Perform generalized linear regression on a Spark DataFrame.

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

- 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`

. - 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`

.

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.

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`