# class_probabilities: Posterior class probabilities for a gene In johannabertl/SelectionMix: Negative binomial mixture model

## Description

The probability that a gene with a given number of non-synonymous mutations is member of each selection class is estimated given a set of (estimated) parameters.

## Usage

 `1` ```class_probabilities(x.non, theta, cvec) ```

## Arguments

 `x.non` Numeric. Number of non-synonymous mutations. Can be a scalar (i. e. one gene) or a vector (a set of genes). `theta` Numeric. Model parameters, c(alpha, beta, p1, ..., pk) such that p1 + ... + p_k = 1 `cvec` Numeric. c(c_1, ..., c_k)

Johanna Bertl

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```# Usage on 3 genes # using parameters alpha = 1, beta = 1, p1=p2=0.5, c1 = 1, c2 = 10 class_probabilities(c(2, 7, 100), theta = c(1, 1, 0.5, 0.5), c(1, 10)) # All genes in the yeast data: data("yeast") cvec = c(0.1, 1, 10, 100) p = c(0.19, 0.32, 0.48, 0.007) alpha = 0.9 beta = 45 # class probabilities for all genes: prob_mat = class_probabilities(yeast\$Non, theta = c(alpha, beta, p), cvec = cvec) # class with the highest probability: prob_max = apply(prob_mat, 1, which.max) ```

johannabertl/SelectionMix documentation built on May 3, 2019, 4:03 p.m.