# binomLikelihoodSmooth: Calculates local likelihood estimations for binomial random... In BiSeq: Processing and analyzing bisulfite sequencing data

## Description

For a given set of binomial random variables with 1-dimensional coordinates, this function calculates the local likelihood estimation of the success probability p at a given point. For this purpose, a weighted likelihood estimation with weights obtained by a triangular kernel with given bandwidth is used. This can be used to predict values at points where no variable has been observed and/or to smooth observations using neighboured observations.

## Usage

 1 binomLikelihoodSmooth(pred.pos, pos, m, n, h) 

## Arguments

 pred.pos A vector of positions where p should be estimated. pos A vector of positions where binomial variables have been observed. m A vector of length pos with the number of successfull experiments. n A vector of length pos with the number of experiments. h The bandwidth of the kernel.

## Details

For a given position x, the weighted likelihood for parameter p

L(p; m, n, w) = ∏_{i=1}^k B(m_i|n_i,p)^{w_i}

is maximized. B denotes the binomial probability function. The weights w_i are calculated using a triangular kernel with bandwidth h:

w_i = K(x_i) = (1 - (|x - x_i|)/h) \mathbf{1}_{((|x - x_i|)/h) ≤ 1}

## Value

A vector of length pred.pos giving the local likelihood estimation of the success probability p at the given positions.

## Author(s)

Hans-Ulrich Klein

## See Also

predictMeth

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 n = rpois(100, lambda=10) E = c(rep(0.4, 30), rep(0.8, 40), rep(0.1, 30)) m = rbinom(100, n, E) pos = 1:100 p_10 = binomLikelihoodSmooth(pos, pos, m, n, h=10) p_20 = binomLikelihoodSmooth(pos, pos, m, n, h=20) ## Not run: plot(x=pos, y=m/n) points(x=pos, y=p_10, col="green") lines(x=pos, y=p_10, col="green") points(x=pos, y=p_20, col="red") lines(x=pos, y=p_20, col="red") ## End(Not run) 

BiSeq documentation built on Nov. 1, 2018, 2:25 a.m.