| care.dev |
Internal functions |
| care.exp |
Internal functions |
| characterplot |
Internal functions |
| classification |
General method for classification with various methods |
| classification,data.frame,missing,formula-method |
General method for classification with various methods |
| classification,ExpressionSet,character,missing-method |
General method for classification with various methods |
| classification,matrix,factor,missing-method |
General method for classification with various methods |
| classification,matrix,numeric,missing-method |
General method for classification with various methods |
| classification-methods |
General method for classification with various methods |
| cloutput |
"cloutput" |
| cloutput-class |
"cloutput" |
| clvarseloutput |
"clvarseloutput" |
| clvarseloutput-class |
"clvarseloutput" |
| CMA |
Synthesis of microarray-based classification |
| compare |
Compare different classifiers |
| compare,list-method |
Compare different classifiers |
| compare-methods |
Compare different classifiers |
| compBoostCMA |
Componentwise Boosting |
| compBoostCMA,data.frame,missing,formula-method |
Componentwise Boosting |
| compBoostCMA,ExpressionSet,character,missing-method |
Componentwise Boosting |
| compBoostCMA,matrix,factor,missing-method |
Componentwise Boosting |
| compBoostCMA,matrix,numeric,missing-method |
Componentwise Boosting |
| compBoostCMA-methods |
Componentwise Boosting |
| fdaCMA |
Fisher's Linear Discriminant Analysis |
| fdaCMA,data.frame,missing,formula-method |
Fisher's Linear Discriminant Analysis |
| fdaCMA,ExpressionSet,character,missing-method |
Fisher's Linear Discriminant Analysis |
| fdaCMA,matrix,factor,missing-method |
Fisher's Linear Discriminant Analysis |
| fdaCMA,matrix,numeric,missing-method |
Fisher's Linear Discriminant Analysis |
| fdaCMA-methods |
Fisher's Linear Discriminant Analysis |
| flexdaCMA |
Flexible Discriminant Analysis |
| flexdaCMA,data.frame,missing,formula-method |
Flexible Discriminant Analysis |
| flexdaCMA,ExpressionSet,character,missing-method |
Flexible Discriminant Analysis |
| flexdaCMA,matrix,factor,missing-method |
Flexible Discriminant Analysis |
| flexdaCMA,matrix,numeric,missing-method |
Flexible Discriminant Analysis |
| flexdaCMA-methods |
Flexible Discriminant Analysis |
| ftable,cloutput-method |
Cross-tabulation of predicted and true class labels |
| ftest |
Filter functions for Gene Selection |
| gbmCMA |
Tree-based Gradient Boosting |
| gbmCMA,data.frame,missing,formula-method |
Tree-based Gradient Boosting |
| gbmCMA,ExpressionSet,character,missing-method |
Tree-based Gradient Boosting |
| gbmCMA,matrix,factor,missing-method |
Tree-based Gradient Boosting |
| gbmCMA,matrix,numeric,missing-method |
Tree-based Gradient Boosting |
| gbmCMA-methods |
Tree-based Gradient Boosting |
| GenerateLearningsets |
Repeated Divisions into learn- and tets sets |
| genesel |
"genesel" |
| genesel-class |
"genesel" |
| GeneSelection |
General method for variable selection with various methods |
| GeneSelection,data.frame,missing,formula-method |
General method for variable selection with various methods |
| GeneSelection,ExpressionSet,character,missing-method |
General method for variable selection with various methods |
| GeneSelection,matrix,factor,missing-method |
General method for variable selection with various methods |
| GeneSelection,matrix,numeric,missing-method |
General method for variable selection with various methods |
| GeneSelection-methods |
General method for variable selection with various methods |
| golub |
ALL/AML dataset of Golub et al. (1999) |
| golubcrit |
Filter functions for Gene Selection |
| LassoCMA |
L1 penalized logistic regression |
| LassoCMA,data.frame,missing,formula-method |
L1 penalized logistic regression |
| LassoCMA,ExpressionSet,character,missing-method |
L1 penalized logistic regression |
| LassoCMA,matrix,factor,missing-method |
L1 penalized logistic regression |
| LassoCMA,matrix,numeric,missing-method |
L1 penalized logistic regression |
| LassoCMA-methods |
L1 penalized logistic regression |
| ldaCMA |
Linear Discriminant Analysis |
| ldaCMA,data.frame,missing,formula-method |
Linear Discriminant Analysis |
| ldaCMA,ExpressionSet,character,missing-method |
Linear Discriminant Analysis |
| ldaCMA,matrix,factor,missing-method |
Linear Discriminant Analysis |
| ldaCMA,matrix,numeric,missing-method |
Linear Discriminant Analysis |
| ldaCMA-methods |
Linear Discriminant Analysis |
| learningsets |
"learningsets" |
| learningsets-class |
"learningsets" |
| limmatest |
Filter functions for Gene Selection |
| pknnCMA |
Probabilistic Nearest Neighbours |
| pknnCMA,data.frame,missing,formula-method |
Probabilistic nearest neighbours |
| pknnCMA,ExpressionSet,character,missing-method |
Probabilistic nearest neighbours |
| pknnCMA,matrix,factor,missing-method |
Probabilistic nearest neighbours |
| pknnCMA,matrix,numeric,missing-method |
Probabilistic nearest neighbours |
| pknnCMA-methods |
Probabilistic nearest neighbours |
| Planarplot |
Visualize Separability of different classes |
| Planarplot,data.frame,missing,formula-method |
Visualize Separability of different classes |
| Planarplot,ExpressionSet,character,missing-method |
Visualize Separability of different classes |
| Planarplot,matrix,factor,missing-method |
Visualize Separability of different classes |
| Planarplot,matrix,numeric,missing-method |
Visualize Separability of different classes |
| Planarplot-methods |
Visualize Separability of different classes |
| plot,cloutput,missing-method |
Probability plot |
| plot,cloutput-method |
Probability plot |
| plot,genesel,missing-method |
Barplot of variable importance |
| plot,genesel-method |
Barplot of variable importance |
| plot,tuningresult,missing-method |
Visualize results of tuning |
| plot,tuningresult-method |
Visualize results of tuning |
| plotprob |
Internal functions |
| plrCMA |
L2 penalized logistic regression |
| plrCMA,data.frame,missing,formula-method |
L2 penalized logistic regression |
| plrCMA,ExpressionSet,character,missing-method |
L2 penalized logistic regression |
| plrCMA,matrix,factor,missing-method |
L2 penalized logistic regression |
| plrCMA,matrix,numeric,missing-method |
L2 penalized logistic regression |
| plrCMA-methods |
L2 penalized logistic regression |
| pls_ldaCMA |
Partial Least Squares combined with Linear Discriminant Analysis |
| pls_ldaCMA,data.frame,missing,formula-method |
Partial Least Squares combined with Linear Discriminant Analysis |
| pls_ldaCMA,ExpressionSet,character,missing-method |
Partial Least Squares combined with Linear Discriminant Analysis |
| pls_ldaCMA,matrix,factor,missing-method |
Partial Least Squares combined with Linear Discriminant Analysis |
| pls_ldaCMA,matrix,numeric,missing-method |
Partial Least Squares combined with Linear Discriminant Analysis |
| pls_ldaCMA-methods |
Partial Least Squares combined with Linear Discriminant Analysis |
| pls_lrCMA |
Partial Least Squares followed by logistic regression |
| pls_lrCMA,data.frame,missing,formula-method |
Partial Least Squares followed by logistic regression |
| pls_lrCMA,ExpressionSet,character,missing-method |
Partial Least Squares followed by logistic regression |
| pls_lrCMA,matrix,factor,missing-method |
Partial Least Squares followed by logistic regression |
| pls_lrCMA,matrix,numeric,missing-method |
Partial Least Squares followed by logistic regression |
| pls_lrCMA-methods |
Partial Least Squares followed by logistic regression |
| pls_rfCMA |
Partial Least Squares followed by random forests |
| pls_rfCMA,data.frame,missing,formula-method |
Partial Least Squares followed by random forests |
| pls_rfCMA,ExpressionSet,character,missing-method |
Partial Least Squares followed by random forests |
| pls_rfCMA,matrix,factor,missing-method |
Partial Least Squares followed by random forests |
| pls_rfCMA,matrix,numeric,missing-method |
Partial Least Squares followed by random forests |
| pls_rfCMA-methods |
Partial Least Squares followed by random forests |
| pnnCMA |
Probabilistic Neural Networks |
| pnnCMA,data.frame,missing,formula-method |
Probabilistic Neural Networks |
| pnnCMA,ExpressionSet,character,missing-method |
Probabilistic Neural Networks |
| pnnCMA,matrix,factor,missing-method |
Probabilistic Neural Networks |
| pnnCMA,matrix,numeric,missing-method |
Probabilistic Neural Networks |
| pnnCMA-methods |
Probabilistic Neural Networks |
| safeexp |
Internal functions |
| scdaCMA |
Shrunken Centroids Discriminant Analysis |
| scdaCMA,data.frame,missing,formula-method |
Shrunken Centroids Discriminant Analysis |
| scdaCMA,ExpressionSet,character,missing-method |
Shrunken Centroids Discriminant Analysis |
| scdaCMA,matrix,factor,missing-method |
Shrunken Centroids Discriminant Analysis |
| scdaCMA,matrix,numeric,missing-method |
Shrunken Centroids Discriminant Analysis |
| scdaCMA-methods |
Shrunken Centroids Discriminant Analysis |
| show,cloutput-method |
"cloutput" |
| show,evaloutput-method |
"evaloutput" |
| show,genesel-method |
"genesel" |
| show,learningsets-method |
"learningsets" |
| show,tuningresult-method |
"tuningresult" |
| shrinkldaCMA |
Shrinkage linear discriminant analysis |
| shrinkldaCMA,data.frame,missing,formula-method |
Shrinkage linear discriminant analysis |
| shrinkldaCMA,ExpressionSet,character,missing-method |
Shrinkage linear discriminant analysis |
| shrinkldaCMA,matrix,factor,missing-method |
Shrinkage linear discriminant analysis |
| shrinkldaCMA,matrix,numeric,missing-method |
Shrinkage linear discriminant analysis |
| shrinkldaCMA-methods |
Shrinkage linear discriminant analysis |
| summary,evaloutput-method |
Summarize classifier evaluation |
| svmCMA |
Support Vector Machine |
| svmCMA,data.frame,missing,formula-method |
Support Vector Machine |
| svmCMA,ExpressionSet,character,missing-method |
Support Vector Machine |
| svmCMA,matrix,factor,missing-method |
Support Vector Machine |
| svmCMA,matrix,numeric,missing-method |
Support Vector Machine |
| svmCMA-methods |
Support Vector Machine |