trainParameters: Accessors for the 'trainParameters' slot.

Description Usage Arguments Examples

Description

This slot stores the transformation and normalization parameters from train set. These parameters are used to normalize and transform test set using train set parameters.

Usage

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trainParameters(object)

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

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

Arguments

object

an MLSeq or MLSeqModelInfo object.

Examples

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## 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))

trainParameters(cart)

## End(Not run)

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