getSizeNB {dexus} | R Documentation

## Maximum-likelihood and maximum-a-posteriori estimators for the negative binomial distribution.

### Description

Estimates the size parameter of a a negative binomial distribution from given data.

### Usage

      getSizeNB(x, maxCyc = 1000, eta = 0, rmax = Inf,
        method = "bisection")

### Arguments

`x` | The input data. Must be a numeric vector.
`maxCyc` | The maximum number of cycles of the numeric procedure to find the estimator. (Default = 1000).
`eta` | The weight of the exponential prior. The higher eta, the lower the estimate for the size parameter. Setting eta = 0 means that the prior is not used and, therefore, the maximum-likelihood estimator is calculated. (Default = 0).
`rmax` | Upper bound on the size parameter. This corresponds to a truncated exponential prior. If not used there is a non-zero probability that the estimator for the size parameter is ∞. (Default = Inf).
`method` | The procedure used to solve the equation ∑_{k=1} ^N ψ (x_i+r) - Nψ(r)+N \log ≤ft(\frac{r}{r+ 1/N ∑_{i=1}^N x_i} \right) - η =0 for r. This can either be "bisection" or "regula falsi". (Default="bisection").

### Details

Depending on the parameters you can either obtain the Maximum-likelihood estimator or the maximum-a-posteriori estimator using an exponential prior.

maximum-likelihood estimator | eta = 0
maximum-a-posteriori estimator | eta > 0

By setting the variable rmax to a positive value one can enforce an upper bound on the parameter.

The inverse of the size parameter is the overdispersion parameter.

### Value

"numeric" An estimate of the size parameter of the negative binomial distribution. The overdispersion parameter is the inverse of the size parameter of a negative binomial distribution

### Author(s)

Guenter Klambauer klambauer@bioinf.jku.at and Thomas Unterthiner unterthiner@bioinf.jku.at

### Examples

    x <- rnbinom(mu=50, size=5, n=10)
    getSizeNB(x)

[Package dexus version 1.22.1 Index]