# mapParams: Parameters for mapping mixture components to distinct copy... In CNPBayes: Bayesian mixture models for copy number polymorphisms

## Description

Parameters for mapping mixture components to distinct copy number states

## Usage

 ```1 2 3``` ```mapParams(threshold = 0.1, proportion.subjects = 0.5, outlier.variance.ratio = 5, max.homozygous = c(-1.5, -0.5), min.foldchange = 1.5) ```

## Arguments

 `threshold` numeric value in [0, 0.5]. For a given observation (sample), mixture component probabilities > threshold and less than 1-threshold are combined. `proportion.subjects` numeric value in [0, 1]. Two components are combined if the fraction of subjects with component probabilities in the range [threshold, 1-threshold] exceeds this value. `outlier.variance.ratio` if the ratio of the component variance to the median variance of the other component exceeds the value of this argument, the component is considered to correspond to outliers. `max.homozygous` length-2 numeric vector of cutoffs used for establishing a homozygous deletion component. The first element is the cutoff for the mean log R ratios when there are 2 or more states. The second element is the cutoff for the mean log R ratio when there are 3 or more states. `min.foldchange` a length-one numeric vector. When there are 3 or more states, we compute the ratio of the distance between the means of the sorted components 1 and 2 (the 2 components with lowest means) and the distance between the means of components 2 and 3. If the ratio (i) exceeds the value specified by this parameter, (ii) there are 3 or more states, and (iii) the first component has a mean less than `max_homozygous`, we infer that the first component is a homozygous deletion.

## Examples

 `1` ```mapParams() ```

CNPBayes documentation built on May 6, 2019, 4:06 a.m.