confusionMat: Accessors for the 'confusionMat' slot of an 'MLSeq' object

Description Usage Arguments Details Author(s) See Also Examples

Description

This slot stores the confusion matrix for the trained model using classify function.

Usage

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confusionMat(object)

## S4 method for signature 'MLSeq'
confusionMat(object)

Arguments

object

an MLSeq object.

Details

confusionMat slot includes information about cross-tabulation of observed and predicted classes and corresponding statistics such as accuracy rate, sensitivity, specifity, etc. The returned object is in confusionMatrix class of caret package. See confusionMatrix for details.

Author(s)

Gokmen Zararsiz

See Also

confusionMatrix

Examples

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library(DESeq2)
data(cervical)

# a subset of cervical data with first 150 features.
data <- cervical[c(1:150),]

# defining sample classes.
class <- data.frame(condition=factor(rep(c("N", "T"), c(29, 29))))

n <- ncol(data)  # number of samples
p <- nrow(data)  # number of features

# number of samples for test set (20% test, 80% train).
nTest <- ceiling(n*0.2)
ind <- sample(n, nTest, FALSE)

# train set
data.train <- data[,-ind]
data.train <- as.matrix(data.train + 1)
classtr <- data.frame(condition=class[-ind, ])

# train set in S4 class
data.trainS4 <- DESeqDataSetFromMatrix(countData = data.train,
                  colData = classtr, formula(~ condition))
data.trainS4 <- DESeq(data.trainS4, fitType = "local")

# Classification and Regression Trees (CART)
cart <- classify(data = data.trainS4, method = "cart",
          transformation = "vst", ref = "T", normalize = "deseq",
          control = trainControl(method = "repeatedcv", number = 5,
                                 repeats = 3, classProbs = TRUE))

confusionMat(cart)

gokmenzararsiz/MLSeq documentation built on May 17, 2019, 7:41 a.m.