed' information? Default is expected
If TRUE, then the QR decomposition of
is returned, where
is a diagonal matrix with the working weights (object$weights) and
is the model matrix.
If TRUE, then the Cholesky decomposition of the information matrix at the coefficients is returned
lm/enriched_lm
Function to compute/extract a function that returns the information
matrix for an object of class lm/enriched_lm
## S3 method for class 'lm' get_information_function(object, ...)## S3 method for class 'lm' get_information_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted function has arguments
the regression coefficients at which the information matrix is evaluated. If missing then the maximum likelihood estimates are used
the dispersion parameter at which the information matrix is evaluated. If missing then the maximum likelihood estimate is used
should the function return th 'expected' or 'observed' information? Default is expected
If TRUE, then the QR decomposition of
is returned, where
is a diagonal matrix with the working weights (object$weights) and
is the model matrix.
If TRUE, then the Cholesky decomposition of the information matrix at the coefficients is returned
Generic method for extracting or computing a pmodel function for modelling objects
get_pmodel_function(object, ...)get_pmodel_function(object, ...)
object |
the object to be enriched or the enriched object |
... |
currently not used |
pmodel functionFunction to compute/extract a pmodel function
## S3 method for class 'glm' get_pmodel_function(object, ...)## S3 method for class 'glm' get_pmodel_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted function has arguments
a data frame with observations at which to compute the
distribution function. If missing then probabilities are computed
at the model frame extracted from the object (see
glm)
the regression coefficients at which the distribution function are computed. If missing then the maximum likelihood estimates are used
the dispersion parameter at which the distribution function is computed. If missing then the maximum likelihood estimate is used
logical; if TRUE, the logarithm of the
distribution function is returned
logical; if TRUE (default), probabilities
are P[X <= x] otherwise, P[X > x]
Generic method for extracting or computing a qmodel function for modelling objects
get_qmodel_function(object, ...)get_qmodel_function(object, ...)
object |
the object to be enriched or the enriched object |
... |
currently not used |
qmodel functionFunction to compute/extract a qmodel function
## S3 method for class 'glm' get_qmodel_function(object, ...)## S3 method for class 'glm' get_qmodel_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted function has arguments
a vector of probabilities with length(p) equal to
nrow(data) at which to evaluate quantiles
a data frame with observations at which to compute the
quantiles. If missing then quantiles are computed at the model
frame extracted from the object (see glm)
the regression coefficients at which the quantiles are computed. If missing then the maximum likelihood estimates are used
the dispersion parameter at which the quantiles are computed. If missing then the maximum likelihood estimate is used
logical; if TRUE, the logarithm of the
probabilities is used
logical; if TRUE (default), probabilities
are P[X <= x] otherwise, P[X > x]
Generic method for extracting or computing a function that returns the scores for modelling objects
get_score_function(object, ...)get_score_function(object, ...)
object |
the object to be enriched or the enriched object |
... |
currently not used |
betareg/enriched_betareg
Function to compute/extract a function that returns the scores
(derivatives of the log-likelihood) for an object of class
betareg/enriched_betareg
## S3 method for class 'betareg' get_score_function(object, ...)## S3 method for class 'betareg' get_score_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted function has arguments
the regression coefficients at which the scores are computed. If missing then the maximum likelihood estimates are used
glm/enriched_glm
Function to compute/extract a function that returns the scores
(derivatives of the log-likelihood) for an object of class
glm/enriched_glm
## S3 method for class 'glm' get_score_function(object, ...)## S3 method for class 'glm' get_score_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted function has arguments
the regression coefficients at which the scores are computed. If missing then the maximum likelihood estimates are used
the dispersion parameter at which the score function is evaluated. If missing then the maximum likelihood estimate is used
lm/enriched_lm
Function to compute/extract a function that returns the scores
(derivatives of the log-likelihood) for an object of class
lm/enriched_lm
## S3 method for class 'lm' get_score_function(object, ...)## S3 method for class 'lm' get_score_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted function has arguments
the regression coefficients at which the scores are computed. If missing then the maximum likelihood estimates are used
the dispersion parameter at which the score function is evaluated. If missing then the maximum likelihood estimate is used
Generic method for extracting or computing a simulate function for modelling objects
get_simulate_function(object, ...)get_simulate_function(object, ...)
object |
the object to be enriched or the enriched object |
... |
currently not used |
betareg/enriched_betareg
Function to compute/extract a simulate function for response
vectors from an object of class betareg/enriched_betareg
## S3 method for class 'betareg' get_simulate_function(object, ...)## S3 method for class 'betareg' get_simulate_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted simulate function has arguments
the regression coefficients at which the response vectors are simulated. If missing then the maximum likelihood estimates are used
number of response vectors to simulate. Defaults to 1
an object specifying if and how the random number
generator should be initialized ('seeded'). It can be either
NULL or an integer that will be used in a call to
set.seed before simulating the response vectors. If set,
the value is saved as the seed attribute of the returned
value. The default, NULL will not change the random
generator state, and return .Random.seed as the seed
attribute, see Value
glm/enriched_glm
Function to compute/extract a simulate function for response
vectors from an object of class glm/enriched_glm
## S3 method for class 'glm' get_simulate_function(object, ...)## S3 method for class 'glm' get_simulate_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted simulate function has arguments
the regression coefficients at which the response vectors are simulated. If missing then the maximum likelihood estimates are used
the dispersion parameter at which the response vectors are simulated. If missing then the maximum likelihood estimate is used
number of response vectors to simulate. Defaults to 1
an object specifying if and how the random number
generator should be initialized ('seeded'). It can be either
NULL or an integer that will be used in a call to
set.seed before simulating the response vectors. If set,
the value is saved as the seed attribute of the returned
value. The default, NULL will not change the random
generator state, and return .Random.seed as the seed
attribute, see Value
lm/enriched_lm
Function to compute/extract a simulate function for response
vectors from an object of class lm/enriched_lm
## S3 method for class 'lm' get_simulate_function(object, ...)## S3 method for class 'lm' get_simulate_function(object, ...)
object |
an object of class |
... |
currently not used |
The computed/extracted simulate function has arguments
the regression coefficients at which the response vectors are simulated. If missing then the maximum likelihood estimates are used
the dispersion parameter at which the response vectors are simulated. If missing then the maximum likelihood estimate is used
number of response vectors to simulate. Defaults to 1
an object specifying if and how the random number
generator should be initialized ('seeded'). It can be either
NULL or an integer that will be used in a call to
set.seed before simulating the response vectors. If set,
the value is saved as the seed attribute of the returned
value. The default, NULL will not change the random
generator state, and return .Random.seed as the seed
attribute, see Value