GLMModel  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 = list(), 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 overdispersion 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 and further model details can be found in the source links below.
In calls to varimp
for GLMModel
and
GLMStepAICModel
, numeric argument base
may be specified for the
(negative) logarithmic transformation of pvalues [defaul: exp(1)
].
Transformed pvalues 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.