ZScoreXform {pmmlTransformations}

Performs a z-score normalization on continuous values in accordance to the PMML element: NormContinuous

Description

Performs a z-score normalization on data given in WrapData format

Usage

ZScoreXform(boxdata, xformInfo=NA, mapMissingTo=NA, ...)

Arguments

boxdata

wrapper object obtained by using the WrapData function on the raw data.

xformInfo

specification of details of the transformation.

mapMissingTo

value to be given to the transformed variable if the value of the input variable is missing.

...

further arguments passed to or from other methods.

Details

Given an input variable named InputVar, the name of the transformed variable OutputVar, and the desired value of the transformed variable if the input variable value is missing missingVal, the ZScoreXform command including all the optional parameters is:

xformInfo="InputVar -> OutputVar", mapMissingTo="missingVal"

There are two methods in which the variables can be referred to. The first method is to use its column number; given the data attribute of the boxData object, this would be the order at which the variable appears. This can be indicated in the format "column#". The second method is to refer to the variable by its name.
The name of the transformed variable is optional; if the name is not provided, the transformed variable is given the name: "derived_" + original_variable_name

missingValue, an optional parameter, is the value to be given to the output variable if the input variable value is missing. If no input variable names are provided, by default all numeric variables are transformed. Note that in this case a replacement value for missing input values cannot be specified.

Value

R object containing the raw data, the transformed data and data statistics.

Author(s)

Tridivesh Jena, Zementis, Inc.

See Also

WrapData.

Examples

# Load the standard iris dataset, already built into R
   data(iris)

# First wrap the data
   irisBox <- WrapData(iris)

# Perform a z-transform on all numeric variables of the loaded 
# iris dataset. These would be Sepal.Length, Sepal.Width, 
# Petal.Length, and Petal.Width. The 4 new derived variables 
# will be named derived_Sepal.Length, derived_Sepal.Width, 
# derived_Petal.Length, and derived_Petal.Width
   ZScoreXform(irisBox)

# Perform a z-transform on the 1st column of the dataset (Sepal.Length)
# and give the derived variable the name "dsl" 
   ZScoreXform(irisBox,xformInfo="column1 -> dsl")

# Repeat the above operation; adding the new transformed variable 
# to the irisBox object
   irisBox <- ZScoreXform(irisBox,xformInfo="column1 -> dsl")

# Transform Sepal.Width(the 2nd column)
# The new transformed variable will be given the default name 
# "derived_Sepal.Width"
   ZScoreXform(irisBox,xformInfo="column2")

# Repeat the same operation as above, this time using the variable 
# name
   ZScoreXform(irisBox,xformInfo="Sepal.Width")

# Repeat the same operation as above, assign the transformed variable 
# "derived_Sepal.Width". The value of 1.0 if the input value of the 
# "Sepal.Width" variable is missing. Add the new information to the 
# irisBox object. 
   irisBox <- ZScoreXform(irisBox,xformInfo="Sepal.Width",
                          "mapMissingTo=1.0")