R: Synthesis of microarray-based classification

# Synthesis of microarray-based classification

## Documentation for package ‘CMA’ version 1.70.0

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

B C D E F G J K L M N O P Q R S T V W

CMA-package | Synthesis of microarray-based classification

## -- B --

best | Show best hyperparameter settings
best-method | Show best hyperparameter settings
bklr | Internal functions
bklr.predict | Internal functions
bkreg | Internal functions
boxplot-method | Make a boxplot of the classifier evaluation

## -- C --

care.dev | Internal functions
care.exp | Internal functions
characterplot | Internal functions
classification | General method for classification with various methods
classification-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-method | Compare different classifiers
compare-methods | Compare different classifiers
compBoostCMA | Componentwise Boosting
compBoostCMA-method | Componentwise Boosting
compBoostCMA-methods | Componentwise Boosting

## -- D --

dldaCMA | Diagonal Discriminant Analysis
dldaCMA-method | Diagonal Discriminant Analysis
dldaCMA-methods | Diagonal Discriminant Analysis

## -- E --

ElasticNetCMA | Classfication and variable selection by the ElasticNet
ElasticNetCMA-method | Classfication and variable selection by the ElasticNet
ElasticNetCMA-methods | Classfication and variable selection by the ElasticNet
evaloutput | "evaloutput"
evaloutput-class | "evaloutput"
evaluation | Evaluation of classifiers
evaluation-method | Evaluation of classifiers
evaluation-methods | Evaluation of classifiers

## -- F --

fdaCMA | Fisher's Linear Discriminant Analysis
fdaCMA-method | Fisher's Linear Discriminant Analysis
fdaCMA-methods | Fisher's Linear Discriminant Analysis
flexdaCMA | Flexible Discriminant Analysis
flexdaCMA-method | Flexible Discriminant Analysis
flexdaCMA-methods | Flexible Discriminant Analysis
ftable-method | Cross-tabulation of predicted and true class labels
ftest | Filter functions for Gene Selection

## -- G --

gbmCMA | Tree-based Gradient Boosting
gbmCMA-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-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

## -- J --

join | Combine list elements returned by the method classification
join-method | Combine list elements returned by the method classification
join-methods | Combine list elements returned by the method classification

## -- K --

khan | Small blue round cell tumor dataset of Khan et al. (2001)
knnCMA | Nearest Neighbours
knnCMA-method | Nearest Neighbours
knnCMA-methods | Nearest Neighbours
kruskaltest | Filter functions for Gene Selection

## -- L --

LassoCMA | L1 penalized logistic regression
LassoCMA-method | L1 penalized logistic regression
LassoCMA-methods | L1 penalized logistic regression
ldaCMA | Linear Discriminant Analysis
ldaCMA-method | Linear Discriminant Analysis
ldaCMA-methods | Linear Discriminant Analysis
learningsets | "learningsets"
learningsets-class | "learningsets"
limmatest | Filter functions for Gene Selection

## -- M --

mklr | Internal functions
mklr.predict | Internal functions
mkreg | Internal functions
my.care.exp | Internal functions

## -- N --

nnetCMA | Feed-forward Neural Networks
nnetCMA-method | Feed-Forward Neural Networks
nnetCMA-methods | Feed-Forward Neural Networks

## -- O --

obsinfo | Classifiability of observations
obsinfo-method | "evaloutput"

## -- P --

pknnCMA | Probabilistic Nearest Neighbours
pknnCMA-method | Probabilistic nearest neighbours
pknnCMA-methods | Probabilistic nearest neighbours
Planarplot | Visualize Separability of different classes
Planarplot-method | Visualize Separability of different classes
Planarplot-methods | Visualize Separability of different classes
plot-method | Probability plot
plot-method | Barplot of variable importance
plot-method | Visualize results of tuning
plotprob | Internal functions
plrCMA | L2 penalized logistic regression
plrCMA-method | L2 penalized logistic regression
plrCMA-methods | L2 penalized logistic regression
pls_ldaCMA | Partial Least Squares combined with Linear Discriminant Analysis
pls_ldaCMA-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-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-method | Partial Least Squares followed by random forests
pls_rfCMA-methods | Partial Least Squares followed by random forests
pnnCMA | Probabilistic Neural Networks
pnnCMA-method | Probabilistic Neural Networks
pnnCMA-methods | Probabilistic Neural Networks
prediction | General method for predicting classes of new observations
prediction-method | General method for predicting class lables of new observations
prediction-methods | General method for predicting class lables of new observations
predoutput | "predoutput"
predoutput-class | "predoutput"

## -- Q --

qdaCMA | Quadratic Discriminant Analysis
qdaCMA-method | Quadratic Discriminant Analysis
qdaCMA-methods | Quadratic Discriminant Analysis

## -- R --

rfCMA | Classification based on Random Forests
rfCMA-method | Classification based on Random Forests
rfCMA-methods | Classification based on Random Forests
rfe | Filter functions for Gene Selection
roc | Receiver Operator Characteristic
roc-method | Receiver Operator Characteristic
ROCinternal | Internal functions
roundvector | Internal functions
rowswaps | Internal functions

## -- S --

safeexp | Internal functions
scdaCMA | Shrunken Centroids Discriminant Analysis
scdaCMA-method | Shrunken Centroids Discriminant Analysis
scdaCMA-methods | Shrunken Centroids Discriminant Analysis
show-method | "cloutput"
show-method | "evaloutput"
show-method | "genesel"
show-method | "learningsets"
show-method | "predoutput"
show-method | "tuningresult"
show-method | "wmcr.result"
shrinkcat | Filter functions for Gene Selection
shrinkldaCMA | Shrinkage linear discriminant analysis
shrinkldaCMA-method | Shrinkage linear discriminant analysis
shrinkldaCMA-methods | Shrinkage linear discriminant analysis
summary-method | Summarize classifier evaluation
svmCMA | Support Vector Machine
svmCMA-method | Support Vector Machine
svmCMA-methods | Support Vector Machine

## -- T --

toplist | Display 'top' variables
toplist-method | Display 'top' variables
ttest | Filter functions for Gene Selection
tune | Hyperparameter tuning for classifiers
tune-method | Hyperparameter tuning for classifiers
tune-methods | Hyperparameter tuning for classifiers
tuningresult | "tuningresult"
tuningresult-class | "tuningresult"

## -- V --

varseloutput | "varseloutput"
varseloutput-class | "varseloutput"

## -- W --

weighted.mcr | Tuning / Selection bias correction
weighted.mcr-method | General method for tuning / selection bias correction
weighted.mcr-methods | General method for tuning / selection bias correction
welchtest | Filter functions for Gene Selection
wilcoxtest | Filter functions for Gene Selection
wmc | Tuning / Selection bias correction based on matrix of subsampling fold errors
wmc-method | General method for tuning / selection bias correction based on a matrix of subsampling fold errors.
wmc-methods | General method for tuning / selection bias correction based on a matrix of subsampling fold errors.
wmcr.result | "wmcr.result"
wmcr.result-class | "wmcr.result"