predict {MLSeq} | R Documentation

## Extract predictions from `classify()` object

### Description

This function predicts the class labels of test data for a given model.

`predictClassify` and `predict` functions return the predicted class information along with trained model. Predicted values are given either as class labels or estimated probabilities of each class for each sample. If `type = "raw"`, as can be seen in the example below, the predictions are extracted as raw class labels.In order to extract estimated class probabilities, one should follow the steps below:

- set `classProbs = TRUE` within `control` arguement in `classify`

- set `type = "prob"` within `predictClassify`

### Usage

    ## S3 method for class 'MLSeq'
    predict(object, test.data, ...)

    predictClassify(object, test.data, ...)

    ## S4 method for signature 'MLSeq'
    predict(object, test.data, ...)

### Arguments

`object` | a model of `MLSeq` class returned by `classify`
`test.data` | a `DESeqDataSet` instance of new observations.
`...` | further arguments to be passed to or from methods. These arguements are used in `predict.train` from caret package.

### Value

`MLSeqObject` an MLSeq object returned from `classify`. See details.

`Predictions` a data frame or vector including either the predicted class probabilities or class labels of given test data.

### Note

`predictClassify(...)` function was used in `MLSeq` up to package version 1.14.x. This function is alliased with generic function `predict`. In the upcoming versions of MLSeq package, `predictClassify` function will be ommitted. Default function for predicting new observations will be `predict` from version 1.16.x and later.

### Author(s)

Dincer Goksuluk, Gokmen Zararsiz, Selcuk Korkmaz, Vahap Eldem, Ahmet Ozturk and Ahmet Ergun Karaagaoglu

### See Also

`classify`, `train`, `trainControl`

### 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

    # predicted classes of test samples for CART method (class probabilities)
    pred.cart = predictClassify(cart, data.testS4, type = "prob")
    pred.cart

    # predicted classes of test samples for RF method (class labels)
    pred.cart = predictClassify(cart, data.testS4, type = "raw")
    pred.cart

    ## End(Not run)

[Package MLSeq version 2.6.0 Index]