| trained {MLSeq} | R Documentation |
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.
trained(object) ## S4 method for signature 'MLSeq' trained(object) ## S4 method for signature 'MLSeqModelInfo' trained(object)
object |
an |
train.default, voom.train-class, discrete.train-class
## 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)