method {MLSeq} | R Documentation |
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.
method(object) method(object) <- value ## S4 method for signature 'MLSeq' method(object) ## S4 method for signature 'MLSeqModelInfo' method(object) ## S4 replacement method for signature 'MLSeq,character' method(object) <- value
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
.
## 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)