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<tool id="randomForest" name="randomForest" version="1.0.1">
  <description>predict phenotype using a random forest approach</description>
  <command interpreter="Rscript">
	  randomForest.R $config &gt; ${output1}
  </command>
  
  <inputs>
	<param name="genotype" type="data"
		label="genotype data" help="a tabular datatype containing the encoded genotypes" 
	/>
		  
	<param name="phenotype" type="data"
			label="phenotype data" help=" a tabular datatype containing the phenotypes " 
			/>
	
	<param name="eval" type="integer" value="0"
			label="do evaluation" help=" whether to produce a model or to use folds to evaluate the tool. 1 means the tool will be evaluate (and a folds argument is required) any other value produces a model " 
	/>
	
	<param name="ntree" type="float" value="1000"
			label="ntree" help="the ntree parameter of Random Forest. Suitable value could be around 1000. " 
			/>
			
	<param name="mtry" type="float" value="-1"
			label="mtry" help="the mtry parameter of Random Forest (number of node per tree). default value are around nuber_of_variable/3 through optimization is highly desirable. -1 means the parameter must be optimized by the tool" 
			/>
	
	<param name="folds" type="data" optional="true"
			label="folds" help=" OPTIONAL ARGUMENT path to a folds file containing folds indexes in a R list called /folds/ such as produced by the folds tools in OGHMA suite. " 
			/>
		
	<!-- <param name="model" type="text"
			label="path to the output folds" help= " a path to a file where the results (depending on the chosen mode) will be writen" 
	/> -->
  </inputs>
  
  <configfiles>
    <configfile name="config">
## Desc: this file is sourced in encode wrapper script
##  as means to pass all galaxy params to R
"${genotype}" -> genotype
"${phenotype}" -> phenotype
"${eval}" -> doEvaluation
"${folds}" -> folds
"${output1}" -> out
"${mtry}" -> mtry
"${ntree}" -> ntree

    </configfile>
</configfiles>
  
<outputs>
	<data format="tabular" name = "output1" label="random forest output" />
</outputs>
  
  <help>
	  make the classification using the random forest method
  </help>
</tool>