What types of Neural Network models built with R nnet can I export to PMML?

You can basically export most of the Neural Network models you build using the R nnet package into PMML 3.2 by using the PMML package available from Togaware. See link below:

http://rattle.togaware.com/

The PMML package is also available through CRAN.

The function to be used is pmml.nnet. With this function, a PMML representation can be obtained for Neural Networks implementing:

• multi-class classification
• binary classifcation
• regression

Details you should know:

1. Scaling of input variables: Since nnet does not automatically implement scaling of numerical inputs, you will need to add scaling to the generated PMML file by hand if you are planning to use the model to compute scores/results from raw data. Scaling of numerical values in PMML is easy. See blog on scaling and transformations in PMML for details.
2. The PMML exporter uses transformations to create dummy variables for categorical inputs. These are expressed in the NeuralInputs element of the resulting PMML file.
3. PMML does not support the censored variant of softmax.
4. Given that nnet uses a single output node to represent binary classification, the resulting PMML file contains a discretizer with a threshold set to 0.5.
   

BTW, any of the models you build in nnet and export using the PMML package can be uploaded directly into ADAPA for scoring.