Fits generalized linear models, specified by giving a symbolic description of the linear predictor and a description of the error distribution.
GLMModel(family = NULL, quasi = FALSE, ...)GLMStepAICModel(
family = NULL,
quasi = FALSE,
...,
direction = c("both", "backward", "forward"),
scope = list(),
k = 2,
trace = FALSE,
steps = 1000
)
MLModel
class object.
optional error distribution and link function to be used in the model. Set automatically according to the class type of the response variable.
logical indicator for over-dispersion of binomial and Poisson families; i.e., dispersion parameters not fixed at one.
arguments passed to glm.control
.
mode of stepwise search, can be one of "both"
(default), "backward"
, or "forward"
.
defines the range of models examined in the stepwise search.
This should be a list containing components upper
and lower
,
both formulae.
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.
if positive, information is printed during the running of
stepAIC
. Larger values may give more information on the fitting
process.
maximum number of steps to be considered.
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
.
glm
, glm.control
,
stepAIC
, fit
, resample
fit(sale_amount ~ ., data = ICHomes, model = GLMModel)
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