logistic_regression: Fit a 'logistic_regression'

In jlaria/glasp: Group Linear Algorithm with Sparse Principal decomposition

Fit a logistic_regression

Description

logistic_regression() fits a model.

Usage

logistic_regression(x, ...)

## Default S3 method:
logistic_regression(x, ...)

## S3 method for class 'data.frame'
logistic_regression(x, y, ...)

## S3 method for class 'matrix'
logistic_regression(x, y, ...)

## S3 method for class 'formula'
logistic_regression(formula, data, ...)

## S3 method for class 'recipe'
logistic_regression(x, data, ...)

Arguments

x	Depending on the context: A data frame of predictors. A matrix of predictors. A recipe specifying a set of preprocessing steps created from recipes::recipe().
...	Not currently used, but required for extensibility.
y	When x is a data frame or matrix, y is the outcome specified as: A data frame with 1 numeric column. A matrix with 1 numeric column. A numeric vector.
formula	A formula specifying the outcome terms on the left-hand side, and the predictor terms on the right-hand side.
data	When a recipe or formula is used, data is specified as: A data frame containing both the predictors and the outcome.

Value

A logistic_regression object.

Examples

set.seed(0)
data <- simulate_dummy_logistic_data()

model <- logistic_regression(y~., data, l1=0.05, l2=0.01, frob=0.01, num_comp=3)
model

new_data <- simulate_dummy_logistic_data()

predict(model, new_data, type = "numeric")
predict(model, new_data, type = "prob")
predict(model, new_data, type = "class")

jlaria/glasp documentation built on Dec. 5, 2022, 6:42 a.m.