apd_pca: Fit a 'apd_pca'

In tidymodels/applicable: A Compilation of Applicability Domain Methods

Fit a apd_pca

Description

apd_pca() fits a model.

Usage

apd_pca(x, ...)

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

## S3 method for class 'data.frame'
apd_pca(x, threshold = 0.95, ...)

## S3 method for class 'matrix'
apd_pca(x, threshold = 0.95, ...)

## S3 method for class 'formula'
apd_pca(formula, data, threshold = 0.95, ...)

## S3 method for class 'recipe'
apd_pca(x, data, threshold = 0.95, ...)

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.
threshold	A number indicating the percentage of variance desired from the principal components. It must be a number greater than 0 and less or equal than 1.
formula	A formula specifying the predictor terms on the right-hand side. No outcome should be specified.
data	When a recipe or formula is used, data is specified as: A data frame containing the predictors.

Details

The function computes the principal components that account for up to either 95% or the provided threshold of variability. It also computes the percentiles of the absolute value of the principal components. Additionally, it calculates the mean of each principal component.

Value

A apd_pca object.

Examples

predictors <- mtcars[, -1]

# Data frame interface
mod <- apd_pca(predictors)

# Formula interface
mod2 <- apd_pca(mpg ~ ., mtcars)

# Recipes interface
library(recipes)
rec <- recipe(mpg ~ ., mtcars)
rec <- step_log(rec, disp)
mod3 <- apd_pca(rec, mtcars)

tidymodels/applicable documentation built on March 18, 2023, 4:08 p.m.