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 |