| isUpdated {MLSeq} | R Documentation |
These functions are used to check whether the MLSeq object is modified and/or updated. It is possible to update
classification parameters of MLSeq object which is returned by classify() function.
isUpdated(object) isUpdated(object) <- value isModified(object) isModified(object) <- value ## S4 method for signature 'MLSeq' isUpdated(object) ## S4 replacement method for signature 'MLSeq,logical' isUpdated(object) <- value ## S4 method for signature 'MLSeq' isModified(object) ## S4 replacement method for signature 'MLSeq,logical' isModified(object) <- value
object |
an |
value |
a logical. Change the state of update info. |
a logical.
## 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))
isUpdated(cart)
isModified(cart)
## End(Not run)