ATA.Transform {ATAforecasting}  R Documentation 
Transformation Techniques for The ATAforecasting
Description
The function provides the applicability of different types of transformation techniques for the data to which the Ata method will be applied.
The ATA.Transform
function works with many different types of inputs.
Usage
ATA.Transform(
X,
tMethod = c("Box_Cox", "Sqrt", "Reciprocal", "Log", "NegLog", "Modulus",
"BickelDoksum", "Manly", "Dual", "YeoJohnson", "GPower", "GLog"),
tLambda,
tShift = 0,
bcMethod = c("loglik", "guerrero"),
bcLower = 0,
bcUpper = 5
)
Arguments
X 
a numeric vector or time series of class 
tMethod 
BoxCox power transformation family is consist of "Box_Cox", "Sqrt", "Reciprocal", "Log", "NegLog",
"Modulus", "BickelDoksum", "Manly", "Dual", "YeoJohnson", "GPower", "GLog" in ATAforecasting package. If the transformation process needs shift parameter,

tLambda 
BoxCox power transformation family parameter. Default is NULL. When lambda is set as NULL, required "lambda" parameter will be calculated automatically based on "bcMethod, bcLower, and bcUpper". 
tShift 
BoxCox power transformation family shifting parameter. Default is 0. When "transform.method" is selected, required shifting parameter will be calculated automatically according to dataset. 
bcMethod 
Choose method to be used in calculating lambda. "loglik" is default. Other method is "guerrero" (Guerrero, V.M. (1993)). 
bcLower 
Lower limit for possible lambda values. The lower value is limited by 5. Default value is 0. 
bcUpper 
Upper limit for possible lambda values. The upper value is limited by 5. Default value is 1. 
Value
A list object consists of transformation parameters and transformed data.
ATA.Transform
is a list containing at least the following elements:
trfmX : Transformed data
tLambda : BoxCox power transformation family parameter
tShift : BoxCox power transformation family shifting parameter
References
#'Tukey JW (1957). “On the Comparative Anatomy of Transformations.” The Annals of Mathematical Statistics, 28(3), 602–632.
#'Box GEP, Cox DR (1964). “An Analysis of Transformations.” Journal of the Royal Statistical Society. Series B (Methodological), 26(2), 211–252.
#'Manly BFJ (1976). “Exponential data transformations.” Journal of the Royal Statistical Society Series D, 25(1), 37–42.
#'John JA, Draper NR (1980). “An alternative family of transformations.” Journal of the Royal Statistical Society Series C, 29(2), 190–197.
#'Bickel PJ, Doksum KA (1982). “An analysis of transformations revisited.” Journal of the American Statistical Association, 76(374), 296–311.
#'Sakia RM (1992). “The BoxCox Transformation Technique: A Review.” Journal of the Royal Statistical Society Series D, 41(2), 169–178.
#'Guerrero VM (1993). “Timeseries analysis supported by power transformations.” Journal of Forecasting, 12(1), 37–48.
#'Yeo I, Johnson RA (2000). “A New Family of Power Transformations to Improve Normality or Symmetry.” Biometrika, 87(4), 954–959.
#'Durbin BP, Hardin JS, Hawkins DM, Rocke DM (2002). “A variancestabilizing transformation for geneexpression microarray data.” Bioinformatics, 18(1), 105–110.
#'Whittaker J, Whitehead C, Somers M (2005). “The neglog transformation and quantile regression for the analysis of a large credit scoring database.” Journal of the Royal Statistical Society Series C, 54(4), 863–878.
#'Yang Z (2005). “A modified family of power transformations.” Economics Letters, 92(1), 14–19.
#'Kelmansky DM, Martinez EJ, Leiva V (2013). “A new variance stabilizing transformation for gene expression data analysis.” Statistical Applications in Genetics and Molecular Biology, 12(6), 653–666.