std.train {dtComb}

Standardization according to the chosen method.

Description

The std.train Standardization (range, zScore etc.) can be estimated from the training data and applied to any dataset with the same variables.

Usage

std.train(data, standardize = NULL)

Arguments

data

a numeric data frame of biomarkers

standardize

a character string indicating the name of the standardization method. The default option is no standardization applied. Available options are:

• Z-score (zScore): This method scales the data to have a mean of 0 and a standard deviation of 1. It subtracts the mean and divides by the standard deviation for each feature. Mathematically,

  Z-score = \frac{x - (\overline x)}{sd(x)}

  where x is the value of a marker, \overline{x} is the mean of the marker and sd(x) is the standard deviation of the marker.

• T-score (tScore): T-score is commonly used in data analysis to transform raw scores into a standardized form. The standard formula for converting a raw score x into a T-score is:

  T-score = \Biggl(\frac{x - (\overline x)}{sd(x)}\times 10 \Biggl) +50

  where x is the value of a marker, \overline{x} is the mean of the marker and sd(x) is the standard deviation of the marker.

• Range (a.k.a. min-max scaling) (range): This method transforms data to a specific range, between 0 and 1. The formula for this method is:

  Range = \frac{x - min(x)}{max(x) - min(x)}

• Mean (mean): This method, which helps to understand the relative size of a single observation concerning the mean of dataset, calculates the ratio of each data point to the mean value of the dataset.

  Mean = \frac{x}{\overline{x}}

  where x is the value of a marker and \overline{x} is the mean of the marker.

• Deviance (deviance): This method, which allows for comparison of individual data points in relation to the overall spread of the data, calculates the ratio of each data point to the standard deviation of the dataset.

  Deviance = \frac{x}{sd(x)}

  where x is the value of a marker and sd(x) is the standard deviation of the marker.

Value

A numeric data.frame of standardized biomarkers

Author(s)

Serra Ilayda Yerlitas, Serra Bersan Gengec, Necla Kochan, Gozde Erturk Zararsiz, Selcuk Korkmaz, Gokmen Zararsiz

Examples

# call data
data(exampleData1)

# define the function parameters
markers <- exampleData1[, -1]
markers2 <- std.train(markers, "deviance")