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

Description Usage Arguments Details Author(s) Examples

Description

Details about the training model information which is obtained classify function.

Usage

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

Arguments

object

an MLSeq object

Details

trained slot stores information about the training process such as optimum model parameters and resampling properties on the fitted classification model.

Author(s)

Gokmen Zararsiz, Dincer Goksuluk, Selcuk Korkmaz, Vahap Eldem, Izzet Parug Duru, Turgay Unver, Ahmet Ozturk

Examples

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data(cervical)

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

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

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

nTest = ceiling(n*0.2)  # number of samples for test set (20% test, 80% train).
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")

# Random Forest (RF) Classification
rf = classify(data = data.trainS4, method = "randomforest", normalize = "deseq", deseqTransform = "vst", cv = 5, rpt = 3, ref="T")

trained(rf)

MLSeq documentation built on May 20, 2017, 9:22 p.m.

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