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"Is is possible to see the output from an R model in Rapidminer?"
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I've been trying (unsucessfully so far) to place an operator "Execute R" inside a X-validation operator and get the performance metric (this I can) and the model output (say the regression coefficients--this I can't).
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="6.5.002">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="6.5.002" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="6.5.002" expanded="true" height="60" name="Retrieve Polynomial" width="90" x="45" y="30">
<parameter key="repository_entry" value="//Samples/data/Polynomial"/>
<description align="center" color="blue" colored="true" width="126">Fetch example data</description>
</operator>
<operator activated="true" class="x_validation" compatibility="6.5.002" expanded="true" height="112" name="Validation" width="90" x="581" y="120">
<process expanded="true">
<operator activated="true" class="r_scripting:execute_r" compatibility="6.5.000" expanded="true" height="76" name="Learn Model" width="90" x="246" y="120">
<parameter key="script" value="# train a linear model on the training data and return the learned model rm_main = function(data) { 	linearModel <- lm(formula =label ~ . , data =data) 	return(linearModel) } "/>
<description align="center" color="red" colored="true" width="126">Train a linear model in R and return it as an R object</description>
</operator>
<connect from_port="training" to_op="Learn Model" to_port="input 1"/>
<connect from_op="Learn Model" from_port="output 1" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="r_scripting:execute_r" compatibility="6.5.000" expanded="true" height="94" name="Apply R Model" width="90" x="112" y="75">
<parameter key="script" value="## load the trained model and apply it on the test data rm_main = function(model, data) { # apply the model and build a prediction result <-predict(model, data) # add the prediction to the example set data$prediction <- result # update the meta data metaData$data$prediction <<- list(type="real", role="prediction") return(data) } "/>
<description align="center" color="red" colored="true" width="126">Apply the trained model on the test data</description>
</operator>
<operator activated="true" class="performance" compatibility="6.5.002" expanded="true" height="76" name="Performance" width="90" x="313" y="75"/>
<connect from_port="model" to_op="Apply R Model" to_port="input 1"/>
<connect from_port="test set" to_op="Apply R Model" to_port="input 2"/>
<connect from_op="Apply R Model" from_port="output 1" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<connect from_op="Retrieve Polynomial" from_port="output" to_op="Validation" to_port="training"/>
<connect from_op="Validation" from_port="model" to_port="result 2"/>
<connect from_op="Validation" from_port="averagable 1" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
<portSpacing port="sink_result 3" spacing="0"/>
</process>
</operator>
</process>
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Answers
the R lm model your first Operator is returned as a file object, which can passed to another R Operator simply be used there.
To use this inside the X-Validation you have to bypass the normal workflow and pass the training Object via the "through" port to the validation site of the operator. But as this a special scenario and X-Validation normally expects an RapidMiner model to be used, you have to connect a dummy object as well.
See the attached process for a solution. Best,
David