MachineShop: GLMModel – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

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.

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.

Value

MLModel class object.

Examples

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

MachineShop

Machine Learning Models and Tools

v2.8.0

GPL-3

Authors

Brian J Smith [aut, cre]

Initial release

2021-04-16