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, verbose = FALSE, samples_in_rows = 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.
`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. Note: this might drastically change the number of selected features.

### 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)

[Package FCBF version 1.0.1 Index]