confusionMat: Accessors for the 'confusionMat' slot.

In MLSeq: Machine Learning Interface for RNA-Seq Data

Description

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

Usage

confusionMat(object)

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

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

Arguments

object

an MLSeq or MLSeqModelInfo 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.

See Also

confusionMatrix

Examples

## Not run: 
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 (30% test, 70% train).
nTest <- ceiling(n*0.3)
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(~ 1))

## Number of repeats (repeats) might change model accuracies ##
# Classification and Regression Tree (CART) Classification
cart <- classify(data = data.trainS4, method = "rpart",
          ref = "T", preProcessing = "deseq-vst",
          control = trainControl(method = "repeatedcv", number = 5,
                                 repeats = 3, classProbs = TRUE))

confusionMat(cart)

## End(Not run)

MLSeq documentation built on Nov. 8, 2020, 5:37 p.m.