| pmml.lm {pmml} | R Documentation |
Generate the PMML (Predictive Model Markup Language) representation of an lm object. The PMML can then be imported into other systems that accept PMML.
## S3 method for class 'lm'
pmml(model, model.name="Linear_Regression_Model", app.name="Rattle/PMML",
description="Linear Regression Model", copyright=NULL,
transforms=NULL, dataset=NULL, weights=NULL, ...)
model |
an lm object. |
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. |
copyright |
the copyright notice for the model. |
transforms |
a coded list of transforms performed. |
dataset |
the orginal training dataset, if available. |
weights |
the weights used for building the model. |
... |
further arguments passed to or from other methods. |
The generated PMML can be imported into any PMML consuming application, such as Zementis' ADAPA.
Currently, the resultant PMML document will not encode interaction terms.
Only numeric regression is supported currently. Generalised linear models (logistic regression) are not yet supported.
Package home page: http://rattle.togaware.com
PMML home page: http://www.dmg.org
Zementis' useful PMML convert: http://www.zementis.com/pmml_converters.htm
pmml.
# Build a simple lm model (iris.lm <- lm(Sepal.Length ~ ., data=iris)) # Convert to pmml pmml(iris.lm)