zscore: Z-score normalization

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Z-score normalization

Description

Standardize numeric columns to zero mean and unit variance, optionally rescaled to a target mean (nmean) and sd (nsd).

Usage

zscore(nmean = 0, nsd = 1)

Arguments

nmean	new mean for normalized data
nsd	new standard deviation for normalized data

Details

For each numeric column j, computes ((x - mean_j)/sd_j) * nsd + nmean. Constant columns become nmean.

zscore = (x - mean(x))/sd(x)

Value

returns the z-score transformation object

References

Han, J., Kamber, M., Pei, J. (2011). Data Mining: Concepts and Techniques. (Standardization)

Examples

data(iris)
head(iris)

trans <- zscore()
trans <- fit(trans, iris)
tiris <- transform(trans, iris)
head(tiris)

itiris <- inverse_transform(trans, tiris)
head(itiris)

daltoolbox documentation built on Nov. 5, 2025, 7:09 p.m.