glm,formula,ANY,SparkDataFrame-method {SparkR} | R Documentation |
Fits a generalized linear model, similarly to R's glm().
## S4 method for signature 'formula,ANY,SparkDataFrame' glm(formula, family = gaussian, data, epsilon = 1e-06, maxit = 25, weightCol = NULL, var.power = 0, link.power = 1 - var.power, stringIndexerOrderType = c("frequencyDesc", "frequencyAsc", "alphabetDesc", "alphabetAsc"), offsetCol = NULL)
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: |
data |
a SparkDataFrame or R's glm data for training. |
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 |
var.power |
the index of the power variance function in the Tweedie family. |
link.power |
the index of the power link function in the Tweedie family. |
stringIndexerOrderType |
how to order categories of a string feature column. This is used to decide the base level of a string feature as the last category after ordering is dropped when encoding strings. Supported options are "frequencyDesc", "frequencyAsc", "alphabetDesc", and "alphabetAsc". The default value is "frequencyDesc". When the ordering is set to "alphabetDesc", this drops the same category as R when encoding strings. |
offsetCol |
the offset column name. If this is not set or empty, we treat all instance offsets as 0.0. The feature specified as offset has a constant coefficient of 1.0. |
glm
returns a fitted generalized linear model.
glm since 1.5.0
## Not run:
##D sparkR.session()
##D t <- as.data.frame(Titanic)
##D df <- createDataFrame(t)
##D model <- glm(Freq ~ Sex + Age, df, family = "gaussian")
##D summary(model)
## End(Not run)