# ml_generalized_linear_regression

##### 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"), weights.column = NULL,
iter.max = 100L, ml.options = ml_options(), ...)
```

##### 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`

.- weights.column
The name of the column to use as weights for the model fit.

- iter.max
The maximum number of iterations to use.

- ml.options
Optional arguments, used to affect the model generated. See

`ml_options`

for more details.- ...
Optional arguments. The

`data`

argument can be used to specify the data to be used when`x`

is a formula; this allows calls of the form`ml_linear_regression(y ~ x, data = tbl)`

, and is especially useful in conjunction with`do`

.

##### 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 0.6.1, License: Apache License 2.0 | file LICENSE*