GLMModel {MachineShop} | R Documentation |
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 = NULL, k = 2, trace = FALSE, steps = 1000 )
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 |
direction |
mode of stepwise search, can be one of |
scope |
defines the range of models examined in the stepwise search.
This should be a list containing components |
k |
multiple of the number of degrees of freedom used for the penalty.
Only |
trace |
if positive, information is printed during the running of
|
steps |
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 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
.
MLModel
class object.
glm
, glm.control
,
stepAIC
, fit
, resample
fit(sale_amount ~ ., data = ICHomes, model = GLMModel)