R: Make Predictions from a "Glm" Object

predict.Glm {hana.ml.r}

R Documentation

Make Predictions from a "Glm" Object

Description

Similar to other predict methods, this function predicts fitted values from a fitted "Glm" object.

Usage

## S3 method for class 'Glm'
predict(model, data, key, features = NULL,
  prediction.type = NULL, significance.level = NULL,
  handle.missing = NULL)

Arguments

`model`	`R6Class object` Glm object for prediction.
`data`	`DataFrame` data for prediction.
`key`	`character` Name of the ID column.
`features`	`list of character, optional` Names of the feature columns. If features is not provided, it defaults to all non-ID columns.
`prediction.type`	`character, optional` Specifies whether to output predicted values of the response or the link function. Defaults to 'response'.
`significance.level`	`double, optional` Significance level for confidence intervals and prediction intervals. If specified, overrides the value passed to the GLM constructor.
`handle.missing`	`("remove", "fill_zero"), optional` How to handle data rows with missing independent variable values. "remove" Remove missing rows. "fill_zero" Replace missing values with 0. Defaults to "remove".

Value

Dataframe Predicted values, structured as follows. The following two columns are always populated:

ID column, with same name and type as ‘data'’s ID column.
PREDICTION, type NVARCHAR(100), representing predicted values.

The following five columns are only populated for IRLS:

SE, type DOUBLE. Standard error, or for ordinal regression, the probability that the data point belongs to the predicted category.
CI_LOWER, type DOUBLE. Lower bound of the confidence interval.
CI_UPPER, type DOUBLE. Upper bound of the confidence interval.
PI_LOWER, type DOUBLE. Lower bound of the prediction interval.
PI_UPPER, type DOUBLE. Upper bound of the prediction interval.

Examples

## Not run: 
#'DataFrame df2 for prediction:
> df2.collect()
   ID  X
0   1 -1
1   2  0
2   3  1
3   4  2

Output:
>predict(glm, df2, key="ID")$collect()
   ID          PREDICTION
0   1  0.25543735346197155
1   2    0.744562646538029
2   3   2.1702915689746476
3   4     6.32608352871737

## End(Not run)