trained: Accessors for the 'trainedModel' slot.

In MLSeq: Machine Learning Interface for RNA-Seq Data

Description

This slot stores the trained model. This object is returned from train function in caret package. Any further request using caret functions is available for trainedModel since this object is in the same class as the returned object from train. See train for details.

Usage

trained(object)

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

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

Arguments

object

an MLSeq or MLSeqModelInfo object.

See Also

train.default, voom.train-class, discrete.train-class

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))

trained(cart)

## End(Not run)

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