Package index
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NCI60 - NCI60 data set
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PPclassify() - Predict class for the test set and calculate prediction error after finding the PPtree structure, .
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PPforest() - Projection Pursuit Random Forest
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PPtree_split() - Projection pursuit classification tree with random variable selection in each split
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baggtree() - For each bootstrap sample grow a projection pursuit tree (PPtree object).
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crab - Astralian crabs
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fishcatch - Fish catch data set
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glass - Glass data set
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image - The image data set
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leukemia - Leukemia data set This dataset comes from a study of gene expression in two types of acute leukemias, acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). Gene expression levels were measured using Affymetrix high density oligonucleotide arrays containing 6817 human genes. A data set containing 72 observations from 3 leukemia types classes.
- Type
has 3 classes with 38 cases of B-cell ALL, 25 cases of AML and 9 cases of T-cell ALL
. - Gene1 to Gen 40
gene expression levels
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lymphoma - Lymphoma data set
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node_data() - Data structure with the projected and boundary by node and class.
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olive - The olive data set
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parkinson - Parkinson data set
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permute_importance() - Obtain the permuted importance variable measure
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ppf_avg_imp() - Global importance measure for a PPforest object as the average IMP PPtree measure over all the trees in the forest
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ppf_global_imp() - Global importance measure for a PPforest object
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predict(<PPforest>) - Predict method for PPforest objects
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print(<PPforest>) - Print PPforest object
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ternary_str() - Data structure with the projected and boundary by node and class.
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trees_pred() - Obtain predicted class for new data from baggtree function or PPforest
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wine - Wine data set