filterCollinearities {qpgraph} | R Documentation

## Filter collinearities

### Description

Filters out variables or features that lead to collinearities in the input data.

### Usage

    filterCollinearities(X, soft.filter=FALSE, long.dim.are.variables=TRUE)

### Arguments

`X` | data set where collinearities are identified.
`soft.filter` | logical; if FALSE (default) then the intput object `X` is returned without the variables or features that lead to collinearities, i.e., a hard-filtered version of `X`; if TRUE, then a logical mask is returned with as many positions as variables or features and those that lead to collinearities are set to `TRUE`, while the rest are set to `FALSE`.
`long.dim.are.variables` | logical; if TRUE (default) it is assumed that when `X` is a `data.frame` or a matrix, the longer dimension is the one defining the random variables (default); if FALSE, then random variables are assumed to be at the columns of the `data.frame` or matrix.

### Details

The input object `X` can be either a `matrix` object, a `data.frame` object or any other class of object that can be handled by the function `qpPCC()`, which is internally called, such as an `ExpressionSet` object.

### Value

The input object `X` without the variables or features that lead to collinearities when `soft.filter=FALSE`, its default value. Otherwise, when `soft.filter=TRUE` then a logical mask is returned.

### Author(s)

R. Castelo

### See Also

`qpPCC`

### Examples

    ## build an undirected GMM model with
    ## average correlation 0.99 on the present edges
    set.seed(1234)
    gmm <- rUGgmm(dRegularGraphParam(), rho=0.99)
    gmm

    ## sample n=100 observations from this GMM
    X <- rmvnorm(100, gmm)
    dim(X)
    head(X)

    ## notice some variables lead to collinearities (r > 0.99)
    cor(X)

    ## mask those variables
    mask <- filterCollinearities(X, long.dim.are.variables=FALSE,
                                 soft.filter=TRUE)
    mask
    head(X[, !mask])

[Package qpgraph version 2.21.2 Index]