apd_pca() fits a model.

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, ...)



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.


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.


A formula specifying the predictor terms on the right-hand side. No outcome should be specified.


When a recipe or formula is used, data is specified as:

  • A data frame containing the predictors.


A apd_pca object.


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.


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)