# estimateDirichDist: Nonlinear Parameter Estimation for Dirichlet Distribution In genomaths/usefr: Utility Functions for Statistical Analyses

 estimateDirichDist R Documentation

## Nonlinear Parameter Estimation for Dirichlet Distribution

### Description

The parameter estimation is accomplished using a count data matrix. The estimation is based on the fact that if a variable x = (x_1, x_2, ...x_n) follows Dirichlet Distribution with parameters α = α_1, ... , α_n (all positive reals), in short, x ~ Dir(α), then x_i ~ Beta(α_i, α_0 - α_i), where Beta(.) stands for the Beta distribution and α_0 = ∑ α_i.

Dirichlet distribution is a family of continuous multivariate probability distributions, a multivariate generalization of the Beta distribution.

### Usage

```estimateDirichDist(
x,
start = NULL,
num.cores = 1L,
seed = 123,
refit = TRUE,
verbose = TRUE,
...
)
```

### Arguments

 `x` A matrix or a data.frame object carrying count data. `start` Initial parameter values for \lapha = α_1, ... , α_n (all positive reals). Defaults is NULL. `num.cores, tasks` Parameters for parallel computation using `BiocParallel-package`: the number of cores to use, i.e. at most how many child processes will be run simultaneously (see `bplapply` and the number of tasks per job (only for Linux OS). `verbose` if TRUE, prints the function log to stdout and a progress bar `...` Further arguments for `betaDistEstimation` function.

### Details

As any non-linear fitting, results strongly depends on the start parameter values.

### Value

A vector of estimated parameter values

### Author(s)

Robersy Sanchez <https://genomaths.com>

`betaDistEstimation` and `betaBinPost`

### Examples

```library(DirichletReg)

## A random generation numerical vectors with
x <- rdirichlet(n = 1000, alpha = c(2.1, 3.1, 1.2))