MachineShop (version 2.8.0)

GLMModel: 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 = NULL,
  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.

Value

MLModel class object.

Details

GLMModel Response Types:

BinomialVariate, factor, matrix, NegBinomialVariate, numeric, PoissonVariate

GLMStepAICModel Response Types:

binary factor, BinomialVariate, NegBinomialVariate, numeric, PoissonVariate

Default values for the NULL arguments and further model details can be found in the source link 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.

See Also

glm, glm.control, stepAIC, fit, resample

Examples

# NOT RUN {
fit(sale_amount ~ ., data = ICHomes, model = GLMModel)

# }