Package: nlcv 0.3.5
nlcv: Nested Loop Cross Validation
Nested loop cross validation for classification purposes for misclassification error rate estimation. The package supports several methodologies for feature selection: random forest, Student t-test, limma, and provides an interface to the following classification methods in the 'MLInterfaces' package: linear, quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of the classifier are included: plot of the ranks of the features, scores plot for a specific classification algorithm and number of features, misclassification rate for the different number of features and classification algorithms tested and ROC plot. For further details about the methodology, please check: Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004) <doi:10.2202/1544-6115.1078>.
Authors:
nlcv_0.3.5.tar.gz
nlcv_0.3.5.zip(r-4.5)nlcv_0.3.5.zip(r-4.4)nlcv_0.3.5.zip(r-4.3)
nlcv_0.3.5.tgz(r-4.4-any)nlcv_0.3.5.tgz(r-4.3-any)
nlcv_0.3.5.tar.gz(r-4.5-noble)nlcv_0.3.5.tar.gz(r-4.4-noble)
nlcv_0.3.5.tgz(r-4.4-emscripten)nlcv_0.3.5.tgz(r-4.3-emscripten)
nlcv.pdf |nlcv.html✨
nlcv/json (API)
NEWS
| # Install 'nlcv' in R: |
| install.packages('nlcv', repos = c('https://lcougnaud.r-universe.dev', 'https://cloud.r-project.org')) |
- nlcvRF_R - Nlcv results on random data with random forest feature selection
- nlcvRF_SHS - Nlcv results on strong hetero signal data with random forest feature selection
- nlcvRF_SS - Nlcv results on strong signal data a with random forest feature selection
- nlcvRF_WHS - Nlcv results on weak signal data with random forest feature selection
- nlcvRF_WS - Nlcv results on weak hetero signal data with random forest feature selection
- nlcvTT_R - Nlcv results on random data with t-test feature selection
- nlcvTT_SHS - Nlcv results on strong hetero signal data with t-test feature selection
- nlcvTT_SS - Nlcv results on strong signal data a with t-test feature selection
- nlcvTT_WHS - Nlcv results on weak signal data with t-test feature selection
- nlcvTT_WS - Nlcv results on weak hetero signal data with t-test feature selection
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 years agofrom:6d2e9bddb4. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 15 2024 |
| R-4.5-win | NOTE | Sep 15 2024 |
| R-4.5-linux | NOTE | Sep 15 2024 |
| R-4.4-win | NOTE | Sep 15 2024 |
| R-4.4-mac | NOTE | Sep 15 2024 |
| R-4.3-win | NOTE | Sep 15 2024 |
| R-4.3-mac | NOTE | Sep 15 2024 |
Exports:mcrPlotnlcvnldaIpamrIpamrMLpamrTrainrankDistributionPlotrocPlotscoresPlot
Dependencies:a4CoreabindannotateAnnotationDbiaskpassassertthatbase64encBiobaseBiocGenericsBiostringsbitbit64bitopsblobbslibcachemcaToolsclasscliclustercodetoolscommonmarkcpp11crayoncrosstalkcurldata.tableDBIDelayedArrayDEoptimRdiagramdigestdiptestdplyre1071evaluatefansifastmapflexmixfontawesomeforeachfpcfsfuturefuture.applygbmgdatagenefiltergenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggvisglmnetglobalsgluegplotsgtoolshighrhtmltoolshtmlwidgetshttpuvhttrhwriterigraphipredIRangesiteratorsjquerylibjsonliteKEGGRESTkernlabKernSmoothknitrlaterlatticelavalazyevallifecyclelimmalistenvmagrittrMASSMatrixMatrixGenericsmatrixStatsmclustmemoisemimemlbenchMLInterfacesmodeltoolsmulttestnnetnumDerivopensslpamrparallellypillarpkgconfigplogrplspngprabclusprodlimprogressrpromisesproxyR6randomForestrappdirsRColorBrewerRcppRcppEigenrlangrmarkdownrobustbaseROCRrpartRSQLiteS4ArraysS4VectorssasssfsmiscshapeshinysourcetoolsSparseArraySQUAREMstatmodSummarizedExperimentsurvivalsysthreejstibbletidyselecttinytexUCSC.utilsutf8vctrswithrxfunXMLxtableXVectoryamlzlibbioc
