CRITIC Weight Method — critic_weight • mathmodels

Computes objective weights of indicators and scores of samples using the CRITIC method. The method considers both the contrast intensity (e.g., standard deviation or entropy) and conflict among indicators (based on correlation) to determine indicator importance. This version supports different methods for contrast intensity and correlation types.

Usage

critic_weight(
  X,
  index = NULL,
  method = "std",
  cor_method = "pearson",
  epsilon = 0.002
)

Arguments

X

A numeric data frame or matrix where rows represent samples (observations) and columns represent indicators (variables).

index

A character vector indicating the direction of each indicator: Use "+" for positive indicators (higher is better), "-" for negative indicators (lower is better), and NA for already normalized indicators (no rescaling will be applied).

         If `index = NULL` (default), all indicators are treated as `NA`, meaning no normalization is performed.

method

Character scalar; specifies the method used to compute contrast intensity. Options: "std" (standard deviation, default), or "entropy" (based on information redundancy).

cor_method

Character scalar; specifies the method for computing correlations. Options: "pearson" (default), "spearman", or "kendall".

epsilon

A small constant used to replace exact 0s and 1s in the data to prevent log(0) errors. Default is 0.002. Only used when method = "entropy".

Value

A list containing:

w: Numeric vector of weights for each indicator.
s: Numeric vector of scores for each sample (row), scaled by 100.

Examples

# Example: Using CRITIC method on a simple dataset
X = data.frame(
  x1 = c(3, 5, 2, 7),
  x2 = c(10, 20, 15, 25)
)
index = c("+", "-")
critic_weight(X, index)
#> $w
#>        x1        x2 
#> 0.5075187 0.4924813 
#> 
#> $s
#> [1] 59.39851 46.86716 32.83209 50.75187
#> 
critic_weight(X, index, method = "entropy")
#> $w
#>        x1        x2 
#> 0.5455552 0.4544448 
#> 
#> $s
#> [1] 56.26470 47.88147 30.40543 54.53730
#>