Package: PPforest 0.1.3

PPforest: Projection Pursuit Classification Forest

Implements projection pursuit forest algorithm for supervised classification.

Authors:Natalia da Silva, Eun-Kyung Lee, Di Cook

PPforest_0.1.3.tar.gz
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PPforest.pdf |PPforest.html✨
PPforest/json (API)
NEWS

# Install 'PPforest' in R:
install.packages('PPforest', repos = c('https://natydasilva.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/natydasilva/ppforest/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • NCI60 - NCI60 data set
  • crab - Astralian crabs
  • fishcatch - Fish catch data set
  • glass - Glass data set
  • image - The image data set
  • 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
  • lymphoma - Lymphoma data set
  • olive - The olive data set
  • parkinson - Parkinson data set
  • wine - Wine data set

On CRAN:

5.53 score 18 stars 19 scripts 202 downloads 10 exports 28 dependencies

Last updated 5 months agofrom:6e37d833bf. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-win-x86_64OKNov 04 2024
R-4.5-linux-x86_64OKNov 04 2024
R-4.4-win-x86_64OKNov 04 2024
R-4.4-mac-x86_64OKNov 04 2024
R-4.4-mac-aarch64OKNov 04 2024
R-4.3-win-x86_64OKNov 04 2024
R-4.3-mac-x86_64OKNov 04 2024
R-4.3-mac-aarch64OKNov 04 2024

Exports:baggtreenode_datapermute_importancePPclassify2ppf_avg_impppf_global_impPPforestPPtree_splitternary_strtrees_pred

Dependencies:clicodetoolscpp11doParalleldplyrfansiforeachgenericsglueiteratorslifecyclemagrittrpillarpkgconfigplyrpurrrR6RcppRcppArmadillorlangstringistringrtibbletidyrtidyselectutf8vctrswithr

Projection pursuit classification random forest

N. da Silva, D. Cook & E.K Lee

Rendered fromPPforest-vignette.Rmdusingknitr::rmarkdownon Nov 04 2024.

Last update: 2021-10-05
Started: 2017-01-29

Readme and manuals

Help Manual

Help pageTopics
For each bootstrap sample grow a projection pursuit tree (PPtree object).baggtree
Astralian crabscrab
Fish catch data setfishcatch
Glass data setglass
The image data setimage
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 levelsleukemia
Lymphoma data setlymphoma
NCI60 data setNCI60
Data structure with the projected and boundary by node and class.node_data
The olive data setolive
Parkinson data setparkinson
Obtain the permuted importance variable measurepermute_importance
Predict class for the test set and calculate prediction error after finding the PPtree structure, .PPclassify2
Global importance measure for a PPforest object as the average IMP PPtree measure over all the trees in the forestppf_avg_imp
Global importance measure for a PPforest objectppf_global_imp
Projection Pursuit Random ForestPPforest
Projection pursuit classification tree with random variable selection in each splitPPtree_split
Print PPforest objectprint.PPforest
Data structure with the projected and boundary by node and class.ternary_str
Obtain predicted class for new data from baggtree function or PPforesttrees_pred
Wine data setwine