cor_matrix: Pairwise Correlation Matrix

In collinear: Automated Multicollinearity Management

Pairwise Correlation Matrix

Description

If argument 'df' results from cor_df(), transforms it to a correlation matrix. If argument 'df' is a dataframe with predictors, and the argument 'predictors' is provided then cor_df() is used to compute pairwise correlations, and the result is transformed to matrix.

Accepts a parallelization setup via future::plan() and a progress bar via progressr::handlers() (see examples).

Usage

cor_matrix(df = NULL, predictors = NULL)

Arguments

df	(required; data frame, tibble, or sf) A data frame with responses and predictors. Default: NULL.
predictors	(optional; character vector) Names of the predictors to select from df. If omitted, all numeric columns in df are used instead. If argument response is not provided, non-numeric variables are ignored. Default: NULL

Value

correlation matrix

Author(s)

Blas M. Benito, PhD

See Also

Other pairwise_correlation: cor_clusters(), cor_cramer_v(), cor_df(), cor_select()

Examples

data(
  vi,
  vi_predictors
)

#reduce size of vi to speed-up example execution
vi <- vi[1:1000, ]

#mixed predictors
vi_predictors <- vi_predictors[1:10]

#parallelization setup
future::plan(
  future::multisession,
  workers = 2 #set to parallelly::availableCores() - 1
)

#progress bar
# progressr::handlers(global = TRUE)

#correlation data frame
df <- cor_df(
  df = vi,
  predictors = vi_predictors
)

df

#correlation matrix
m <- cor_matrix(
  df = df
)

m

#generating it from the original data
m <- cor_matrix(
  df = vi,
  predictors = vi_predictors
)

m

#disable parallelization
future::plan(future::sequential)

collinear documentation built on April 12, 2025, 1:36 a.m.