DaMiR.EnsembleLearning2cl_Predict {DaMiRseq} | R Documentation

## Predict new samples class

### Description

The models learned by the DaMiR.EnsembleLearning2cl_Training functionn are applied to a dataset, in order to predict the samples class

### Usage

    DaMiR.EnsembleLearning2cl_Predict(data, models_List)

### Arguments

`data` | A data frame of normalized expression data. Rows and Cols should be, respectively, observations and features
`models_List` | A list with the models trained by DaMiR.EnsembleLearning2cl_Training function.

### Details

This function implements the test step of DaMiR.EnsembleLearning2cl function

### Value

A dataframe containing the predictions

### Author(s)

Mattia Chiesa, Luca Piacentini

### Examples

    # use example data:
    data(selected_features)
    data(df)
    set.seed(1)
    # only for the example:
    # speed up the process setting a low 'iter' argument value;
    # for real data set use default 'iter' value (i.e. 100) or higher:
    #  Tr_res <- DaMiR.EnsembleLearning2cl_Training(
    #  selected_features,classes=df$class, fSample.tr.w=0.6, iter=3,
    # cl_type=c("RF","LR"))
    # DaMiR.EnsembleLearning2cl_Predict(selected_features, Tr_res)

[Package DaMiRseq version 1.10.0 Index]