update {MLSeq} | R Documentation

## Update `MLSeq` objects returnd from `classify()`

### Description

This function updates the MLSeq object. If one of the options is changed inside MLSeq object, it should be updated to pass its effecs into classification results.

### Usage

    ## S3 method for class 'MLSeq'
    update(object, ..., env = .GlobalEnv)

    ## S4 method for signature 'MLSeq'
    update(object, ..., env = .GlobalEnv)

### Arguments

`object` | a model of `MLSeq` class returned by `classify`
`...` | optional arguements passed to `classify` function.
`env` | an environment. Define the environment where the trained model is stored.

### Value

same object as an MLSeq object returned from `classify`.

### Note

When an `MLSeq` object is updated, new results are updated on the given object. The results before update process are lost when update is done. To keep the results before update, one should copy the MLSeq object to a new object in global environment.

### See Also

`classify`

### 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))

    # test set
    data.test <- data[ ,ind]
    data.test <- as.matrix(data.test + 1)
    classts <- data.frame(condition=class[ind, ])

    data.testS4 <- DESeqDataSetFromMatrix(countData = data.test,
                                          colData = classts, 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))
    cart

    # Change classification model into "Random Forests" (rf)
    method(cart) <- "rf"
    rf <- update(cart)

    rf

    ## End(Not run)

[Package MLSeq version 2.10.0 Index]