# ml_generalized_linear_regression

From sparklyr v0.3.4
by Javier Luraschi

##### Spark ML -- Generalized Linear Regression

Perform generalized linear regression on a Spark DataFrame.

##### Usage

`ml_generalized_linear_regression(x, response, features, intercept = TRUE, family = gaussian(link = "identity"), max.iter = 100L, ...)`

##### Arguments

- x
- An object coercable to a Spark DataFrame (typically, a
`tbl_spark`

). - response
- The name of the response vector (as a length-one character
vector), or a formula, giving a symbolic description of the model to be
fitted. When
`response`

is a formula, it is used in preference to other parameters to set the`response`

,`features`

, and`intercept`

parameters (if available). Currently, only simple linear combinations of existing parameters is supposed; e.g.`response ~ feature1 + feature2 + ...`

. The intercept term can be omitted by using`- 1`

in the model fit. - features
- The name of features (terms) to use for the model fit.
- intercept
- Boolean; should the model be fit with an intercept term?
- family
- The family / link function to use; analogous to those normally
passed in to calls to R's own
`glm`

. - max.iter
- The maximum number of iterations to use.
- ...
- Optional arguments; currently unused.

##### Details

In contrast to `ml_linear_regression()`

and
`ml_logistic_regression()`

, these routines do not allow you to
tweak the loss function (e.g. for elastic net regression); however, the model
fits returned by this routine are generally richer in regards to information
provided for assessing the quality of fit.

##### See Also

Other Spark ML routines: `ml_als_factorization`

,
`ml_decision_tree`

,
`ml_gradient_boosted_trees`

,
`ml_kmeans`

, `ml_lda`

,
`ml_linear_regression`

,
`ml_logistic_regression`

,
`ml_multilayer_perceptron`

,
`ml_naive_bayes`

,
`ml_one_vs_rest`

, `ml_pca`

,
`ml_random_forest`

,
`ml_survival_regression`

*Documentation reproduced from package sparklyr, version 0.3.4, License: file LICENSE*

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