Correlation Matrix as Plain Data Frames

A thin wrapper around smart_cormat() for pipeline work. It never prompts, prints nothing, and returns two plain data frames (the estimates and the method used for each pair) instead of an S3 object.

Usage

smart_cor_df(
  data,
  cols = NULL,
  types = NULL,
  assume_latent_normal = "auto",
  ordinal_threshold = 10,
  ignore_na = TRUE,
  conf_level = 0.95,
  bootstrap = "auto",
  n_boot = 500,
  n_perm = 500,
  seed = NULL
)

Arguments

data

A data frame or tibble.

cols

Optional character vector of column names to include. Default: all columns.

types

Optional named list mapping column names to types ("continuous", "count", "binary", "ordinal", "categorical"). Columns not listed are auto-detected.

assume_latent_normal

"auto" (default) or logical, passed to smart_cormat(). NULL is not useful here because this wrapper never prompts.

ordinal_threshold

Integer passed to detect_type(). Default: 10.

ignore_na

Logical. If TRUE (default), missing values are handled pairwise. If FALSE, pairs with missing values come back NA.

conf_level

Numeric between 0 and 1. Confidence level passed through to each smart_cor() call. Default: 0.95.

bootstrap

Character or logical, passed through: "auto" (default), TRUE, or FALSE.

n_boot

Integer. Bootstrap replications per pair. Default: 500.

n_perm

Integer. Permutation shuffles for Theil's U p-values. Default: 500.

seed

Optional integer seed for the bootstrap and permutation draws; the global RNG state is saved and restored.

Value

A named list with two elements:

correlations

A data frame of correlation estimates (numeric).

methods

A data frame of method codes (character) used for each pair.

Details

By default it uses assume_latent_normal = "auto", so each pair is screened with test_bivariate_normality() and the method follows the test (a saturated 2 x 2 pair gets phi).

The two returned data frames share identical row and column names (the variable names from data). Diagonal entries in correlations are 1; diagonal entries in methods name the method a self-pair of that type would get, matching smart_cormat().

Examples

csv = system.file("extdata", "gss_2024_casestudy.csv", package = "smartcor")
gss = read.csv(csv)

# Correlations and methods for four columns in the frozen GSS extract
result = smart_cor_df(
  gss[, c("age", "coninc", "degree", "sex")],
  assume_latent_normal = FALSE
)
result$correlations
#>                 age      coninc     degree          sex
#> age     1.000000000  0.01924127 0.02762683 -0.008699344
#> coninc  0.019241267  1.00000000 0.45098152 -0.100768838
#> degree  0.027626830  0.45098152 1.00000000  0.015352069
#> sex    -0.008699344 -0.10076884 0.01535207  1.000000000
result$methods
#>                   age         coninc        degree            sex
#> age           pearson        pearson      spearman point_biserial
#> coninc        pearson        pearson      spearman point_biserial
#> degree       spearman       spearman       kendall  rank_biserial
#> sex    point_biserial point_biserial rank_biserial            phi

# Missing values are handled pairwise by default
example = gss[, c("age", "coninc", "sex")]
example$coninc[1:3] = NA
smart_cor_df(example, assume_latent_normal = FALSE)
#> $correlations
#>                 age      coninc          sex
#> age     1.000000000  0.01911907 -0.008699344
#> coninc  0.019119070  1.00000000 -0.100493238
#> sex    -0.008699344 -0.10049324  1.000000000
#> 
#> $methods
#>                   age         coninc            sex
#> age           pearson        pearson point_biserial
#> coninc        pearson        pearson point_biserial
#> sex    point_biserial point_biserial            phi
#>