plotPriors {baySeq} | R Documentation

## Plots the density of the log values estimated for the mean rate in the prior data for the Negative Binomial approach to detecting differential expression

### Description

This function plots the density of the log values estimated for the mean rate in the data used to estimate a prior distribution for data under the assumption of a Negative Binomial distribution. This function is useful for looking for bimodality of the distributions, and thus determining whether we should try and identify data with no true expression.

### Usage

    plotPriors(cD, group, par = 1)

### Arguments

`cD` | `countData` object, for which priors have been estimated using the assumption of a Negative Binomial distribution (see `getPriors.NB`).
`group` | Which group should we plot the priors for? In general, should be the group that defines non-differentially expressed data. Can be defined either as the number of the element in `'cD@groups'` or as a string which will be partially matched to the names of the `'cD@groups'` elements.
`par` | The parameter of the prior that will be plotted.

### Details

If the plot of the data appears bimodal, then it may be sensible to try and look for data with no true expression by using the option `nullPosts = TRUE` in `getLikelihoods.NB`.

### Value

Plotting function.

### Author(s)

Thomas J. Hardcastle

### See Also

`getPriors.NB`, `getLikelihoods.NB`

### Examples

    # We load in a `countData' object containing the estimated priors (see `getPriors').

    data(CDPriors)

    plotPriors(CDPriors, group = "NDE", par = 1)

[Package baySeq version 2.20.0 Index]