pmml (version 2.1.0)

xform_function: Add a function transformation to a xform_wrap object.

Description

Add a function transformation to a xform_wrap object.

Usage

xform_function(wrap_object, orig_field_name, new_field_name = "newField",
  new_field_data_type = "numeric", expression, map_missing_to = NA)

Arguments

wrap_object

Output of xform_wrap or another transformation function

orig_field_name

String specifying name(s) of the original data field(s) being used in the transformation

new_field_name

Name of the new field created by the transformation

new_field_data_type

Data type of the new field created by the transformation

expression

String expression specifying the transformation

map_missing_to

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

Value

R object containing the raw data, the transformed data and data statistics. The data data frame will contain a new new_field_name column, and field_data will contain a new new_field_name row.

Details

Calculate the expression provided in expression for every row in the wrap_object$data data frame. The expression argument must represent a valid R expression, and any functions used in expression must be defined in the current environment.

The name of the new field is optional (a default name is provided), but an error will be thrown if attempting to create a field with a name that already exists in the xform_wrap object.

See Also

xform_wrap

Examples

# NOT RUN {
# Load the standard iris dataset:
data(iris)

# Wrap the data:
iris_box <- xform_wrap(iris)

# Perform a transform on the Sepal.Length field:
# the value is squared and then divided by 100
iris_box <- xform_function(iris_box,
  orig_field_name = "Sepal.Length",
  new_field_name = "Sepal.Length.Transformed",
  expression = "(Sepal.Length^2)/100"
)

# Combine two fields to create another new feature:
iris_box <- xform_function(iris_box,
  orig_field_name = "Sepal.Width, Petal.Width",
  new_field_name = "Width.Sum",
  expression = "Sepal.Width + Sepal.Length"
)

# Create linear model using the derived features:
fit <- lm(Petal.Length ~
Sepal.Length.Transformed + Width.Sum, data = iris_box$data)

# Create pmml from the fit:
fit_pmml <- pmml(fit, transform = iris_box)
# }