Package: CMA 1.63.0
Roman Hornung
CMA: Synthesis of microarray-based classification
This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment.
Authors:
CMA_1.63.0.tar.gz
CMA_1.63.0.zip(r-4.5)CMA_1.63.0.zip(r-4.4)CMA_1.63.0.zip(r-4.3)
CMA_1.63.0.tgz(r-4.4-any)CMA_1.63.0.tgz(r-4.3-any)
CMA_1.63.0.tar.gz(r-4.5-noble)CMA_1.63.0.tar.gz(r-4.4-noble)
CMA_1.63.0.tgz(r-4.4-emscripten)CMA_1.63.0.tgz(r-4.3-emscripten)
CMA.pdf |CMA.html✨
CMA/json (API)
| # Install 'CMA' in R: |
| install.packages('CMA', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
On BioConductor:CMA-1.63.0(bioc 3.20)CMA-1.62.0(bioc 3.19)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 months agofrom:69919004b5
Exports:bestboxplotclassificationcomparecompBoostCMAdldaCMAElasticNetCMAevaluationfdaCMAflexdaCMAftableftestgbmCMAGenerateLearningsetsGeneSelectiongolubcritjoinknnCMAkruskaltestLassoCMAldaCMAlimmatestnnetCMAobsinfopknnCMAPlanarplotplotplrCMApls_ldaCMApls_lrCMApls_rfCMApnnCMApredictionqdaCMArfCMArferocscdaCMAshowshrinkldaCMAsummarysvmCMAtoplistttesttuneweighted.mcrwelchtestwilcoxtestwmc
Dependencies:BiobaseBiocGenerics
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Synthesis of microarray-based classification | CMA-package CMA |
| Barplot of variable importance | plot,genesel,missing-method plot,genesel-method |
| Show best hyperparameter settings | best best,tuningresult-method |
| Make a boxplot of the classifier evaluation | boxplot,evaloutput-method |
| General method for classification with various methods | classification |
| General method for classification with various methods | classification,data.frame,missing,formula-method classification,ExpressionSet,character,missing-method classification,matrix,factor,missing-method classification,matrix,numeric,missing-method classification-methods |
| "cloutput" | cloutput cloutput-class show,cloutput-method |
| "clvarseloutput" | clvarseloutput clvarseloutput-class |
| Compare different classifiers | compare |
| Compare different classifiers | compare,list-method compare-methods |
| Componentwise Boosting | compBoostCMA |
| Componentwise Boosting | compBoostCMA,data.frame,missing,formula-method compBoostCMA,ExpressionSet,character,missing-method compBoostCMA,matrix,factor,missing-method compBoostCMA,matrix,numeric,missing-method compBoostCMA-methods |
| Diagonal Discriminant Analysis | dldaCMA |
| Diagonal Discriminant Analysis | dldaCMA,data.frame,missing,formula-method dldaCMA,ExpressionSet,character,missing-method dldaCMA,matrix,factor,missing-method dldaCMA,matrix,numeric,missing-method dldaCMA-methods |
| Classfication and variable selection by the ElasticNet | ElasticNetCMA |
| Classfication and variable selection by the ElasticNet | ElasticNetCMA,data.frame,missing,formula-method ElasticNetCMA,ExpressionSet,character,missing-method ElasticNetCMA,matrix,factor,missing-method ElasticNetCMA,matrix,numeric,missing-method ElasticNetCMA-methods |
| "evaloutput" | evaloutput evaloutput-class obsinfo,evaloutput-method show,evaloutput-method |
| Evaluation of classifiers | evaluation |
| Evaluation of classifiers | evaluation,list-method evaluation-methods |
| Fisher's Linear Discriminant Analysis | fdaCMA |
| Fisher's Linear Discriminant Analysis | fdaCMA,data.frame,missing,formula-method fdaCMA,ExpressionSet,character,missing-method fdaCMA,matrix,factor,missing-method fdaCMA,matrix,numeric,missing-method fdaCMA-methods |
| Filter functions for Gene Selection | ftest golubcrit kruskaltest limmatest rfe shrinkcat ttest welchtest wilcoxtest |
| Flexible Discriminant Analysis | flexdaCMA |
| Flexible Discriminant Analysis | flexdaCMA,data.frame,missing,formula-method flexdaCMA,ExpressionSet,character,missing-method flexdaCMA,matrix,factor,missing-method flexdaCMA,matrix,numeric,missing-method flexdaCMA-methods |
| Cross-tabulation of predicted and true class labels | ftable,cloutput-method |
| Tree-based Gradient Boosting | gbmCMA |
| Tree-based Gradient Boosting | gbmCMA,data.frame,missing,formula-method gbmCMA,ExpressionSet,character,missing-method gbmCMA,matrix,factor,missing-method gbmCMA,matrix,numeric,missing-method gbmCMA-methods |
| Repeated Divisions into learn- and tets sets | GenerateLearningsets |
| "genesel" | genesel genesel-class show,genesel-method |
| General method for variable selection with various methods | GeneSelection |
| General method for variable selection with various methods | GeneSelection,data.frame,missing,formula-method GeneSelection,ExpressionSet,character,missing-method GeneSelection,matrix,factor,missing-method GeneSelection,matrix,numeric,missing-method GeneSelection-methods |
| ALL/AML dataset of Golub et al. (1999) | golub |
| Internal functions | bklr bklr.predict bkreg care.dev care.exp characterplot mklr mklr.predict mkreg my.care.exp plotprob ROCinternal roundvector rowswaps safeexp |
| Combine list elements returned by the method classification | join |
| Combine list elements returned by the method classification | join,list-method join-methods |
| Small blue round cell tumor dataset of Khan et al. (2001) | khan |
| Nearest Neighbours | knnCMA |
| Nearest Neighbours | knnCMA,data.frame,missing,formula-method knnCMA,ExpressionSet,character,missing-method knnCMA,matrix,factor,missing-method knnCMA,matrix,numeric,missing-method knnCMA-methods |
| L1 penalized logistic regression | LassoCMA |
| L1 penalized logistic regression | LassoCMA,data.frame,missing,formula-method LassoCMA,ExpressionSet,character,missing-method LassoCMA,matrix,factor,missing-method LassoCMA,matrix,numeric,missing-method LassoCMA-methods |
| Linear Discriminant Analysis | ldaCMA |
| Linear Discriminant Analysis | ldaCMA,data.frame,missing,formula-method ldaCMA,ExpressionSet,character,missing-method ldaCMA,matrix,factor,missing-method ldaCMA,matrix,numeric,missing-method ldaCMA-methods |
| "learningsets" | learningsets learningsets-class show,learningsets-method |
| Feed-forward Neural Networks | nnetCMA |
| Feed-Forward Neural Networks | nnetCMA,data.frame,missing,formula-method nnetCMA,ExpressionSet,character,missing-method nnetCMA,matrix,factor,missing-method nnetCMA,matrix,numeric,missing-method nnetCMA-methods |
| Classifiability of observations | obsinfo |
| Probabilistic Nearest Neighbours | pknnCMA |
| Probabilistic nearest neighbours | pknnCMA,data.frame,missing,formula-method pknnCMA,ExpressionSet,character,missing-method pknnCMA,matrix,factor,missing-method pknnCMA,matrix,numeric,missing-method pknnCMA-methods |
| Visualize Separability of different classes | Planarplot |
| Visualize Separability of different classes | Planarplot,data.frame,missing,formula-method Planarplot,ExpressionSet,character,missing-method Planarplot,matrix,factor,missing-method Planarplot,matrix,numeric,missing-method Planarplot-methods |
| Probability plot | plot,cloutput,missing-method plot,cloutput-method |
| Visualize results of tuning | plot,tuningresult,missing-method plot,tuningresult-method |
| L2 penalized logistic regression | plrCMA |
| L2 penalized logistic regression | plrCMA,data.frame,missing,formula-method plrCMA,ExpressionSet,character,missing-method plrCMA,matrix,factor,missing-method plrCMA,matrix,numeric,missing-method plrCMA-methods |
| Partial Least Squares combined with Linear Discriminant Analysis | pls_ldaCMA |
| Partial Least Squares combined with Linear Discriminant Analysis | pls_ldaCMA,data.frame,missing,formula-method pls_ldaCMA,ExpressionSet,character,missing-method pls_ldaCMA,matrix,factor,missing-method pls_ldaCMA,matrix,numeric,missing-method pls_ldaCMA-methods |
| Partial Least Squares followed by logistic regression | pls_lrCMA |
| Partial Least Squares followed by logistic regression | pls_lrCMA,data.frame,missing,formula-method pls_lrCMA,ExpressionSet,character,missing-method pls_lrCMA,matrix,factor,missing-method pls_lrCMA,matrix,numeric,missing-method pls_lrCMA-methods |
| Partial Least Squares followed by random forests | pls_rfCMA |
| Partial Least Squares followed by random forests | pls_rfCMA,data.frame,missing,formula-method pls_rfCMA,ExpressionSet,character,missing-method pls_rfCMA,matrix,factor,missing-method pls_rfCMA,matrix,numeric,missing-method pls_rfCMA-methods |
| Probabilistic Neural Networks | pnnCMA |
| Probabilistic Neural Networks | pnnCMA,data.frame,missing,formula-method pnnCMA,ExpressionSet,character,missing-method pnnCMA,matrix,factor,missing-method pnnCMA,matrix,numeric,missing-method pnnCMA-methods |
| General method for predicting classes of new observations | prediction |
| General method for predicting class lables of new observations | prediction,data.frame,missing,data.frame,formula-method prediction,ExpressionSet,character,ExpressionSet,missing-method prediction,matrix,ANY,matrix,missing-method prediction-methods |
| "predoutput" | predoutput predoutput-class show,predoutput-method |
| Quadratic Discriminant Analysis | qdaCMA |
| Quadratic Discriminant Analysis | qdaCMA,data.frame,missing,formula-method qdaCMA,ExpressionSet,character,missing-method qdaCMA,matrix,factor,missing-method qdaCMA,matrix,numeric,missing-method qdaCMA-methods |
| Classification based on Random Forests | rfCMA |
| Classification based on Random Forests | rfCMA,data.frame,missing,formula-method rfCMA,ExpressionSet,character,missing-method rfCMA,matrix,factor,missing-method rfCMA,matrix,numeric,missing-method rfCMA-methods |
| Receiver Operator Characteristic | roc roc,cloutput-method |
| Shrunken Centroids Discriminant Analysis | scdaCMA |
| Shrunken Centroids Discriminant Analysis | scdaCMA,data.frame,missing,formula-method scdaCMA,ExpressionSet,character,missing-method scdaCMA,matrix,factor,missing-method scdaCMA,matrix,numeric,missing-method scdaCMA-methods |
| Shrinkage linear discriminant analysis | shrinkldaCMA |
| Shrinkage linear discriminant analysis | shrinkldaCMA,data.frame,missing,formula-method shrinkldaCMA,ExpressionSet,character,missing-method shrinkldaCMA,matrix,factor,missing-method shrinkldaCMA,matrix,numeric,missing-method shrinkldaCMA-methods |
| Summarize classifier evaluation | summary,evaloutput-method |
| Support Vector Machine | svmCMA |
| Support Vector Machine | svmCMA,data.frame,missing,formula-method svmCMA,ExpressionSet,character,missing-method svmCMA,matrix,factor,missing-method svmCMA,matrix,numeric,missing-method svmCMA-methods |
| Display 'top' variables | toplist toplist,genesel-method |
| Hyperparameter tuning for classifiers | tune |
| Hyperparameter tuning for classifiers | tune,data.frame,missing,formula-method tune,ExpressionSet,character,missing-method tune,matrix,factor,missing-method tune,matrix,numeric,missing-method tune-methods |
| "tuningresult" | show,tuningresult-method tuningresult tuningresult-class |
| "varseloutput" | varseloutput varseloutput-class |
| Tuning / Selection bias correction | weighted.mcr |
| General method for tuning / selection bias correction | weighted.mcr,character,character,missing,character,matrix,factor-method weighted.mcr,character,character,numeric,character,matrix,factor-method weighted.mcr,character,character,numeric,character,matrix,numeric-method weighted.mcr-methods |
| Tuning / Selection bias correction based on matrix of subsampling fold errors | wmc |
| General method for tuning / selection bias correction based on a matrix of subsampling fold errors. | wmc,matrix,numeric,numeric-method wmc-methods |
| "wmcr.result" | show,wmcr.result-method wmcr.result wmcr.result-class |
