AnomalyDetection (version 0.1.2)

mc_adjust: Multi-Collinearity Adjustment

Description

mc_adjust handles issues with multi-collinearity.

Usage

mc_adjust(data, min_var = 0.1, max_cor = 0.9, action = "exclude")

Arguments

data

named numeric data object (either data frame or matrix)

min_var

numeric value between 0-1 for the minimum acceptable variance (default = 0.1)

max_cor

numeric value between 0-1 for the maximum acceptable correlation (default = 0.9)

action

select action for handling columns causing multi-collinearity issues

1. exclude: exclude all columns causing multi-collinearity issues (default)

2. select: identify the columns causing multi-collinearity issues and allow the user to interactively select those columns to remove

Value

mc_adjust returns the numeric data object supplied minus variables violating the minimum acceptable variance (min_var) and the maximum acceptable correlation (max_cor) levels.

Details

mc_adjust handles issues with multi-collinearity by first removing any columns whose variance is close to or less than min_var. Then, it removes linearly dependent columns. Finally, it removes any columns that have a high absolute correlation value equal to or greater than max_cor.

Examples

# NOT RUN {
x <- matrix(runif(100), ncol = 10)
x %>%
  mc_adjust()

x %>%
  mc_adjust(min_var = .15, max_cor = .75, action = "select")
# }
# NOT RUN {
# }