glm {SparkR}R Documentation

Generalized Linear Models (R-compliant)

Description

Fits a generalized linear model, similarly to R's glm().

Usage

glm(formula, family = gaussian, data, weights, subset, na.action,
  start = NULL, etastart, mustart, offset, control = list(...),
  model = TRUE, method = "glm.fit", x = FALSE, y = TRUE,
  contrasts = NULL, ...)

## S4 method for signature 'formula,ANY,SparkDataFrame'
glm(formula, family = gaussian, data,
  epsilon = 1e-06, maxit = 25, weightCol = NULL)

Arguments

formula

a symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', '.', ':', '+', and '-'.

family

a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. Refer R family at https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html. Currently these families are supported: binomial, gaussian, Gamma, and poisson.

data

a SparkDataFrame or R's glm data for training.

weights

an optional vector of ‘prior weights’ to be used in the fitting process. Should be NULL or a numeric vector.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

na.action

a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The ‘factory-fresh’ default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.

start

starting values for the parameters in the linear predictor.

etastart

starting values for the linear predictor.

mustart

starting values for the vector of means.

offset

this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases. One or more offset terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See model.offset.

control

a list of parameters for controlling the fitting process. For glm.fit this is passed to glm.control.

model

a logical value indicating whether model frame should be included as a component of the returned value.

method

the method to be used in fitting the model. The default method "glm.fit" uses iteratively reweighted least squares (IWLS): the alternative "model.frame" returns the model frame and does no fitting.

User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments as glm.fit. If specified as a character string it is looked up from within the stats namespace.

x, y

For glm: logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value.

contrasts

an optional list. See the contrasts.arg of model.matrix.default.

...

For glm: arguments to be used to form the default control argument if it is not supplied directly.

For weights: further arguments passed to or from other methods.

epsilon

positive convergence tolerance of iterations.

maxit

integer giving the maximal number of IRLS iterations.

weightCol

the weight column name. If this is not set or NULL, we treat all instance weights as 1.0.

Value

glm returns a fitted generalized linear model.

Note

glm since 1.5.0

See Also

spark.glm

Examples

## Not run: 
##D sparkR.session()
##D data(iris)
##D df <- createDataFrame(iris)
##D model <- glm(Sepal_Length ~ Sepal_Width, df, family = "gaussian")
##D summary(model)
## End(Not run)
[Package SparkR version 2.1.2 Index]