Man pages for chbernau/CMA
Synthesis of microarray-based classification
| best | Show best hyperparameter settings |
| boxplot | Make a boxplot of the classifier evaluation |
| classification | General method for classification with various methods |
| classification-methods | General method for classification with various methods |
| cloutput-class | "cloutput" |
| clvarseloutput-class | "clvarseloutput" |
| CMA-package | Synthesis of microarray-based classification |
| compare | Compare different classifiers |
| compare-methods | Compare different classifiers |
| compBoostCMA | Componentwise Boosting |
| compBoostCMA-methods | Componentwise Boosting |
| dldaCMA | Diagonal Discriminant Analysis |
| dldaCMA-methods | Diagonal Discriminant Analysis |
| ElasticNetCMA | Classfication and variable selection by the ElasticNet |
| ElasticNetCMA-methods | Classfication and variable selection by the ElasticNet |
| evaloutput-class | "evaloutput" |
| evaluation | Evaluation of classifiers |
| evaluation-methods | Evaluation of classifiers |
| fdaCMA | Fisher's Linear Discriminant Analysis |
| fdaCMA-methods | Fisher's Linear Discriminant Analysis |
| filter | Filter functions for Gene Selection |
| flexdaCMA | Flexible Discriminant Analysis |
| flexdaCMA-methods | Flexible Discriminant Analysis |
| ftable | Cross-tabulation of predicted and true class labels |
| gbmCMA | Tree-based Gradient Boosting |
| gbmCMA-methods | Tree-based Gradient Boosting |
| GenerateLearningsets | Repeated Divisions into learn- and tets sets |
| genesel-class | "genesel" |
| GeneSelection | 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) |
| internals | Internal functions |
| join | Combine list elements returned by the method classification |
| join-methods | Combine list elements returned by the method classification |
| khan | Small blue round cell tumor dataset of Khan et al. (2001) |
| knnCMA | Nearest Neighbours |
| knnCMA-methods | Nearest Neighbours |
| LassoCMA | L1 penalized logistic regression |
| LassoCMA-methods | L1 penalized logistic regression |
| ldaCMA | Linear Discriminant Analysis |
| ldaCMA-methods | Linear Discriminant Analysis |
| learningsets-class | "learningsets" |
| nnetCMA | Feed-forward Neural Networks |
| nnetCMA-methods | Feed-Forward Neural Networks |
| obsinfo | Classifiability of observations |
| pknnCMA | Probabilistic Nearest Neighbours |
| pknnCMA-methods | Probabilistic nearest neighbours |
| Planarplot | Visualize Separability of different classes |
| Planarplot-methods | Visualize Separability of different classes |
| plot,cloutput-method | Probability plot |
| plot,genesel-method | Barplot of variable importance |
| plot,tuningresult-method | Visualize results of tuning |
| plrCMA | L2 penalized logistic regression |
| plrCMA-methods | L2 penalized logistic regression |
| pls_ldaCMA | Partial Least Squares combined with Linear Discriminant... |
| pls_ldaCMA-methods | Partial Least Squares combined with Linear Discriminant... |
| pls_lrCMA | 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-methods | Partial Least Squares followed by random forests |
| pnnCMA | Probabilistic Neural Networks |
| pnnCMA-methods | Probabilistic Neural Networks |
| prediction | General method for predicting classes of new observations |
| prediction-methods | General method for predicting class lables of new... |
| predoutput-class | "predoutput" |
| qdaCMA | Quadratic Discriminant Analysis |
| qdaCMA-methods | Quadratic Discriminant Analysis |
| rfCMA | Classification based on Random Forests |
| rfCMA-methods | Classification based on Random Forests |
| roc | Receiver Operator Characteristic |
| scdaCMA | Shrunken Centroids Discriminant Analysis |
| scdaCMA-methods | Shrunken Centroids Discriminant Analysis |
| shrinkldaCMA | Shrinkage linear discriminant analysis |
| shrinkldaCMA-methods | Shrinkage linear discriminant analysis |
| summary | Summarize classifier evaluation |
| svmCMA | Support Vector Machine |
| svmCMA-methods | Support Vector Machine |
| toplist | Display 'top' variables |
| tune | Hyperparameter tuning for classifiers |
| tune-methods | Hyperparameter tuning for classifiers |
| tuningresult-class | "tuningresult" |
| varseloutput-class | "varseloutput" |
| weighted_mcr | Tuning / Selection bias correction |
| weighted_mcr-methods | General method for tuning / selection bias correction |
| wmc | Tuning / Selection bias correction based on matrix of... |
| wmc-methods | General method for tuning / selection bias correction based... |
| wmcr_result-class | "wmcr.result" |
