trained {MLSeq} | R Documentation

## Accessors for the 'trainedModel' slot.

### Description

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.

### Usage

    trained(object)

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

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

### Arguments

`object` | an `MLSeq` or `MLSeqModelInfo` object.

### See Also

`train.default`, `voom.train-class`, `discrete.train-class`

### Examples

    ## 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)

[Package MLSeq version 1.20.3 Index]