Scale by Median

Transform selected columns in a data frame into z-scores centered around the median using median absolute deviation (MAD). This transformation is recommended when using range size and evolutionary distinctiveness for geographic and phylogenetic dimensions of rarity.

Usage

scale_by_median(data_frame, columns_chosen = NULL)

Arguments

data_frame

A data frame containing the columns to transform. Must not contain NA values.

columns_chosen

A character vector specifying the names of the columns to transform. If NULL (default), all columns except "species_name" are transformed.

Value

A data frame where the selected columns have been scaled to z-scores based on the median and MAD.

Details

This function calculates z-scores as: $$z = (x - \text{median}(x)) / \text{MAD}(x)$$ where MAD is computed as the median of absolute deviations from the median,without applying the standard consistency constant. If columns_chosen is NULL, all columns except "species_name" are scaled.

See also

prepare_gdrarity_axes() where it is applied internally.

Author

Alivia G Nytko, anytko@vols.utk.edu

Examples

# Create dataframe of pine species with range sizes and evolutionary distinctiveness
pine_names <- c("Pinus_banksiana", "Pinus_cembra", "Pinus_nigra", "Pinus_pinaster", 
                "Pinus_pinea", "Pinus_ponderosa", "Pinus_strobus", "Pinus_sylvestris", "Pinus_uncinata")
evol_dist_values <- runif(n = 9, min = -50, max = 50)
range_values <- runif(n = 9, min = -20000, max = 20000)
pine_data <- data.frame(species_name = pine_names, range_size = range_values, evol_dist = evol_dist_values)

# Apply median scaling to all columns except species_name
transform_all <- scale_by_median(data_frame = pine_data)
print(transform_all)
#>       species_name  range_size   evol_dist
#> 1  Pinus_banksiana  0.01035741  0.09593961
#> 2     Pinus_cembra -3.12878817  1.59802104
#> 3      Pinus_nigra -2.57155004  0.00000000
#> 4   Pinus_pinaster -1.00000000 -1.29524563
#> 5      Pinus_pinea  0.00000000 -1.23479241
#> 6  Pinus_ponderosa  1.90583567 -1.00000000
#> 7    Pinus_strobus  0.52071923  0.75267739
#> 8 Pinus_sylvestris -0.67693971 -2.63169666
#> 9   Pinus_uncinata  1.07995383  0.58976869