pmml.multinom: Generate PMML for a multinomial (multinom) object

Description

Generate the Predictive Model Markup Language (PMML) representation of a nnet multinomial object. In particular, this function gives the user the ability to save a multinomial regression object as a PMML XML document.

Usage

## S3 method for class 'multinom':
pmml(model, model.name="multinom_Model", app.name="Rattle/PMML",
     description="Multinomial Logistic Model", copyright = NULL, transforms = NULL, ...)

Arguments

model

the multinom object contained in an object of class nnet, as that contained in the object returned by the function multinom.

model.name

a name to give to the model in the PMML.

app.name

the name of the application that generated the PMML.

description

a descriptive text for the header of the PMML.

the copyright notice for the model.

transforms

transforms.

...

further arguments passed to or from other methods.

Value

An object of class XMLNode as that defined by the XML package. This represents the top level, or root node, of the XML document and is of type PMML. It can be written to file with saveXML.

Details

The Predictive Model Markup Language is an XML based language which provides a way for applications to define statistical and data mining models and to share models between PMML compliant applications. More information about PMML and the Data Mining Group can be found at http://www.dmg.org.

Use of PMML and pmml.multinom requires the XML package. Be aware that XML is a very verbose data format.

This function is used to export the structure of the multinomial logistic regression model to any PMML consuming applications, such as the Zementis ADAPA and UPPI scoring engines which allow for predictive models built in R to be deployed and executed on site, in the cloud (Amazon, IBM, and FICO), in-database (IBM Netezza, Pivotal, Sybase IQ, Teradata and Teradata Aster) or Hadoop (Datameer and Hive).

References

Venables, W.N. and Ripley, B.D. (2002) /emph{Modern Applied Statistics with S}. Fourth edition. Springer.

PMML home page: http://www.dmg.org

A. Guazzelli, W. Lin, T. Jena (2012), /emph{PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics}. CreativeSpace (Second Edition) - Available on Amazon.com - http://www.amazon.com/dp/1470003244.