eglm: Efficient Fitting of Generalized Linear Models

In eflm: Efficient Fitting of Linear and Generalized Linear Models

Description

Efficient Generalized Linear Model ("eglm") is used to fit generalized linear models in an equivalent way to "glm" but in a reduced time depending on the design matrix and the family (or link).

Usage

eglm(
  formula,
  family = gaussian,
  data,
  weights,
  subset,
  na.action,
  start = NULL,
  etastart,
  mustart,
  offset,
  control = list(...),
  model = TRUE,
  method = "eglm.wfit",
  x = FALSE,
  y = TRUE,
  singular.ok = TRUE,
  contrasts = NULL,
  reduce = FALSE,
  ...
)

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. This can be a character string naming a family function, a family function or the result of a call to a family function. See family for details of family functions.
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.
weights	an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector. If non-NULL, weighted least squares is used with weights weights (that is, minimizing sum(w*e^2)); otherwise ordinary least squares is used.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
na.action	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The ‘factory-fresh’ default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.
start	starting values for the parameters in the linear predictor.
etastart	starting values for the linear predictor.
mustart	starting values for the vector of means.
offset	this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector or matrix of extents matching those of the response. One or more offset terms can be included in the formula instead or as well, and if more than one are specified their sum is used. See model.offset.
control	a list of parameters for controlling the fitting process. For eglm.wfit this is passed to glm.control.
model	a logical value indicating whether model frame should be included as a component of the returned value.
method	the method to be used in fitting the model. The default method "eglm.wfit" uses iteratively reweighted least squares (IWLS): the alternative "model.frame" returns the model frame and does no fitting. User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as glm.fit from the stats package. If specified as a character string it is looked up from within the eflm namespace.
x, y	logical values indicating whether the model matrix (x) and the response vector (y) used in the fitting process should be returned as components of the returned value.
singular.ok	logical; if FALSE a singular fit is an error.
contrasts	an optional list. See the contrasts.arg of model.matrix.default.
reduce	logical; if TRUE an alternate design matrix of p * p is used for the fitting instead of the traditional n * p design matrix.
...	For eglm: arguments to be used to form the default control argument if it is not supplied directly. For weights: further arguments passed to or from other methods.

Details

Models for eglm are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second, and exactly the same as "glm" from the stats package.

Value

An object of class "eglm" that behaves the same way as the "glm" class, see the function "glm". This output also includes the logical "reduce" and, depending on it, the reduced design matrix "xtx" when the reduce argument is set to TRUE.

Examples

eglm(mpg ~ wt, family = gaussian, data = mtcars)

eflm documentation built on June 1, 2021, 1:06 a.m.