check_correlations

generates a correlation matrix among all columns of a data.frame

Usage

check_correlations(dat, parallel = FALSE, n.cores = 4)

Arguments

dat

the data.frame containing the columns for which a correlation matrix is sought.

parallel

a logical indicating if calaculation of the correlation matrix should be done in parallel. Defaults to FALSE.

n.cores

a numeric value indicating the number of cores to utilise if parallel is TRUE.

Value

a correlation matrix

Details

The function uses cor to calculate the Pearson correlation coefficient among continuous variables, lm to approximate the correlation coefficient among a continuous variable and a factor variable through the call lm(continuous~factor), and nnet to approximate the correlation among factor variables using a multinomial model fit.

Missing values are handled pairwise: each pair of predictors is evaluated on the rows for which both are present. For a factor-factor pair this applies to the intercept-only model as well as the fitted one, so the two deviances the estimate is a ratio of are always computed on the same rows.

Examples

data(case_study1)
check_correlations(case_study1[, c("depth", "complexity", "ZONE")])
#>                 depth complexity       ZONE
#> depth      1.00000000 0.33364348 0.08234682
#> complexity 0.33364348 1.00000000 0.01410832
#> ZONE       0.08234682 0.01410832 1.00000000