DaMiR.FBest {DaMiRseq} | R Documentation

## Select best predictors to build Classification Model

### Description

This function allows the user to select a subset of predictors; the number of predictors can be defined by user or selected automatically.

### Usage

    DaMiR.FBest(data, ranking, autoselect = c("no", "yes"), n.pred = 10,
      th.zscore = 2)

### Arguments

`data` | A transposed data frame of expression data. Rows and Cols should be, respectively, observations and features
`ranking` | A data frame with importance score for each feature, generated by `DaMiR.FSort`
`autoselect` | A flag to specify how to select predictors: "no" (default) - Manually: users can specify the number of best predictors, setting `n.pred` argument "yes" - Automatically: users have to specify the importance threshold defined by the `th.zscore` argument; features will be accordingly selected
`n.pred` | If `autoselect="no"` then the user have to specify the number of predictors; default is 10
`th.zscore` | Threshold of scaled importance score (Z-score); default value is 2

### Value

A list containing:

- A data frame of normalized expression data of the most important selected predictors.

- A vector with predictors name.

### Author(s)

Mattia Chiesa, Luca Piacentini

### See Also

`DaMiR.FSort`

### Examples

    # use example data:
    data(data_reduced)
    data(data_relief)
    # select the first 8 predictors rankad by imporatance:
    selected_features <- DaMiR.FBest(data_reduced, data_relief, n.pred = 8)
    # select predictors by importance but automatically:
    selected_features <- DaMiR.FBest(data_reduced, data_relief,
    autoselect = "yes", th.zscore = 1.5)

[Package DaMiRseq version 2.4.3 Index]