ppi_mean: Simulate and Summarize Projection Pursuit Index (PPI) Values

In spinebil: Investigating New Projection Pursuit Index Functions

Simulate and Summarize Projection Pursuit Index (PPI) Values

Description

Simulate and Summarize Projection Pursuit Index (PPI) Values

Usage

ppi_mean(data, index_fun, n_sim = 100, n_obs = 300)

Arguments

data	A data frame or matrix. Must have at least two columns.
index_fun	A function taking two numeric vectors (x, y) and returning a scalar index.
n_sim	Integer. Number of simulations. Default is 100.
n_obs	Integer. Number of observations to sample in each simulation. Default is 300.

Value

A tibble with:

• var_i, var_j: Names of variable pairs

• mean_index: Mean index value over simulations

Examples

data <- as.data.frame(data_gen(type = "polynomial", degree = 2))
ppi_mean(data, scag_index("stringy"), n_sim = 10)

spinebil documentation built on Nov. 5, 2025, 7:35 p.m.