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Normalize a numeric vector using z-score standardization

Source: R/trend_analysis.R
normalize_zscore.Rd

standardizes a numeric vector by subtracting the mean and dividing by the standard deviation. this is intended for preparing time series prior to trend analysis such as mann-kendall or sen's slope, especially when results need to be comparable across indicators.

Usage

normalize_zscore(vec, na_on_fail = TRUE)

Arguments

vec

numeric vector to be normalized.

na_on_fail

logical. if TRUE, returns a vector of NA_real_ when normalization is undefined. if FALSE, throws an error.

Value

a numeric vector of the same length as vec, containing z-scores or NA_real_ values if normalization is not possible.

Details

the function fails fast on non-numeric input. for undefined normalization cases (all missing values or zero variance), behavior is controlled via na_on_fail.

Examples

normalize_zscore(c(1, 2, 3, 4))
#> [1] -1.1618950 -0.3872983  0.3872983  1.1618950

normalize_zscore(c(5, 5, 5), na_on_fail = TRUE)
#> [1] NA NA NA

try(
  normalize_zscore(c(5, 5, 5), na_on_fail = FALSE)
)
#> Error in normalize_zscore(c(5, 5, 5), na_on_fail = FALSE) : 
#>   standard deviation is zero or NA. cannot normalize.

normalize_zscore(c(NA_real_, NA_real_))
#> [1] NA NA