forward_selection_byrank {structToolbox} | R Documentation

## forward selection by rank

### Description

Forward selection by rank is a stepwise procedures that includes features incrementally based on their rank. Any measure for ranking the features may be used e.g. PLS VIP score, ttest p-value etc.

### Usage

    forward_selection_byrank(
      min_no_vars = 1,
      max_no_vars = 100,
      step_size = 1,
      factor_name,
      variable_rank,
      ...
    )

### Arguments

`min_no_vars` | minimum number of features to test
`max_no_vars` | maximum numbe ro features to test
`step_size` | the size of the incremenent between min and max no of vars
`factor_name` | the sample-meta colum to use
`variable_rank` | a vector of values that can be used to rank the features, where the smallest value is the first rank.
`...` | additional slots and values passed to struct_class

### Value

A struct object

### Examples

    # some data
    D = MTBLS79_DatasetExperiment(filtered=TRUE)

    # normalise, impute and scale then remove QCs
    P = pqn_norm(qc_label='QC',factor_name='class') +
        knn_impute(neighbours=5) +
        glog_transform(qc_label='QC',factor_name='class') +
        filter_smeta(mode='exclude',levels='QC',factor_name='class')
    P = model_apply(P,D)
    D = predicted(P)

    # forward selection using a PLSDA model
    M = forward_selection_byrank(factor_name='class',
                                 min_no_vars=2,
                                 max_no_vars=11,
                                 variable_rank=1:2063) *
        (mean_centre() + PLSDA(number_components=1,
                               factor_name='class'))
    M = run(M,D,balanced_accuracy())

[Package structToolbox version 1.0.1 Index]