get_corr_flags {COINr}

Find highly-correlated indicators within groups

Description

This returns a data frame of any highly correlated indicators within the same aggregation group. The level of the aggregation grouping can be controlled by the grouplev argument.

Usage

get_corr_flags(
  coin,
  dset,
  cor_thresh = 0.9,
  thresh_type = "high",
  cortype = "pearson",
  grouplev = NULL,
  roundto = 3,
  use_directions = FALSE
)

Arguments

coin

A coin class object

dset

The name of the data set to apply the function to, which should be accessible in .$Data.

cor_thresh

A threshold to flag high correlation. Default 0.9.

thresh_type

Either "high", which will only flag correlations above cor_thresh, or "low", which will only flag correlations below cor_thresh.

cortype

The type of correlation, either "pearson" (default), "spearman" or "kendall". See stats::cor.

grouplev

The level to group indicators in. E.g. if grouplev = 2 it will look for high correlations between indicators that belong to the same group in Level 2.

roundto

Number of decimal places to round correlations to. Default 3. Set NULL to disable rounding.

use_directions

Logical: if TRUE the extracted data is adjusted using directions found inside the coin (i.e. the "Direction" column input in iMeta. See comments on this argument in get_corr().

Details

This function is motivated by the idea that having very highly-correlated indicators within the same group may amount to double counting, or possibly redundancy in the framework.

This function replaces the now-defunct hicorrSP() from COINr < v1.0.

Value

A data frame with one entry for every indicator pair that is highly correlated within the same group, at the specified level. Pairs are only reported once, i.e. only uses the upper triangle of the correlation matrix.

Examples

# build example coin
coin <- build_example_coin(up_to = "Normalise", quietly = TRUE)

# get correlations between indicator over 0.75 within level 2 groups
get_corr_flags(coin, dset = "Normalised", cor_thresh = 0.75,
               thresh_type = "high", grouplev = 2)