Description Usage Arguments Details See Also Examples
This slot stores the name of selected model which is used in classify function.
The trained model is stored in slot trainedModel.
See trained for details.
1 2 3 4 5 6 7 8 9 10 11 12  | 
object | 
 an   | 
value | 
 a character string. One of the available classification methods to replace with current method stored in MLSeq object.  | 
method slot stores the name of the classification method such as "svmRadial" for Radial-based Support Vector Machines, "rf" for Random Forests, "voomNSC" for
voom-based Nearest Shrunken Centroids, etc. For the complete list of available methods, see printAvailableMethods and availableMethods.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36  | ## 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))
method(cart)
## End(Not run)
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