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(), ...)

## 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` . |

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`