weightFuncs {podkat} | R Documentation

## Weighting Functions

### Description

Functions for computing SNV weights from minor allele frequences (MAF)

### Usage

    betaWeights(x, shape1=1, shape2=25)
    logisticWeights(x, th=0.07, slope=150)
    invSdWeights(x)

### Arguments

`x` | a numeric vector of minor allele frequencies (MAFs); see details below
`shape1,shape2` | shape parameters of Beta distribution weighting function (see `dbeta` for details)
`th,slope` | parameters of the logistic weighting function (see details below)

### Details

The function `betaWeights` is a wrapper around the `dbeta` function. It uses the same parameters `shape1` and `shape2`, but does not support the non-centrality parameter `ncp`. The defaults are `shape1=1` and `shape2=25` as suggested by Wu et al. (2011) and implemented in the SKAT package. If called without argument `x`, a function with a single argument `x` is returned that can directly be used as weighting function, e.g. passed as `weightFunc` argument to the `assocTest` method.

The function `logisticWeights` provides a logistic weighting that corresponds to a soft threshold function. The `th` parameter corresponds to the threshold and the `slope` parameter corresponds to the steepness of the soft threshold. Like `betaWeights`, this function can be called without `x` argument to produce a parameter-free weighting function.

The function `invSdWeights` computes weights as suggested by Madsen and Browning (2009). For consistency, this function also returns a single-argument function if called without `x` argument.

For mathematical details, see Subsection 9.3 of the package vignette.

### Value

a numeric vector with weights as long as the argument `x`, a function if `x` was missing;

### Author(s)

Ulrich Bodenhofer bodenhofer@bioinf.jku.at

### References

http://www.bioinf.jku.at/software/podkat

Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M., and Lin, X. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82-93. DOI: 10.1016/j.ajhg.2011.05.029.

Madsen, B. E. and Browning, S. R. (2009) A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genetics 5, e1000384. 10.1371/journal.pgen.1000384

### See Also

`GenotypeMatrix`, `dbeta`, `assocTest`

### Examples

    ## create a toy example
    A <- matrix(rbinom(50, 2, prob=0.2), 5, 10)
    MAF <- colSums(A) / (2 * nrow(A))

    ## compute some weight vectors
    betaWeights(MAF, 1, 25)
    betaWeights(MAF, 1, 30)
    logisticWeights(MAF)
    invSdWeights(MAF)

    ## plot weighting functions (note the missing 'x' arguments)
    plot(betaWeights(shape2=30), xlim=c(0, 1))
    plot(logisticWeights(), xlim=c(0, 1))
    plot(invSdWeights, xlim=c(0, 1))

[Package podkat version 1.18.0 Index]