GLMModel: Generalized Linear Model

In MachineShop: Machine Learning Models and Tools

Generalized Linear Model

Description

Fits generalized linear models, specified by giving a symbolic description of the linear predictor and a description of the error distribution.

Usage

GLMModel(family = NULL, quasi = FALSE, ...)

GLMStepAICModel(
  family = NULL,
  quasi = FALSE,
  ...,
  direction = c("both", "backward", "forward"),
  scope = list(),
  k = 2,
  trace = FALSE,
  steps = 1000
)

Arguments

family	optional error distribution and link function to be used in the model. Set automatically according to the class type of the response variable.
quasi	logical indicator for over-dispersion of binomial and Poisson families; i.e., dispersion parameters not fixed at one.
...	arguments passed to glm.control.
direction	mode of stepwise search, can be one of "both" (default), "backward", or "forward".
scope	defines the range of models examined in the stepwise search. This should be a list containing components upper and lower, both formulae.
k	multiple of the number of degrees of freedom used for the penalty. Only k = 2 gives the genuine AIC; k = .(log(nobs)) is sometimes referred to as BIC or SBC.
trace	if positive, information is printed during the running of stepAIC. Larger values may give more information on the fitting process.
steps	maximum number of steps to be considered.

Details

GLMModel Response types:

BinomialVariate, factor, matrix, NegBinomialVariate, numeric, PoissonVariate

GLMStepAICModel Response types:

binary factor, BinomialVariate, NegBinomialVariate, numeric, PoissonVariate

Default argument values and further model details can be found in the source See Also links below.

In calls to varimp for GLMModel and GLMStepAICModel, numeric argument base may be specified for the (negative) logarithmic transformation of p-values [defaul: exp(1)]. Transformed p-values are automatically scaled in the calculation of variable importance to range from 0 to 100. To obtain unscaled importance values, set scale = FALSE.

Value

MLModel class object.

See Also

glm, glm.control, stepAIC, fit, resample

Examples

fit(sale_amount ~ ., data = ICHomes, model = GLMModel)

MachineShop documentation built on June 10, 2025, 1:08 a.m.