pimp.import {LogicForest}R Documentation

Predictor Importance - Variables and Interactions

Description

Measures the predictor importance for each predictor and interaction importance for each iteration.

Usage

pimp.import(fit, data, testdata, BSpred, pred, Xs, mtype)

Arguments

fit

Fit information including outcome/model type, input data, logic tree, etc.

data

In-bag sample (i.e., training data).

testdata

Out-of-bag sample (i.e., test data).

BSpred

Number of Xs in the interactions (includes not-ed variables).

pred

Matrix of predicted values.

Xs

Matrix or data frame of zeros and ones for all predictor variables.

mtype

Model type: "classification", "linear", or "survival regression".

Details

This function is called to calculate importance measures for each bootstrapped sample. Importance measures are calculated as differences between the original out-of-bag sample and a permuted out-of-bag sample. Model fit for both samples is evaluated using:

Value

A list with:

single.vimp

Vector of predictor importance estimates. One estimate per predictor used in the tree of that sample.

pimp.vimp

Vector of interaction importance estimates. One estimate per interaction detected in that sample.

Ipimat

Matrix updated for each sample. Contains all predictors (and their NOT-ed versions if used) for each interaction.

vec.Xvars

Vector of predictors used in the tree of that sample.

Xids

Vector of predictor IDs used in the tree of that sample.

Author(s)

Bethany J. Wolf wolfb@musc.edu
J. Madison Hyer madison.hyer@osumc.edu

References

Wolf BJ, Hill EG, Slate EH. Logic Forest: an ensemble classifier for discovering logical combinations of binary markers. Bioinformatics. 2010;26(17):2183-2189. doi:10.1093/bioinformatics/btq354

See Also

logforest


[Package LogicForest version 2.1.2 Index]