Transform {Transform}

Statistical Transformations for Normality

Description

Transform performs transformations for normality of a variable and provides graphical analysis.

Usage

Transform(data, method = "dl", lambda = seq(0,6,0.01), lambda2 = NULL, plot = TRUE, 
  alpha = 0.05, verbose = TRUE)

Arguments

data

a numeric vector of data values.

method

a character string. Different transformation methods can be used for the estimation of the optimal transformation parameter: Box-Cox ("bc"), Log-shift ("ls"), Bickel-Doksum ("bd"), Yeo-Johnson ("yj"), Square Root ("ss"), Manly ("mn"), Modulus ("md"), Dual ("dl"), Gpower ("gp"), Log ("lg"), Glog ("gl"), Neglog ("nl"), Reciprocal ("rp"). Default is set to method = "dl".

lambda

a vector which includes the sequence of candidate lambda values. Please see the corresponding method to learn the lambda range. Default is set to (0,6) with increment 0.01.

lambda2

a numeric for an additional shifting parameter. Please see the corresponding method to learn the lambda2. Default is set to lambda2 = NULL.

plot

a logical to plot histogram with its density line and qqplot of raw and transformed data. Defaults plot = TRUE.

alpha

the level of significance to check the normality after transformation. Default is set to alpha = 0.05.

verbose

a logical for printing output to R console.

Value

See the corresponding transformation method.

Author(s)

Muge Coskun Yildirim, Osman Dag

Examples

data <- cars$dist

library(Transform)
out <- Transform(data, method = "bc")
out$lambda.hat # the estimate of Box-Cox parameter based on Shapiro-Wilk test statistic 
out$p.value # p.value of Shapiro-Wilk test for transformed data 
out$tf.data # transformed data set