= R rpms =

'''rpms''' is an implementation of [[Statistics/DecisionTrees|decision trees]] and [[Statistics/RandomForest|random forests]] for R.

<<TableOfContents>>

----



== Installation ==

{{{
install.packages('rpms')
}}}


----



== Usage ==

A model is trained like:

{{{
tree <- rpms(y ~ va + vb + vc, data=data)
}}}

Note that the dependent variable (`y` in the above example) must be [[R/DataTypes#Numeric|numeric]]; it being any other class leads to error messages like "'list' object cannot be coerced to type 'double'".

Partitions are determined through randomized permutation and hypothesis tests.

Using a trained model, the predicted clusters can be attached to a (new) dataset like:

{{{
data$node <- end_nodes(tree, newdata=data)
}}}

Similarly, the predicted outcomes (which are uniform within a predicted cluster) can be attached to a (new) dataset like:

{{{
data$prediction <- predict(tree, newdata=data)
}}}



=== Options for Complex Survey Design ===

The hypothesis tests used in this package support complex survey designs.

{{{
tree <- rpms(y ~ va + vb + vc, data=data, weights=~wtvar, strata=~stratavar, cluster=~clustervar)
}}}

Given clusters, the trees are permuted in a [[APermutationTestOnComplexSampleData|2 step algorithm]]: first across clusters and then within clusters. This algorithm does not perform well when the clusters are significantly varying in (effective) size.



=== Visualization ===

To plot a specific partition, try:

{{{
node_plot(tree, node=1, data=data)
}}}

To render the entire tree, try the `qtree` function. This generates [[LaTeX]] figure markup which can be rendered separately. Note that rendering the figure depends on the `lscape` and `tikz-qtree` packages being included.



=== Random Forests ===

{{{
tree <- rpms(y ~ va + vb + vc, data=data)
}}}

Uniformly random trees are generated, and then aggregated as a weighted average. The trees are weighted by [[Statistics/InverseVarianceWeights|inverse variance]].



----
CategoryRicottone