citrus.buildEndpointModel | R Documentation |
This function constructs an endpoint model using features calculated by citrus.
citrus.buildModel.classification(features, labels, type,
regularizationThresholds, ...)
citrus.buildModel.continuous(features, labels, type, regularizationThresholds,
...)
citrus.buildEndpointModel(features, labels, family = "classification",
type = "pamr", regularizationThresholds = NULL, ...)
print.citrus.endpointModel(citrus.endpointModel, ...)
features |
A numeric matrix of predictive features. Rows are observations and column entries are features. |
labels |
A vector of endpoint values (i.e. class labels) for each row of the feature matrix. |
type |
Statistical model to be used. For |
regularizationThresholds |
Vector of regularization values for penalized model construction. If |
... |
Other parameters passed to model-fitting procedures. |
family |
Family of endpoint model to be constructed. Valid values are |
An object of class citrus.endpointModel
with properties:
model |
The statistical model fit on supplied data. |
regularizationThresholds |
Regularization Thresholds used to constrain penalized models. |
family |
Family of model. |
type |
Model type. |
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 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))
# Build model
endpointModel = citrus.buildEndpointModel(abundanceFeatures,labels)
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