trained: Accessors for the 'trainedModel' slot of an 'MLSeq' object

In gokmenzararsiz/MLSeq: Machine Learning Interface for RNA-Seq Data

Description

This slot stores the trained model. This object is returned from train.default 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.default. See train.default for details.

Usage

trained(object)

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

Arguments

object

an MLSeq object.

Author(s)

Gokmen Zararsiz

See Also

train.default

Examples

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))

trained(cart)

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