| Title: | MicroArray Gene-expression-based Program In Error rate estimation |
|---|---|
| Description: | Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes. |
| Authors: | Camille Maumet <[email protected]>, with contributions from C. Ambroise J. Zhu |
| Maintainer: | Camille Maumet <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.63.0 |
| Built: | 2024-11-30 04:03:15 UTC |
| Source: | https://github.com/bioc/Rmagpie |
This class stores the information relevant to a microarray classification assessment: data set, classifier and options are set here and then one-layer and two-layer cross-validation can be applied.
new("assessment", dataset noFolds1stLayer=10, noFolds2ndLayer=9,
classifierName="svm", featureSelectionMethod="rfe",
typeFoldCreation="original", svmKernel="linear",
noOfRepeat=2, featureSelectionOptions)
Creates an assessment to be performed on the data set dataset using the feature
selection options defined by featureSelectionMethod on the feature selection method
featureSelectionMethod and with the classifier classifierName. Once
all the options have been selected one-layer and two-layers of cross-validation can be
performed by calling runOneLayerExtCv and runTwoLayerExtCv respectively.
new("assessment", dataset noFolds1stLayer=10, noFolds2ndLayer=9,
classifierName="svm", featureSelectionMethod="rfe",
typeFoldCreation="original", svmKernel="linear",
noOfRepeat=2)
If featureSelectionOptions is not precised in the arguments then the options for
the feature selection method are determined according to the dataset and
the featureSelectionMethod. If RFE is selected as feature selection method
then an object of class geneSubsets is automatically created. It defines sizes of
subsets og genes for 1 to the number of features in the dataset by power of 2.
If the feature selection method is NSC then the thresholds are taken to be the default
thresholds generated by the function pamr.train from package pamr
applied on dataset.
dataset:Object of class "dataset". Microarray data set to be used for cross-validation
noFolds1stLayer:numeric. Number of folds in the inner layee layer of cross-validation
noFolds2ndLayer:numeric. Number of folds in one-layer cross-validation and in the
second layer of cross-validation
classifierName:character. Name of the classifier: 'svm' for Support Vector
Machines or 'nsc' for Nearest Shrunken Centroid
featureSelectionMethod:Object of class "character" ~~
typeFoldCreation:character. Type of fold creation: 'original', 'simple' or 'naive'
svmKernel:Object of class "character" ~~
noOfRepeats:numeric. Number of repeats to be performed for each cross-validation.
featureSelectionOptions:Object of class "featureSelectionOptions". Sizes of subsets
to be tried in the RFE or thresholds to be tried with the NSC.
resultRepeated1LayerCV:Object of class "resultRepeated1LayerCVOrNULL" NULL is the external one layer CV has not been run yet, resultRepeated1LayerCV containing the results
resultRepeated2LayerCV:Object of class "result2LayerCVorNULL" NULL is the external one layer CV has not been run yet, result2LayerCV containing the results
finalClassifier:Object of class "finalClassifierOrNULL" NULL is the final classifier has not been determined yet, finalClassifier containing the final Classifier for each feature selection option.
classifyNewSamples(assessment)Classify new samples using the final classifier. See related documentation.
findFinalClassifier(assessment)Train the final classifier related to an assessment based on each feature selection option. See related documentation
getClassifierName(assessment), getClassifierName(assessment)<-Retrieve and Modify the classifier name associated to the current assessment (slot classifierName)
getDataset(assessment), getDataset(assessment)<-Retrieve and Modify the dataset associated to the current assessment (slot dataset), see related documentation for more details.
getFeatureSelectionOptions(assessment), getFeatureSelectionOptions(assessment)<-Retrieve and Modify the options of feature selection associated to the current assessment (slot featureSelectionOptions)
getFinalClassifier(assessment)Retreive the final classifier associated with an exeperiment.
getNoFolds1stLayer(assessment), getNoFolds1stLayer(assessment)<-Retrieve and Modify the number of folds in the inner layer of cross-validation (slot nbFolds1stLayer)
getNoFolds2ndLayer(assessment), getNoFolds2ndLayer(assessment)<-Retrieve and Modify the number of folds in the outer layer of cross-validation (slot nbFolds1stLayer)
getNoOfRepeats(assessment), getNoOfRepeats(assessment)<-Retrieve and Modify the number of repeats of each cross-validation (slot nbOfRepeat)
getResult1LayerCV(assessment)Retrieve the results of the one-layer
cross validation (slot resultRepeated1LayerCV). An easier access to this data is
available via the method getResults )
getResult2LayerCV(assessment)Retrieve the results of the two-layers
cross validation (slot result2LayerCV). An easier access to this data is
available via the method getResults
User-friendly methods to retreive data in the results of one-layer and two-layers of cross-validation. See related documentation page.
getSvmKernel(assessment), getSvmKernel(assessment)<-Retrieve and Modify the svm kernel used as a final classifier if svm is the concerned classifier and during the Recusrsive Feature Elimination (slot svmKernel)
getTypeFoldCreation(assessment), getTypeFoldCreation(assessment)<-Retrieve and Modify the type of folds creation to use for each cross-validation (slot typeFoldCreation)
runOneLayerExtCVRun one-layer cross-validation, see related documantation for more details.
runTwoLayerExtCVRun two-layer cross-validation, see related documantation for more details.
Camille Maumet
geneSubsets, getResults-methods,
runOneLayerExtCV-methods, runTwoLayerExtCV-methods
#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "data") #myDataset <- new("dataset", dataId="vantVeer_70", dataPath=file.path(dataPath, "vantVeer_70")) # myDataset<-loadData(myDataset) data('vV70genesDataset') # assessment with RFE and SVM myExpe <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) # Another assessment where the subsets are computed automatically anotherExpe <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2) getFeatureSelectionOptions(anotherExpe, topic='maxSubsetSize') getFeatureSelectionOptions(anotherExpe, topic='subsetsSizes') # assessment with NSC expeWithNSC <- new("assessment",dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="nsc", featureSelectionMethod='nsc', typeFoldCreation="original", svmKernel="linear", noOfRepeat=2) getFeatureSelectionOptions(expeWithNSC, topic='thresholds')#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "data") #myDataset <- new("dataset", dataId="vantVeer_70", dataPath=file.path(dataPath, "vantVeer_70")) # myDataset<-loadData(myDataset) data('vV70genesDataset') # assessment with RFE and SVM myExpe <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) # Another assessment where the subsets are computed automatically anotherExpe <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2) getFeatureSelectionOptions(anotherExpe, topic='maxSubsetSize') getFeatureSelectionOptions(anotherExpe, topic='subsetsSizes') # assessment with NSC expeWithNSC <- new("assessment",dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="nsc", featureSelectionMethod='nsc', typeFoldCreation="original", svmKernel="linear", noOfRepeat=2) getFeatureSelectionOptions(expeWithNSC, topic='thresholds')
This method classify one or several new samples provided in the file 'newSamplesFile' using the final classifier build by 'findFinalClassifier'.
object |
|
newSamplesFile |
|
optionValue |
|
This method is only applicable on objects of class assessment.
data('vV70genesDataset') expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,4,8,16,32,64,70))) # Build the final classifier expeOfInterest <- findFinalClassifier(expeOfInterest) ## Not run: classifyNewSamples(expeOfInterest, "pathToFile/testSamples_geneExpr.txt", 4) ## End(Not run) expeOfInterest <- runOneLayerExtCV(expeOfInterest) ## Not run: classifyNewSamples(expeOfInterest, "pathToFile/testSamples_geneExpr.txt") ## End(Not run)data('vV70genesDataset') expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,4,8,16,32,64,70))) # Build the final classifier expeOfInterest <- findFinalClassifier(expeOfInterest) ## Not run: classifyNewSamples(expeOfInterest, "pathToFile/testSamples_geneExpr.txt", 4) ## End(Not run) expeOfInterest <- runOneLayerExtCV(expeOfInterest) ## Not run: classifyNewSamples(expeOfInterest, "pathToFile/testSamples_geneExpr.txt") ## End(Not run)
This virtual class has two descendants: geneSubsets and thresholds. As a virtual class, you can't create an object of class featureSelectionOptions.
optionValues:numeric (vector). Value of the possible options
noOfOptions:numeric. Total number of options
getOptionValues(featureSelectionOptions)Retreive the value of options (slot optionValues)
getNoOfOptions(featureSelectionOptions)Retreive the number of options (slot featureSelectionOptions)
Camille Maumet
This class stores the properties of the final classifiers associated to a given assessment. A classifier is usually available for each option value defined in the slot featureSelectionOptions. This final classifier is obtained by running the feature selction method on the whole dataset to find the relevant genes and then train the classifier on the whole data considering only the relevant genes.
To generate the final classifier, call the method 'findFinalClassifier'
on an object of class assessment (findFinalClassifier-methods).
genesFromBestToWorst:character. If the feature selection
method is RFE: the genes ordered by the weights obtained with the smallest
subset size during RFE. If the method of featuure selection is the Nearest
Shrunken Centroid, this slot is empty.
models:list of object of class svm.If the feature selection
method is RFE: svm models trained on the whole dataset for each size of subset
(2 attributes: 'model', the classifier model and
'modelFeatures' the features selected for each subset).
If the feature selection method is NSC: the object created by pamr.train
on the whole dataset.
getGenesFromBestToWorst(finalClassifier)Retreive the genes ordered by their weights obtained with the smallest subset during RFE (slot genesFromBestToWorst)
getModels(finalClassifier)Retreive the svm models for each size of subset (slot models)
Camille Maumet
finalClassifier,assessment, getFinalClassifier-methods
#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data") #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath) #aDataset <- loadData(aDataset) data('vV70genesDataset') mySubsets <- new("geneSubsets", optionValues=c(1,2,4,8,16,32,64,70)) expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=mySubsets) expeOfInterest <- findFinalClassifier(expeOfInterest) # Return the whole object of class finalClassifier finalClassifier <- getFinalClassifier(expeOfInterest) # Svm model corresponding to a subset of size 4 (3rd size of subset) getModels(finalClassifier)[[3]]$model # Relevant genes for a subset of size 4 (3rd size of subset) getModels(finalClassifier)[[3]]$modelFeatures # Genes ordered according to their weight after performing the RFE up to 1 gene getGenesFromBestToWorst(finalClassifier)#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data") #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath) #aDataset <- loadData(aDataset) data('vV70genesDataset') mySubsets <- new("geneSubsets", optionValues=c(1,2,4,8,16,32,64,70)) expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=mySubsets) expeOfInterest <- findFinalClassifier(expeOfInterest) # Return the whole object of class finalClassifier finalClassifier <- getFinalClassifier(expeOfInterest) # Svm model corresponding to a subset of size 4 (3rd size of subset) getModels(finalClassifier)[[3]]$model # Relevant genes for a subset of size 4 (3rd size of subset) getModels(finalClassifier)[[3]]$modelFeatures # Genes ordered according to their weight after performing the RFE up to 1 gene getGenesFromBestToWorst(finalClassifier)
This method generates and stores the final classifier corresponding
to an assessment. This classifier can then be used to classify new samples by
calling classifyNewSamples. The final classifier is build according to the
classifier selected for a given assessment, applied on the whole data considering
only the genes selected by the feature selction method selected.
The methods returns an object of class assessment which finalClassifier has been build.
This method is only applicable on objects of class assessment.
#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data") #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath) #aDataset <- loadData(aDataset) data('vV70genesDataset') # With the RFE-SVM as feature selection method expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,4,8,16,32,64,70))) # Build the final classifier expeOfInterest <- findFinalClassifier(expeOfInterest) # With the NSC as feature selection method expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, featureSelectionMethod="nsc", classifierName="nsc", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("thresholds")) # Build the final classifier expeOfInterest <- findFinalClassifier(expeOfInterest)#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data") #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath) #aDataset <- loadData(aDataset) data('vV70genesDataset') # With the RFE-SVM as feature selection method expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,4,8,16,32,64,70))) # Build the final classifier expeOfInterest <- findFinalClassifier(expeOfInterest) # With the NSC as feature selection method expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, featureSelectionMethod="nsc", classifierName="nsc", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("thresholds")) # Build the final classifier expeOfInterest <- findFinalClassifier(expeOfInterest)
Forward gene selection is usually a computationally expensive task. To reduce the computation expense one may want to do not consider one gene at a time but chunks of genes. This class store the sizes of gene susbets to be tested during forward gene selection.
new("geneSubsets", optionValues)
Create a geneSubsets, the sizes of the different subsets are determined by
optionValues. The size of the biggest subset maxSubsetSize and
the number of subsets to be tried noOfOptions are automatically deducted.
The speed is set to high is there are less models than the size of the
biggest subset and 'slow' if not.
new("geneSubsets", maxSubsetSize, speed="high")
Create a geneSubsets, with a biggest subset of size maxSubsetSize. If
the speed is high the sizes of the subsets are increased by a power of 2
from 1 to the biggest power of 2 smaller than maxSubsetSize. If the
speed is slow the sizes of the subsets are increased by 1
from 1 to the maxSubsetSize.
maxSubsetSize:numeric. Size of the biggest subset
optionValues:numeric (vector). Sizes of the subsets in acsending order
noOfOptions:numeric. Total number of subsets
to be tried during backward gene selection
speed:character. Speed of the backward feature selection.
high if the number of models is smaller than the size of the biggest subset,
slow if not.
getMaxSubsetSize(geneSubsets), getMaxSubsetSize(geneSubsets)<-Retreive and modify the size of the biggest subset (slot maxSubsetSize)
getOptionValues(geneSubsets), getOptionValues(geneSubsets)<-Retreive and modify the sizes of the subsets of features (slot optionValues)
getNoOfOptions(geneSubsets)Retreive the total number of subsets to be tried during backward gene selection (slot noModels)
getSpeed(geneSubsets), getSpeed(geneSubsets)<-Retreive and modify the speed of the backward feature selection. (slot speed)
Camille Maumet
geneSubset235 <- new("geneSubsets", optionValues=c(2,3,5)) geneSubset235 getSubsetsSizes(geneSubset235) getSpeed(geneSubset235) getMaxSubsetSize(geneSubset235) geneSubsetMax60 <- new("geneSubsets", maxSubsetSize=60, speed="slow") geneSubsetMax60 geneSubsetSlow <- new("geneSubsets", maxSubsetSize=70, speed="slow") geneSubsetSlow getMaxSubsetSize(geneSubsetMax60) <- 70 geneSubsetMax60 newSizes <- c(1,2,3,4,5) getSubsetsSizes(geneSubsetMax60) <- newSizes geneSubsetMax60 getSpeed(geneSubset235) <- 'slow' geneSubset235geneSubset235 <- new("geneSubsets", optionValues=c(2,3,5)) geneSubset235 getSubsetsSizes(geneSubset235) getSpeed(geneSubset235) getMaxSubsetSize(geneSubset235) geneSubsetMax60 <- new("geneSubsets", maxSubsetSize=60, speed="slow") geneSubsetMax60 geneSubsetSlow <- new("geneSubsets", maxSubsetSize=70, speed="slow") geneSubsetSlow getMaxSubsetSize(geneSubsetMax60) <- 70 geneSubsetMax60 newSizes <- c(1,2,3,4,5) getSubsetsSizes(geneSubsetMax60) <- newSizes geneSubsetMax60 getSpeed(geneSubset235) <- 'slow' geneSubset235
This method provides an easy interface to access the attributes of a dataset
directly from an object assessment. The argument topic specifies which part
of the dataset is of interest.
object |
|
topic |
|
The value returned by the method changes accordingly to the "topic" argument.
If "topic" is missing
object of class dataset the dataset corresponding to the assessment of interest
If "topic" is "dataId"
object of class character corresponding to the dataId of the dataset
If "topic" is "dataPath"
object of class character corresponding to the dataPath of the dataset
If "topic" is "geneExprFile"
object of class character corresponding to the geneExprFile of the dataset
If "topic" is "classesFile"
object of class character corresponding to the classesFile of the dataset
If "topic" is "eset"
object of class ExpressionSetOrNull corresponding to the eset of the dataset
The method is only applicable on objects of class assessment.
Camille Maumet
#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data") #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath) #aDataset <- loadData(aDataset) data('vV70genesDataset') expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) getDataset(expeOfInterest)#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data") #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath) #aDataset <- loadData(aDataset) data('vV70genesDataset') expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) getDataset(expeOfInterest)
This method provides an easy interface to access the attributes of the object of class
featureSelectionOptions related to a particular assessment, directly from this object assessment.
The argument topic specifies which part of the featureSelectionOptions is of interest.
object |
|
topic |
if the |
The value returned by the method changes accordingly to the 'topic' argument.
If topic is missing
object of class featureSelectionOptions the featureSelectionOptions corresponding to the assessment of interest
If topic is "optionValues"
numeric corresponding to the optionValues of the featureSelectionOptions
If topic is "noOfOptions"
numeric corresponding to the noOfOptions of the featureSelectionOptions
If object is of class geneSubsets and topic is "maxSubsetSize"
numeric corresponding to the maxSubsetSize of the geneSubsets
If object is of class geneSubsets and topic is "subsetsSizes"
numeric corresponding to the optionValues of the geneSubsets
If object is of class geneSubsets and topic is "noModels"
numeric corresponding to the noOfOptions of the geneSubsets
If object is of class geneSubsets and topic is "speed"
numeric corresponding to the speed of the geneSubsets
If object is of class thresholds and topic is "thresholds"
numeric corresponding to the optionValues of the object of class thresholds
If object is of class thresholds and topic is "noThresholds"
numeric corresponding to the noOfOptions of the object of class thresholds
The method is only applicable on objects of class assessment.
Camille Maumet
featureSelectionOptions, assessment
# With an assessment using RFE #dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data") #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath) #aDataset <- loadData(aDataset) data('vV70genesDataset') mySubsets <- new("geneSubsets", optionValues=c(1,2,3,4,5,6)) myExpe <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=mySubsets) # Return the whole object 'featureSelectionOptions' (an object of class geneSusbsets) getFeatureSelectionOptions(myExpe) # Size of the biggest subset getFeatureSelectionOptions(myExpe, topic='maxSubsetSize') # All sizes of subsets getFeatureSelectionOptions(myExpe, topic='subsetsSizes') # Speed getFeatureSelectionOptions(myExpe, topic='speed') # Number of subsets getFeatureSelectionOptions(myExpe, topic='noModels') == getNoModels(mySubsets) # With an assessment using NSC as a feature selection method myThresholds <- new("thresholds", optionValues=c(0.1,0.2,0.3)) myExpe2 <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="nsc", featureSelectionMethod='nsc', typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=myThresholds) # Return the whole object 'featureSelectionOptions' (an object of class geneSusbsets) getFeatureSelectionOptions(myExpe2) # vector of thresholds getFeatureSelectionOptions(myExpe2, topic='thresholds') # Number of thresholds getFeatureSelectionOptions(myExpe2, topic='noThresholds')# With an assessment using RFE #dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data") #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath) #aDataset <- loadData(aDataset) data('vV70genesDataset') mySubsets <- new("geneSubsets", optionValues=c(1,2,3,4,5,6)) myExpe <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=mySubsets) # Return the whole object 'featureSelectionOptions' (an object of class geneSusbsets) getFeatureSelectionOptions(myExpe) # Size of the biggest subset getFeatureSelectionOptions(myExpe, topic='maxSubsetSize') # All sizes of subsets getFeatureSelectionOptions(myExpe, topic='subsetsSizes') # Speed getFeatureSelectionOptions(myExpe, topic='speed') # Number of subsets getFeatureSelectionOptions(myExpe, topic='noModels') == getNoModels(mySubsets) # With an assessment using NSC as a feature selection method myThresholds <- new("thresholds", optionValues=c(0.1,0.2,0.3)) myExpe2 <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="nsc", featureSelectionMethod='nsc', typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=myThresholds) # Return the whole object 'featureSelectionOptions' (an object of class geneSusbsets) getFeatureSelectionOptions(myExpe2) # vector of thresholds getFeatureSelectionOptions(myExpe2, topic='thresholds') # Number of thresholds getFeatureSelectionOptions(myExpe2, topic='noThresholds')
This method provides an easy interface to access the attributes of the object of class
finalClassifier related to a particular assessment, directly from this object assessment.
The argument topic specifies which part of the finalClassifier is of interest.
object |
|
topic |
|
The value returned by the method changes accordingly to the topic argument.
If topic is missing
object of class finalClassifier the finalClassifier corresponding to the assessment of interest
If topic is "genesFromBestToWorst"
numeric corresponding to the genesFromBestToWorst of the finalClassifier
If topic is "models"
numeric corresponding to the models of the finalClassifier
The method is only applicable on objects of class assessment.
Camille Maumet
#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data") #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath) #aDataset <- loadData(aDataset) mySubsets <- new("geneSubsets", optionValues=c(1,2,3,4,5,6)) data('vV70genesDataset') # assessment with RFE and SVM expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=mySubsets) expeOfInterest <- findFinalClassifier(expeOfInterest) # Return the whole object of class finalClassifier getFinalClassifier(expeOfInterest) getFinalClassifier(expeOfInterest, 'genesFromBestToWorst') getFinalClassifier(expeOfInterest, 'models') # assessment with NSC expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, featureSelectionMethod='nsc', classifierName="nsc", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("thresholds")) expeOfInterest <- findFinalClassifier(expeOfInterest) # Return the whole object of class finalClassifier getFinalClassifier(expeOfInterest) getFinalClassifier(expeOfInterest, 'genesFromBestToWorst') getFinalClassifier(expeOfInterest, 'models')#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data") #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath) #aDataset <- loadData(aDataset) mySubsets <- new("geneSubsets", optionValues=c(1,2,3,4,5,6)) data('vV70genesDataset') # assessment with RFE and SVM expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=mySubsets) expeOfInterest <- findFinalClassifier(expeOfInterest) # Return the whole object of class finalClassifier getFinalClassifier(expeOfInterest) getFinalClassifier(expeOfInterest, 'genesFromBestToWorst') getFinalClassifier(expeOfInterest, 'models') # assessment with NSC expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, featureSelectionMethod='nsc', classifierName="nsc", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("thresholds")) expeOfInterest <- findFinalClassifier(expeOfInterest) # Return the whole object of class finalClassifier getFinalClassifier(expeOfInterest) getFinalClassifier(expeOfInterest, 'genesFromBestToWorst') getFinalClassifier(expeOfInterest, 'models')
This method provides an easy interface to access the results of one-layer and two-layers of cross-validation directly from an object assessment.
object |
|
layer |
|
topic |
character. Argument that specifies which kind of
result is requested, the possible values are
|
errorType |
character. Optional, ignored if topic is not |
genesType |
character. Optional, ignored if topic is not |
if there is no error, the value returned by the method depends on the arguments namely,
layer, topic, errorType and genesType.
If layer is 1
General |
Get the results of the repeated one-layer cross-validation corresponding to
the |
if topic is "errorRate" |
|
If errorType="all" or is missing |
All the following error rates |
If errorType="cv" |
|
If errorType="se" |
|
If errorType="class" |
numeric. Class cross-validated error rate error for each value of option tried obtained by one-layer of cross-validation (1 value per class and value of option). |
Else |
Error signaling that the topic is not appropriate. |
if topic is "genesSelected" |
|
If genesType="freq" or is missing |
|
Else |
Error signaling that the topic is not appropriate. |
if topic is "bestOptionValue" |
Size of subset (for RFE-SVM) or threshold (for NSC) corresponding to the minimum cross-validated error rate. |
if topic is "executionTime" |
Time in second to perform this one-layer cross-validation. |
If layer is c(1,i)
General |
Get the results of the ith repeat of the one-layer cross-validation corresponding to
the |
if topic is "errorRate" |
|
If errorType="all" or is missing |
All the following error rates |
If errorType="cv" |
numeric. Cross-validated error-rate for each value of option tried obtained by one-layer of cross-validation on the ith repeat(1 value per subset). |
If errorType="se" |
numeric. Standard error on cross-validated error-rate for each value of option tried obtained by one-layer of cross-validation on the ith repeat (1 value per value of option). |
If errorType="class" |
numeric. Class cross-validated error rate error for each value of option tried obtained by one-layer of cross-validation on the ith repeat (1 value per class and value of option). |
If errorType="fold" |
numeric. Class cross-validated error rate error for each fold and each value of option tried obtained by one-layer of cross-validation on the ith repeat (1 value per class and value of option). |
Else |
Error signaling that the topic is not appropriate. |
if topic is "genesSelected" |
|
If genesType="freq" or is missing |
list. Each elelement of the list corresponds to the genes selected for each model ordered by frequency. |
If genesType="fold" |
list. Each elelement of the list corresponds to a model and contains a list of which one element correspond to the genes selected in a particular fold. |
Else |
Error signaling that the topic is not appropriate. |
if topic is "bestOptionValue" |
numeric. Size of subset (for RFE) or threshold (for NSC) corresponding to the minimum cross-validated error rate in the ith repeat of the one-layer cross-validation. |
if topic is "executionTime" |
Time in second to perform this repeat of one-layer cross-validation. |
If layer is 2
General |
Get the results of the repeated two-layers cross-validation corresponding to
the |
if topic is 'errorRate' |
|
If errorType="all" or is missing |
All the following error rates |
If errorType="cv" |
numeric. Cross-validated error-rate obtained by two-layers of cross-validation (1 value). |
If errorType="se" |
numeric. Standard error on cross-validated error-rate obtained by two-layers of cross-validation (1 value). |
If errorType="class" |
numeric. Class cross-validated error rate obtained by two-layers (1 value per class) |
Else |
Error signaling that the topic is not appropriate. |
if topic is "bestOptionValue" |
numeric. Average best number of genes for SVM-RFE of threshold for NSc obtained among the folds. |
if topic is "executionTime" |
Time in second to perform this two-layers cross-validation. |
If layer is c(2,i)
General |
Get the results of the ith repeated of the two-layers cross-validation corresponding to
the |
if topic is 'errorRate' |
|
If errorType="all" or is missing |
All the following error rates |
If errorType="cv" |
numeric. Cross-validated error-rate obtained by two-layers of cross-validation in this repeat. (1 value). |
If errorType="se" |
numeric. Standard error on cross-validated error-rate obtained by two-layers of cross-validation in this repeat (1 value). |
If errorType="class" |
numeric. Class cross-validated error rate obtained by two-layers in this repeat |
If errorType="fold" |
numeric. Error rate obtained on each of the folds in the second layer in this repeat(1 value per fold). of cross-validation (value per class). |
Else |
Error signaling that the topic is not appropriate. |
if topic is "genesSelected" |
|
If genesType="fold" or is missing |
list. Each elelement of the list corresponds to a fold and contains a list of the genes selected in this particular fold. |
Else |
Error signaling that the topic is not appropriate. |
if topic is "bestOptionValue" |
numeric. Average best number of genes obtained among the folds in this repeat. |
if topic is "executionTime" |
Time in second to perform this repeat of two-layers cross-validation. |
If layer is c(2, i, j)
|
This layer corresponds to the jth inner layer of one-layer cross-validation performed inside the ith repeat of the two-layers cross-validation. The returned values are similar to the one returned by a repeated one-layer cross-validation. |
If layer is c(2, i, j, k)
|
This layer corresponds to the kth repeat of the jth inner layer of one-layer cross-validation performed inside the ith repeat. The returned values are similar to the one returned by a repeat of one-layer cross-validation. |
The method is only applicable on objects of class assessment.
Camille Maumet
#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data") #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath) #aDataset <- loadData(aDataset) data('vV70genesDataset') mySubsets <- new("geneSubsets", optionValues=c(1,2,4,8,16,32,64,70)) myassessment <- new("assessment", dataset=vV70genes, noFolds1stLayer=5, noFolds2ndLayer=4, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=mySubsets) myassessment <- runOneLayerExtCV(myassessment) myassessment <- runTwoLayerExtCV(myassessment) # --- Access to one-layer CV --- # errorRate # 1-layer CV: error Rates getResults(myassessment, 1, 'errorRate') # 1-layer CV: error Rates - all") getResults(myassessment, 1, 'errorRate', errorType='all') # 1-layer CV: error Rates - cv getResults(myassessment, 1, 'errorRate', errorType='cv') # 1-layer CV: error Rates - se getResults(myassessment, 1, 'errorRate', errorType='se') # 1-layer CV: error Rates - class getResults(myassessment, 1, 'errorRate', errorType='class') # genesSelected # 1-layer CV: genes Selected getResults(myassessment, 1, 'genesSelected') # 1-layer CV: genes Selected - frequ getResults(myassessment, 1, 'genesSelected', genesType='frequ') # 1-layer CV: genes Selected - model 7 getResults(myassessment, 1, 'genesSelected', genesType='frequ')[[7]] getResults(myassessment, 1, 'genesSelected')[[7]] # bestOptionValue # 1-layer CV: best number of genes getResults(myassessment, 1, 'bestOptionValue') # executionTime # 1-layer CV: execution time getResults(myassessment, 1, 'executionTime') # --- Access to 2nd repeat of one-layer CV --- # Error rates # 1-layer CV repeat 2: error Rates getResults(myassessment, c(1,2), 'errorRate') # 1-layer CV repeat 2: error Rates - all getResults(myassessment, c(1,2), 'errorRate', errorType='all') # 1-layer CV repeat 2: error Rates - cv getResults(myassessment, c(1,2), 'errorRate', errorType='cv') # 1-layer CV repeat 2: error Rates - se getResults(myassessment, c(1,2), 'errorRate', errorType='se') # 1-layer CV repeat 2: error Rates - fold getResults(myassessment, c(1,2), 'errorRate', errorType='fold') # 1-layer CV repeat 2: error Rates - noSamplesPerFold getResults(myassessment, c(1,2), 'errorRate', errorType='noSamplesPerFold') # 1-layer CV repeat 2: error Rates - class getResults(myassessment, c(1,2), 'errorRate', errorType='class') # genesSelected # 1-layer CV repeat 2: genes Selected getResults(myassessment, c(1,2), 'genesSelected') # 1-layer CV repeat 2: genes Selected - frequ getResults(myassessment, c(1,2), 'genesSelected', genesType='frequ') # 1-layer CV repeat 2: genes Selected - model 7 (twice) getResults(myassessment, c(1,2), 'genesSelected', genesType='frequ')[[7]] getResults(myassessment, c(1,2), 'genesSelected')[[7]] # 1-layer CV repeat 2: genes Selected - fold getResults(myassessment, c(1,2), 'genesSelected', genesType='fold') # 1-layer CV repeat 2: best number of genes getResults(myassessment, c(1,2), 'bestOptionValue') # 1-layer CV repeat 2: execution time getResults(myassessment, c(1,2), 'executionTime') # --- Access to two-layers CV --- # Error rates # 2-layer CV: error Rates getResults(myassessment, 2, 'errorRate') # 2-layer CV: error Rates - all getResults(myassessment, 2, 'errorRate', errorType='all') # 2-layer CV: error Rates - cv getResults(myassessment, 2, 'errorRate', errorType='cv') # 2-layer CV: error Rates - se getResults(myassessment, 2, 'errorRate', errorType='se') # 2-layer CV: error Rates - class getResults(myassessment, 2, 'errorRate', errorType='class') # bestOptionValue # 2-layer CV: best number of genes (avg) getResults(myassessment, 2, 'bestOptionValue') # executionTime # 2-layer CV: execution time getResults(myassessment, 2, 'executionTime') # --- Access to two-layers CV access to repeats --- # Error rates # 2-layer CV repeat 1: error Rates getResults(myassessment, c(2,1), 'errorRate') # 2-layer CV repeat 1: error Rates - all getResults(myassessment, c(2,1), 'errorRate', errorType='all') # 2-layer CV repeat 1: error Rates - cv getResults(myassessment, c(2,1), 'errorRate', errorType='cv') # 2-layer CV repeat 1: error Rates - se getResults(myassessment, c(2,1), 'errorRate', errorType='se') # 2-layer CV repeat 1: error Rates - fold getResults(myassessment, c(2,1), 'errorRate', errorType='fold') # 2-layer CV repeat 1: error Rates - noSamplesPerFold getResults(myassessment, c(2,1), 'errorRate', errorType='noSamplesPerFold') # 2-layer CV repeat 1: error Rates - class getResults(myassessment, c(2,1), 'errorRate', errorType='class') # genesSelected # 2-layer CV repeat 1: genes Selected getResults(myassessment, c(2,1), 'genesSelected') # 2-layer CV repeat 1: genes Selected - fold getResults(myassessment, c(2,1), 'genesSelected', genesType='fold') # 2-layer CV repeat 1: best number of genes getResults(myassessment, c(2,1), 'bestOptionValue') # 2-layer CV repeat 1: execution time getResults(myassessment, c(2,1), 'executionTime') # --- Access to one-layer CV inside two-layers CV --- # errorRate # 2-layer CV repeat 1 inner layer 3: error Rates getResults(myassessment, c(2,1,3), 'errorRate') # 2-layer CV repeat 1 inner layer 3: error Rates - all getResults(myassessment, c(2,1,3), 'errorRate', errorType='all') # 2-layer CV repeat 1 inner layer 3: error Rates - cv getResults(myassessment, c(2,1,3), 'errorRate', errorType='cv') # 2-layer CV repeat 1 inner layer 3: error Rates - se getResults(myassessment, c(2,1,3), 'errorRate', errorType='se') # 2-layer CV repeat 1 inner layer 3: error Rates - class getResults(myassessment, c(2,1,3), 'errorRate', errorType='class') # genesSelected # 2-layer CV repeat 1 inner layer 3: genes Selected getResults(myassessment, c(2,1,3), 'genesSelected') # 2-layer CV repeat 1 inner layer 3: genes Selected - frequ getResults(myassessment, c(2,1,3), 'genesSelected', genesType='frequ') # 2-layer CV repeat 1 inner layer 3: genes Selected - model 7 getResults(myassessment, c(2,1,3), 'genesSelected', genesType='frequ')[[7]] getResults(myassessment, c(2,1,3), 'genesSelected')[[7]] # bestOptionValue # 2-layer CV repeat 1 inner layer 3: best number of genes getResults(myassessment, c(2,1,3), 'bestOptionValue') # executionTime # 2-layer CV repeat 1 inner layer 3: execution time getResults(myassessment, c(2,1,3), 'executionTime') # --- two-layers CV access to repeat 1, inner layer 2 repeat 2 --- # Error rates # 2-layer CV inner layer 3 repeat 2: error Rates getResults(myassessment, c(2,1,3,1), 'errorRate') # 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - all getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='all') # 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - cv getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='cv') # 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - se getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='se') # 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - class getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='class') # 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - fold getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='fold') # 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - noSamplesPerFold getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='noSamplesPerFold') # genesSelected # 2-layer CV repeat 1 inner layer 3 repeat 1: genes Selected getResults(myassessment, c(2,1,3,1), 'genesSelected') # 2-layer CV repeat 1 inner layer 3 repeat 1: genes Selected - fold getResults(myassessment, c(2,1,3,1), 'genesSelected', genesType='fold') # 2-layer CV repeat 1 inner layer 3 repeat 1: genes Selected - model 3 fold 1(twice) getResults(myassessment, c(2,1,3,1), 'genesSelected', genesType='fold')[[3]][[1]] # 2-layer CV repeat 1 inner layer 3 repeat 1: genes Selected frequ - model 3 getResults(myassessment, c(2,1,3,1), 'genesSelected')[[3]] # 2-layer CV repeat 1 inner layer 3 repeat 1: best number of genes getResults(myassessment, c(2,1,3,1), 'bestOptionValue') # 2-layer CV repeat 1 inner layer 3 repeat 1: execution time getResults(myassessment, c(2,1,3,1), 'executionTime')#dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data") #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath) #aDataset <- loadData(aDataset) data('vV70genesDataset') mySubsets <- new("geneSubsets", optionValues=c(1,2,4,8,16,32,64,70)) myassessment <- new("assessment", dataset=vV70genes, noFolds1stLayer=5, noFolds2ndLayer=4, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=mySubsets) myassessment <- runOneLayerExtCV(myassessment) myassessment <- runTwoLayerExtCV(myassessment) # --- Access to one-layer CV --- # errorRate # 1-layer CV: error Rates getResults(myassessment, 1, 'errorRate') # 1-layer CV: error Rates - all") getResults(myassessment, 1, 'errorRate', errorType='all') # 1-layer CV: error Rates - cv getResults(myassessment, 1, 'errorRate', errorType='cv') # 1-layer CV: error Rates - se getResults(myassessment, 1, 'errorRate', errorType='se') # 1-layer CV: error Rates - class getResults(myassessment, 1, 'errorRate', errorType='class') # genesSelected # 1-layer CV: genes Selected getResults(myassessment, 1, 'genesSelected') # 1-layer CV: genes Selected - frequ getResults(myassessment, 1, 'genesSelected', genesType='frequ') # 1-layer CV: genes Selected - model 7 getResults(myassessment, 1, 'genesSelected', genesType='frequ')[[7]] getResults(myassessment, 1, 'genesSelected')[[7]] # bestOptionValue # 1-layer CV: best number of genes getResults(myassessment, 1, 'bestOptionValue') # executionTime # 1-layer CV: execution time getResults(myassessment, 1, 'executionTime') # --- Access to 2nd repeat of one-layer CV --- # Error rates # 1-layer CV repeat 2: error Rates getResults(myassessment, c(1,2), 'errorRate') # 1-layer CV repeat 2: error Rates - all getResults(myassessment, c(1,2), 'errorRate', errorType='all') # 1-layer CV repeat 2: error Rates - cv getResults(myassessment, c(1,2), 'errorRate', errorType='cv') # 1-layer CV repeat 2: error Rates - se getResults(myassessment, c(1,2), 'errorRate', errorType='se') # 1-layer CV repeat 2: error Rates - fold getResults(myassessment, c(1,2), 'errorRate', errorType='fold') # 1-layer CV repeat 2: error Rates - noSamplesPerFold getResults(myassessment, c(1,2), 'errorRate', errorType='noSamplesPerFold') # 1-layer CV repeat 2: error Rates - class getResults(myassessment, c(1,2), 'errorRate', errorType='class') # genesSelected # 1-layer CV repeat 2: genes Selected getResults(myassessment, c(1,2), 'genesSelected') # 1-layer CV repeat 2: genes Selected - frequ getResults(myassessment, c(1,2), 'genesSelected', genesType='frequ') # 1-layer CV repeat 2: genes Selected - model 7 (twice) getResults(myassessment, c(1,2), 'genesSelected', genesType='frequ')[[7]] getResults(myassessment, c(1,2), 'genesSelected')[[7]] # 1-layer CV repeat 2: genes Selected - fold getResults(myassessment, c(1,2), 'genesSelected', genesType='fold') # 1-layer CV repeat 2: best number of genes getResults(myassessment, c(1,2), 'bestOptionValue') # 1-layer CV repeat 2: execution time getResults(myassessment, c(1,2), 'executionTime') # --- Access to two-layers CV --- # Error rates # 2-layer CV: error Rates getResults(myassessment, 2, 'errorRate') # 2-layer CV: error Rates - all getResults(myassessment, 2, 'errorRate', errorType='all') # 2-layer CV: error Rates - cv getResults(myassessment, 2, 'errorRate', errorType='cv') # 2-layer CV: error Rates - se getResults(myassessment, 2, 'errorRate', errorType='se') # 2-layer CV: error Rates - class getResults(myassessment, 2, 'errorRate', errorType='class') # bestOptionValue # 2-layer CV: best number of genes (avg) getResults(myassessment, 2, 'bestOptionValue') # executionTime # 2-layer CV: execution time getResults(myassessment, 2, 'executionTime') # --- Access to two-layers CV access to repeats --- # Error rates # 2-layer CV repeat 1: error Rates getResults(myassessment, c(2,1), 'errorRate') # 2-layer CV repeat 1: error Rates - all getResults(myassessment, c(2,1), 'errorRate', errorType='all') # 2-layer CV repeat 1: error Rates - cv getResults(myassessment, c(2,1), 'errorRate', errorType='cv') # 2-layer CV repeat 1: error Rates - se getResults(myassessment, c(2,1), 'errorRate', errorType='se') # 2-layer CV repeat 1: error Rates - fold getResults(myassessment, c(2,1), 'errorRate', errorType='fold') # 2-layer CV repeat 1: error Rates - noSamplesPerFold getResults(myassessment, c(2,1), 'errorRate', errorType='noSamplesPerFold') # 2-layer CV repeat 1: error Rates - class getResults(myassessment, c(2,1), 'errorRate', errorType='class') # genesSelected # 2-layer CV repeat 1: genes Selected getResults(myassessment, c(2,1), 'genesSelected') # 2-layer CV repeat 1: genes Selected - fold getResults(myassessment, c(2,1), 'genesSelected', genesType='fold') # 2-layer CV repeat 1: best number of genes getResults(myassessment, c(2,1), 'bestOptionValue') # 2-layer CV repeat 1: execution time getResults(myassessment, c(2,1), 'executionTime') # --- Access to one-layer CV inside two-layers CV --- # errorRate # 2-layer CV repeat 1 inner layer 3: error Rates getResults(myassessment, c(2,1,3), 'errorRate') # 2-layer CV repeat 1 inner layer 3: error Rates - all getResults(myassessment, c(2,1,3), 'errorRate', errorType='all') # 2-layer CV repeat 1 inner layer 3: error Rates - cv getResults(myassessment, c(2,1,3), 'errorRate', errorType='cv') # 2-layer CV repeat 1 inner layer 3: error Rates - se getResults(myassessment, c(2,1,3), 'errorRate', errorType='se') # 2-layer CV repeat 1 inner layer 3: error Rates - class getResults(myassessment, c(2,1,3), 'errorRate', errorType='class') # genesSelected # 2-layer CV repeat 1 inner layer 3: genes Selected getResults(myassessment, c(2,1,3), 'genesSelected') # 2-layer CV repeat 1 inner layer 3: genes Selected - frequ getResults(myassessment, c(2,1,3), 'genesSelected', genesType='frequ') # 2-layer CV repeat 1 inner layer 3: genes Selected - model 7 getResults(myassessment, c(2,1,3), 'genesSelected', genesType='frequ')[[7]] getResults(myassessment, c(2,1,3), 'genesSelected')[[7]] # bestOptionValue # 2-layer CV repeat 1 inner layer 3: best number of genes getResults(myassessment, c(2,1,3), 'bestOptionValue') # executionTime # 2-layer CV repeat 1 inner layer 3: execution time getResults(myassessment, c(2,1,3), 'executionTime') # --- two-layers CV access to repeat 1, inner layer 2 repeat 2 --- # Error rates # 2-layer CV inner layer 3 repeat 2: error Rates getResults(myassessment, c(2,1,3,1), 'errorRate') # 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - all getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='all') # 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - cv getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='cv') # 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - se getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='se') # 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - class getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='class') # 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - fold getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='fold') # 2-layer CV repeat 1 inner layer 3 repeat 1: error Rates - noSamplesPerFold getResults(myassessment, c(2,1,3,1), 'errorRate', errorType='noSamplesPerFold') # genesSelected # 2-layer CV repeat 1 inner layer 3 repeat 1: genes Selected getResults(myassessment, c(2,1,3,1), 'genesSelected') # 2-layer CV repeat 1 inner layer 3 repeat 1: genes Selected - fold getResults(myassessment, c(2,1,3,1), 'genesSelected', genesType='fold') # 2-layer CV repeat 1 inner layer 3 repeat 1: genes Selected - model 3 fold 1(twice) getResults(myassessment, c(2,1,3,1), 'genesSelected', genesType='fold')[[3]][[1]] # 2-layer CV repeat 1 inner layer 3 repeat 1: genes Selected frequ - model 3 getResults(myassessment, c(2,1,3,1), 'genesSelected')[[3]] # 2-layer CV repeat 1 inner layer 3 repeat 1: best number of genes getResults(myassessment, c(2,1,3,1), 'bestOptionValue') # 2-layer CV repeat 1 inner layer 3 repeat 1: execution time getResults(myassessment, c(2,1,3,1), 'executionTime')
Initialize method for Rmagpie classes.
Camille Maumet
This method creates a plot that reprenset the error rate in each fold of each repeat of the second layer of cross-validation of the two-layer cross-validation of the assessment at stake. The plot represents the error rate versus the size of gene subsets (for SVM-RFE) or the threshold values (for NSC).
The method is only applicable on objects of class assessment.
plotErrorsSummaryOneLayerCV-methods, plotErrorsRepeatedOneLayerCV-methods
data('vV70genesDataset') expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=3, noFolds2ndLayer=2, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=10, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) expeOfInterest <- runTwoLayerExtCV(expeOfInterest) plotErrorsFoldTwoLayerCV(expeOfInterest)data('vV70genesDataset') expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=3, noFolds2ndLayer=2, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=10, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) expeOfInterest <- runTwoLayerExtCV(expeOfInterest) plotErrorsFoldTwoLayerCV(expeOfInterest)
This method creates a plot that represent the summary estimated error rate and the cross-validated error rate in each repeat of the one-layer cross-validation of the assessment at stake. The plot represents the summary estimate of the error rate (averaged over the repeats) and the cross-validated error rate obtained in each repeat versus the size of gene subsets (for SVM-RFE) or the threshold values (for NSC).
The method is only applicable on objects of class assessment.
plotErrorsFoldTwoLayerCV-methods, plotErrorsSummaryOneLayerCV-methods
data('vV70genesDataset') expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=3, noFolds2ndLayer=2, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=10, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) expeOfInterest <- runOneLayerExtCV(expeOfInterest) plotErrorsRepeatedOneLayerCV(expeOfInterest)data('vV70genesDataset') expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=3, noFolds2ndLayer=2, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=10, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) expeOfInterest <- runOneLayerExtCV(expeOfInterest) plotErrorsRepeatedOneLayerCV(expeOfInterest)
This method creates a plot that represent the summary estimated error rate of the one-layer cross-validation of the assessment at stake. The plot represents the summary estimate of the error rate (averaged over the repeats) versus the size of gene subsets (for SVM-RFE) or the threshold values (for NSC).
The method is only applicable on objects of class assessment.
plotErrorsFoldTwoLayerCV-methods, plotErrorsRepeatedOneLayerCV-methods
data('vV70genesDataset') expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=3, noFolds2ndLayer=2, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=10, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) expeOfInterest <- runOneLayerExtCV(expeOfInterest) plotErrorsSummaryOneLayerCV(expeOfInterest)data('vV70genesDataset') expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=3, noFolds2ndLayer=2, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=10, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) expeOfInterest <- runOneLayerExtCV(expeOfInterest) plotErrorsSummaryOneLayerCV(expeOfInterest)
Generate an image per value of option representing the features (on dot per feature). The color of the dot depends on the frequency of the feature in for the given value of option (number of genes or threshold).
object |
|
storagePath |
|
The method is only applicable on objects of class assessment.
## Not run: data('vV70genesDataset') expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=3, noFolds2ndLayer=2, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=10, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) expeOfInterest <- runOneLayerExtCV(expeOfInterest) rankedGenesImg(expeOfInterest, storagePath='myPath') ## End(Not run)## Not run: data('vV70genesDataset') expeOfInterest <- new("assessment", dataset=vV70genes, noFolds1stLayer=3, noFolds2ndLayer=2, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=10, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) expeOfInterest <- runOneLayerExtCV(expeOfInterest) rankedGenesImg(expeOfInterest, storagePath='myPath') ## End(Not run)
This method run an external one-layer cross-validation according to the options stored in an object of class assessment. The concept of external cross-validation has been introduced by G.J. McLachlan and C. Ambroise in 'Selection bias in gene extraction on the basis of microarray gene-expression data' (cf. section References). This technique of cross-validation is used to determine an unbiased estimate of the error rate when feature selection is involved.
object |
|
object of class assessment in which the one-layer external cross-validation
has been computed, therfore, the slot resultRepeated1LayerCV is no more NULL.
This methods print out the key results of the assessment, to access the full detail
of the results, the user must call the method getResults.
This method is only applicable on objects of class assessment.
C. Amboise and G.J. McLachlan 2002. selection bias in gene extraction on the basis of microarray gene-expression data. PNAS, 99(10):6562-6566
assessment, getResults, runTwoLayerExtCV-methods
data('vV70genesDataset') # assessment with RFE and SVM myExpe <- new("assessment", dataset=vV70genes, noFolds1stLayer=9, noFolds2ndLayer=10, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) myExpe <- runOneLayerExtCV(myExpe)data('vV70genesDataset') # assessment with RFE and SVM myExpe <- new("assessment", dataset=vV70genes, noFolds1stLayer=9, noFolds2ndLayer=10, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) myExpe <- runOneLayerExtCV(myExpe)
This method run an external two-layers cross-validation according to the options stored in an object of class assessment. The concept of two-layers cross-validation has been introduced by J.X. Zhu,G.J. McLachlan, L. Ben-Tovim Jonesa, I.A.Wood in 'On selection biases with prediction rules formed from gene expression data' and by I. A. Wood, P. M. Visscher, and K. L. Mengersen in 'Classification based upon gene expression data: bias and precision of error rates' (cf. section References). This technique of cross-validation is used to determine an unbiased estimate of the best error rate (using the best size of subset for RFE-SVM, of the best threshold for NSC) when feature selection is involved.
object |
|
object of class assessment in which the one-layer external cross-validation
has been computed, therfore, the slot resultRepeated2LayerCV is no more NULL.
This methods print out the key results of the assessment, to access the full detail
of the results, the user must call the method getResults.
This method is only applicable on objects of class assessment.
J.X. Zhu, G.J. McLachlan, L. Ben-Tovim, I.A. Wood (2008), "On selection biases with prediction rules formed from gene expression data", Journal of Statistical Planning and Inference, 38:374-386.
I.A. Wood, P.M. Visscher, and K.L. Mengersen "Classification based upon gene expression data: bias and precision of error rates" Bioinformatics, June 1, 2007; 23(11): 1363 - 1370.
assessment, getResults, runOneLayerExtCV-methods
data('vV70genesDataset') # assessment with RFE and SVM myExpe <- new("assessment", dataset=vV70genes, noFolds1stLayer=9, noFolds2ndLayer=10, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) myExpe <- runTwoLayerExtCV(myExpe)data('vV70genesDataset') # assessment with RFE and SVM myExpe <- new("assessment", dataset=vV70genes, noFolds1stLayer=9, noFolds2ndLayer=10, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) myExpe <- runTwoLayerExtCV(myExpe)
This method provides an easy interface to modify the attributes of a dataset directly from an object assessment. The argument topic specifies which part of the dataset should be modified. This method is only available none of the one-layer CV or two-layers CV have been performed and the final classifier has not been determined yet.
object |
class assessment. Object assessment of interest |
topic |
character. Optional argument that specifies which attribute of
the dataset must be changed, the possible values are
|
value |
The replacement value. |
The methods modifies the object of class assessment and returned the slot modified
accordingly to the request provided by topic.
If 'topic' is missing
object of class dataset the dataset corresponding to the assessment is replaced by 'value'.
If 'topic' is "dataId"
object of class character the 'dataId' of the dataset is replaced by 'value'
If 'topic' is "dataPath"
object of class character the 'dataPath' of the dataset is replaced by 'value'
If 'topic' is "geneExprFile"
object of class character the 'geneExprFile' of the dataset is replaced by 'value'
If 'topic' is "classesFile"
object of class character the 'classesFile' of the dataset is replaced by 'value'
This method is only applicable on objects of class assessment.
Camille Maumet
assessment, getDataset-methods
## Not run: aDataset <- new("dataset", dataId="vantVeer_70", dataPath="pathToFile") aDataset <- loadData(aDataset) expeOfInterest <- new("assessment", dataset=aDataset, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) # Modify the dataId getDataset(expeOfInterest, topic='dataId') <- "khan" getDataset(expeOfInterest, 'dataId') # Replace the dataset getDataset(expeOfInterest) <- aDataset getDataset(expeOfInterest, 'dataId') ## End(Not run)## Not run: aDataset <- new("dataset", dataId="vantVeer_70", dataPath="pathToFile") aDataset <- loadData(aDataset) expeOfInterest <- new("assessment", dataset=aDataset, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6))) # Modify the dataId getDataset(expeOfInterest, topic='dataId') <- "khan" getDataset(expeOfInterest, 'dataId') # Replace the dataset getDataset(expeOfInterest) <- aDataset getDataset(expeOfInterest, 'dataId') ## End(Not run)
This method provides an easy interface to modify the attributes of the object of class
featureSelectionOptions related to a particular assessment, directly from this object assessment.
The argument topic specifies which part of the featureSelectionOptions is of interest.
This method is only available none of the one-layer
CV or two-layers CV have been performed and the final classifier has not been determined yet.
object |
|
topic |
if the |
The methods modifies the object of class assessment and returned the slot modified
accordingly to the request provided by topic.
If topic is missing
object of class featureSelectionOptions featureSelectionOptions corresponding to the assessment is replaced by value.
If topic is "optionValues"
numeric Slot optionValues of the featureSelectionOptions is replaced by value.
If topic is "noOfOptions"
numeric Slot noOfOptions of the featureSelectionOptions is replaced by value.
If object is of class geneSubsets and topic is "maxSubsetSize"
numeric Slot maxSubsetSize of the geneSubsets is replaced by value.
If object is of class geneSubsets and topic is "subsetsSizes"
numeric Slot optionValues of the geneSubsets is replaced by value.
If object is of class geneSubsets and topic is "noModels"
numericSlot noOfOptions of the geneSubsets is replaced by value.
If object is of class geneSubsets and topic is "speed"
numeric Slot speed of the geneSubsets is replaced by value.
If object is of class thresholds and topic is "thresholds"
numeric Slot optionValues of the object of class thresholds is replaced by value.
If object is of class thresholds and topic is "noThresholds"
numeric Slot noOfOptions of the object of class thresholds is replaced by value.
The method is only applicable on objects of class assessment.
Camille Maumet
featureSelectionOptions, assessment
# With an assessment using RFE data('vV70genesDataset') mySubsets <- new("geneSubsets", optionValues=c(1,2,3,4,5,6)) myExpe <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=mySubsets) # Modify the size of the biggest subset getFeatureSelectionOptions(myExpe, topic='maxSubsetSize') <- 70 getFeatureSelectionOptions(myExpe, topic='maxSubsetSize') # Modify all the sizes of subsets getFeatureSelectionOptions(myExpe, topic='subsetsSizes') <- c(1,5,10,25,30) getFeatureSelectionOptions(myExpe, topic='subsetsSizes') # Modify the speed getFeatureSelectionOptions(myExpe, topic='speed') <- 'slow' getFeatureSelectionOptions(myExpe, topic='speed') # Modify the entire geneSubsets getFeatureSelectionOptions(myExpe) <- mySubsets getFeatureSelectionOptions(myExpe, topic='maxSubsetSize') getFeatureSelectionOptions(myExpe, topic='subsetsSizes') getFeatureSelectionOptions(myExpe, topic='speed') getFeatureSelectionOptions(myExpe, topic='noModels') # With an assessment using NSC as a feature selection method myThresholds <- new("thresholds", optionValues=c(0.1,0.2,0.3)) myExpe2 <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="nsc", featureSelectionMethod='nsc', typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=myThresholds) otherThresholds <- new("thresholds", optionValues=c(0,0.5,1,1.5,2,2.5,3)) # Modify the whole object 'featureSelectionOptions' (an object of class thresholds) getFeatureSelectionOptions(myExpe2) <- otherThresholds getFeatureSelectionOptions(myExpe2, topic='thresholds') getFeatureSelectionOptions(myExpe2, topic='noThresholds')# With an assessment using RFE data('vV70genesDataset') mySubsets <- new("geneSubsets", optionValues=c(1,2,3,4,5,6)) myExpe <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="svm", typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=mySubsets) # Modify the size of the biggest subset getFeatureSelectionOptions(myExpe, topic='maxSubsetSize') <- 70 getFeatureSelectionOptions(myExpe, topic='maxSubsetSize') # Modify all the sizes of subsets getFeatureSelectionOptions(myExpe, topic='subsetsSizes') <- c(1,5,10,25,30) getFeatureSelectionOptions(myExpe, topic='subsetsSizes') # Modify the speed getFeatureSelectionOptions(myExpe, topic='speed') <- 'slow' getFeatureSelectionOptions(myExpe, topic='speed') # Modify the entire geneSubsets getFeatureSelectionOptions(myExpe) <- mySubsets getFeatureSelectionOptions(myExpe, topic='maxSubsetSize') getFeatureSelectionOptions(myExpe, topic='subsetsSizes') getFeatureSelectionOptions(myExpe, topic='speed') getFeatureSelectionOptions(myExpe, topic='noModels') # With an assessment using NSC as a feature selection method myThresholds <- new("thresholds", optionValues=c(0.1,0.2,0.3)) myExpe2 <- new("assessment", dataset=vV70genes, noFolds1stLayer=10, noFolds2ndLayer=9, classifierName="nsc", featureSelectionMethod='nsc', typeFoldCreation="original", svmKernel="linear", noOfRepeat=2, featureSelectionOptions=myThresholds) otherThresholds <- new("thresholds", optionValues=c(0,0.5,1,1.5,2,2.5,3)) # Modify the whole object 'featureSelectionOptions' (an object of class thresholds) getFeatureSelectionOptions(myExpe2) <- otherThresholds getFeatureSelectionOptions(myExpe2, topic='thresholds') getFeatureSelectionOptions(myExpe2, topic='noThresholds')
Implementation of R method show to display object from package Rmagpie.
Camille Maumet
The Nearest Shrunken Centroid is computed using a threshold. This threshold is usually determined by finding the best threshold value over a set of values by finding the threshold leading to the best error rate assessed by cross-validation. This class stores the values of thresholds to be tried. If the user wants to use default values it's also possible.
new("thresholds")
Create an empty thresholds. The default thresholds values will be computed and this object updated as soon as it is linked in an assessment.
new("thresholds", optionValues)
Create a thresholds, containing the thresholds values defined by
optionValues. The slot noOfOptions is automatically updated.
optionValues:numeric Values of the thresholds, if
optionValues has length zero then the default thresholds values must be used.
noOfOptions:numeric Number of thresholds.
Class "featureSelectionOptions", directly.
getNoThresholds(thresholds)Retreive the number of the thresholds (slot noOfOptions)
getOptionValues(thresholds), getOptionValues(thresholds)<-
Retreive and modify the values of the thresholds (slot optionValues)
Camille Maumet
# Empty thresholds, the default values will be used when added to an assessment emptThresholds <- new("thresholds") getOptionValues(emptThresholds) getNoThresholds(emptThresholds) # Another thresholds thresholds <- new("thresholds", optionValues=c(0,0.1,0.2,1,2)) getOptionValues(thresholds) getNoThresholds(thresholds) # Set the thresholds newThresholds <- c(0.1,0.2,0.5,0.6,1) getOptionValues(thresholds) <- newThresholds getOptionValues(thresholds) getNoThresholds(thresholds)# Empty thresholds, the default values will be used when added to an assessment emptThresholds <- new("thresholds") getOptionValues(emptThresholds) getNoThresholds(emptThresholds) # Another thresholds thresholds <- new("thresholds", optionValues=c(0,0.1,0.2,1,2)) getOptionValues(thresholds) getNoThresholds(thresholds) # Set the thresholds newThresholds <- c(0.1,0.2,0.5,0.6,1) getOptionValues(thresholds) <- newThresholds getOptionValues(thresholds) getNoThresholds(thresholds)
Gene Expression values and output classes of the 70 best genes selected by van't Veer et al. (cf. references) on 78 patients in ‘Gene expression profiling predicts clinical outcome of breast cancer’.
data('vV70genesDataset')data('vV70genesDataset')
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L.J. van 't Veer LJ, H. Dai, M.J. van de Vijver, Y.D. He, A.A. Hart, M. Mao, H.L. Peterse,K. van der Kooy, M.J. Marton, A.T. Witteveen, G.J. Schreiber, R.M. Kerkhoven, C. Roberts, P.S. Linsley, R. Bernards, S.H. Friend, ‘Gene expression profiling predicts clinical outcome of breast cancer’, in Nature. 2002 Jan 31;415(6871):484-5.
data('vV70genesDataset') vV70genesdata('vV70genesDataset') vV70genes