fcbf {FCBF} | R Documentation

## Fast Correlation Based Filter function.

### Description

This functions allows selection of variables from a feature table of discrete/categorial variables and a target class. The function is based on the algorithm described in Yu, L. and Liu, H.; Feature Selection for High-Dimensional Data A Fast Correlation Based Filter Solution, Proc. 20th Intl. Conf. Mach. Learn. (ICML-2003), Washington DC, 2003

### Usage

    fcbf(
      x,
      y,
      thresh = 0.25,
      n_genes = NULL,
      verbose = FALSE,
      samples_in_rows = FALSE,
      balance_classes = FALSE
    )

### Arguments

`x` | A table of features (samples in rows, variables in columns, and each observation in each cell)
`y` | A target vector, factor containing classes of the observations. Note: the observations must be in the same order as the parameter x
`thresh` | A threshold for the minimum correlation (as determined by symettrical uncertainty) between each variable and the class. Defaults to 0.25. Note: this might drastically change the number of selected features.
`n_genes` | Sets the number of genes to be selected in the first part of the algorithm. If left unchanged, it defaults to NULL and the thresh parameter is used. Caution: it overrides the thresh parameter altogether.
`verbose` | Adds verbosity. Defaults to FALSE.
`samples_in_rows` | A flag for the case in which samples are in rows and variables/genes in columns. Defaults to FALSE.
`balance_classes` | Balances number of instances in the target vector y by sampling the number of instances in the minor class from all others. The number of samplings is controlled by resampling_number. Defaults to FALSE.

### Details

Obs: For gene expression, you will need to run discretize_exprs first

### Value

Returns a data frame with the selected features index (first row) and their symmetrical uncertainty values regarding the class (second row). Variable names are present in rownames

### Examples

     data(scDengue)
     exprs <- SummarizedExperiment::assay(scDengue, 'logcounts')
     discrete_expression <- as.data.frame(discretize_exprs(exprs))
     head(discrete_expression[,1:4])
     infection <- SummarizedExperiment::colData(scDengue)
     target <- infection$infection
     fcbf(discrete_expression,target, thresh = 0.05, verbose = TRUE)
     fcbf(discrete_expression,target, n_genes = 100)

[Package FCBF version 1.8.0 Index]