GeneralizationError-class: Class "GeneralizationError" (slot of GeNetClassifierReturn)

In geNetClassifier: Classify diseases and build associated gene networks using gene expression profiles

Description

Contains the estimation of the Generalization Error and the gene stats for geNetClassifier executed with the given data and parameters. \ Calculated by 5-fold cross-validation.

Slots

accuracy:

"Matrix". Accuracy and call rate.

sensitivitySpecificity:

"Matrix". Sensitivity, Specificity, Matthews Correlation Coefficient and Call Rate for each of the classes.

confMatrix:

"Matrix". Confussion matrix.

probMatrix:

"Matrix". Probabilities of belonging to each class for the assigned samples. Helps identifying where errors are likely to occur even though there were not actual errors in the cross-validation.

querySummary:

"List". Stats regarding the probability and number of assigned test samples to each class.

classificationGenes.stats:

"List". Some basic statistics regarding the chosen genes.

classificationGenes.num:

"Matrix". Number of genes used for each of the 5 cross-validaton classifiers.

Methods

overview

signature(object = "GeneralizationError"): Shows an overview of all the slots in the object.

Author(s)

Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain

See Also

Main package function and classifier training: geNetClassifier

Examples

	
######
# Load data and train a classifier
######

# Load an expressionSet:
library(leukemiasEset)
data(leukemiasEset)

# Select the train samples: 
trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) 
# summary(leukemiasEset$LeukemiaType[trainSamples])

# Train a classifier or load a trained one:
# Note: Required 'estimateGError=TRUE' 
# leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], 
#    sampleLabels="LeukemiaType", plotsName="leukemiasClassifier", 
#    estimateGError=TRUE) 
data(leukemiasClassifier) # Sample trained classifier

# Global view of the returned object and its structure:
leukemiasClassifier
names(leukemiasClassifier)

#########
# Exploring the cross validation stats
# Note: Required 'estimateGError=TRUE' in geNetClassifier()
#########
# Generalization Error estimated by cross-validation:
leukemiasClassifier@generalizationError
overview(leukemiasClassifier@generalizationError)
	# i.e. probabilityMatrix:
	leukemiasClassifier@generalizationError@probMatrix
	# i.e. statistics of the genes chosen in any of the CV loops for for AML:
	leukemiasClassifier@[e-mail]$AML

geNetClassifier documentation built on Nov. 8, 2020, 4:53 p.m.