citrus.buildFoldsEndpointModels | R Documentation |
Builds a model from features derived from each independent fold of clustering.
citrus.buildFoldsEndpointModels(type, citrus.foldFeatureSet, labels,
regularizationThresholds = NULL, family = "classification", ...)
type |
Model Type. Valid options are |
labels |
Endpoint labels for samples. |
regularizationThresholds |
Regularization thresholds for penalized models. |
family |
Family of model to be constructed. Valid options are |
... |
Other arguments passed to model-fitting functions. |
citrus.foldFeatureSet. |
A |
A list of models, one model fit on each fold's feature set.
Robert Bruggner
# 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)
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