Measures the predictor importance for each predictor and interaction importance for each iteration.
pimp.import(fit, data, testdata, BSpred, pred, Xs, mtype)A list with:
Vector of predictor importance estimates. One estimate per predictor used in the tree of that sample.
Vector of interaction importance estimates. One estimate per interaction detected in that sample.
Matrix updated for each sample. Contains all predictors (and their NOT-ed versions if used) for each interaction.
Vector of predictors used in the tree of that sample.
Vector of predictor IDs used in the tree of that sample.
Fit information including outcome/model type, input data, logic tree, etc.
In-bag sample (i.e., training data).
Out-of-bag sample (i.e., test data).
Number of Xs in the interactions (includes not-ed variables).
Matrix of predicted values.
Matrix or data frame of zeros and ones for all predictor variables.
Model type: "classification", "linear", or "survival regression".
Bethany J. Wolf wolfb@musc.edu
J. Madison Hyer madison.hyer@osumc.edu
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:
Concordance for classification,
Mean squared error for linear regression,
Harrell's C-index for survival regression.
Wolf BJ, Hill EG, Slate EH. Logic Forest: an ensemble classifier for discovering logical combinations of binary markers. Bioinformatics. 2010;26(17):2183-2189. tools:::Rd_expr_doi("10.1093/bioinformatics/btq354")
logforest