citrus.thresholdCVs | R Documentation |
Calculate model error rates at different regularization thresholds.
citrus.thresholdCVs.quick.classification(modelType, features, labels,
regularizationThresholds, nCVFolds = 10, ...)
citrus.thresholdCVs.quick.continuous(modelType, features, labels,
regularizationThresholds, nCVFolds = 10, ...)
citrus.thresholdCVs(modelType, foldFeatures, labels, regularizationThresholds,
family, folds, foldModels, leftoutFeatures, ...)
citrus.thresholdCVs.quick(modelType, features, labels, regularizationThresholds,
family, nCVFolds = 10, ...)
modelType |
Type of model to be constructed. Valid options are: |
features |
Features calculated from a clustering of all samples. |
labels |
Endpoint labels of clustered samples. |
regularizationThresholds |
Thresholds for model regularization. |
nCVFolds |
Number of folds for quick cross-validation. |
... |
Other parameters passsed to model-fitting methods. |
foldFeatures |
List of features with each entry containing features from an independent clustering. |
family |
Model family. Valid options are |
folds |
List of fold indices |
foldModels |
Models constructed from each fold of features. |
leftoutFeatures |
Features calculated for leftout samples mapped to clustered data space. |
If independent fold-clustering and fold-features are calculated, use citrus.thresholdCVs
.
If features are derived from a clustering of all samples together, use citrus.thresholdCVs.quick
. See examples.
Matrix of model error rates, standard error of error estimates, and false discovery rates (if possible) at supplied regularization thresholds.
Robert Bruggner
########################################
# Example of citrus.thresholdCVs.quick
########################################
# Where the data lives
dataDirectory = file.path(system.file(package = "citrus"),"extdata","example1")
# Create list of files to be analyzed
fileList = data.frame("unstim"=list.files(dataDirectory,pattern=".fcs"))
# Read the data
citrus.combinedFCSSet = citrus.readFCSSet(dataDirectory,fileList)
# List of columns to be used for clustering
clusteringColumns = c("Red","Blue")
# Cluster data
citrus.clustering = citrus.cluster(citrus.combinedFCSSet,clusteringColumns)
# Large enough clusters
largeEnoughClusters = citrus.selectClusters(citrus.clustering)
# Build features
abundanceFeatures = citrus.calculateFeatures(citrus.combinedFCSSet,clusterAssignments=citrus.clustering$clusterMembership,clusterIds=largeEnoughClusters)
# List disease group of each sample
labels = factor(rep(c("Healthy","Diseased"),each=10))
# Calculate regularization thresholds
regularizationThresholds = citrus.generateRegularizationThresholds.classification(abundanceFeatures,labels,modelType="pamr")
# Calculate CV Error rates
thresholdCVRates = citrus.thresholdCVs.quick("pamr",abundanceFeatures,labels,regularizationThresholds,family="classification")
########################################
# Example of citrus.thresholdCVs
########################################
# Where the data lives
dataDirectory = file.path(system.file(package = "citrus"),"extdata","example1")
# Create list of files to be analyzed
fileList = data.frame("unstim"=list.files(dataDirectory,pattern=".fcs"))
# Read the data
citrus.combinedFCSSet = citrus.readFCSSet(dataDirectory,fileList)
# List disease group of each sample
labels = factor(rep(c("Healthy","Diseased"),each=10))
# List of columns to be used for clustering
clusteringColumns = c("Red","Blue")
# Cluster each fold
citrus.foldClustering = citrus.clusterAndMapFolds(citrus.combinedFCSSet,clusteringColumns,labels,nFolds=4)
# Build fold features and leftout features
citrus.foldFeatureSet = citrus.calculateFoldFeatureSet(citrus.foldClustering,citrus.combinedFCSSet)
# Build fold models
citrus.foldModels = citrus.buildFoldsEndpointModels(type="pamr",citrus.foldFeatureSet,labels)
citrus.thresholdCVs(modelType="pamr",
foldFeatures=citrus.foldFeatureSet$foldFeatures,
labels=labels,
regularizationThresholds=citrus.foldModels[[1]]$regularizationThresholds,
family="classification",
folds=citrus.foldFeatureSet$folds,
foldModels=citrus.foldModels,
leftoutFeatures=citrus.foldFeatureSet$leftoutFeatures)
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