applicable (version 0.0.1.1)

apd_pca: Fit a apd_pca

Description

apd_pca() fits a model.

Usage

apd_pca(x, ...)
# S3 method for default
apd_pca(x, ...)
# S3 method for data.frame
apd_pca(x, threshold = 0.95, ...)
# S3 method for matrix
apd_pca(x, threshold = 0.95, ...)
# S3 method for formula
apd_pca(formula, data, threshold = 0.95, ...)
# S3 method for 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.

Value

A apd_pca object.

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.

Examples

# NOT RUN {
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)

# }