# NakagamiDistribution

Nakagami probability distribution object

## Description

A `NakagamiDistribution` object consists of parameters, a model description, and sample data for a Nakagami probability distribution.

The Nakagami distribution is commonly used in communication theory to model scattered signals that reach a receiver using multiple paths.

The Nakagami distribution uses the following parameters.

ParameterDescriptionSupport
`mu`Shape parameter$\mu >0$
`omega`Scale parameter$\omega >0$

## Creation

There are several ways to create a `NakagamiDistribution` probability distribution object.

## Properties

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### Distribution Parameters

Shape parameter for the Nakagami distribution, specified as a positive scalar value.

Data Types: `single` | `double`

Scale parameter for the Nakagami distribution, specified as a positive scalar value.

Data Types: `single` | `double`

### Distribution Characteristics

Logical flag for truncated distribution, specified as a logical value. If `IsTruncated` equals `0`, the distribution is not truncated. If `IsTruncated` equals `1`, the distribution is truncated.

Data Types: `logical`

Number of parameters for the probability distribution, specified as a positive integer value.

Data Types: `double`

Covariance matrix of the parameter estimates, specified as a p-by-p matrix, where p is the number of parameters in the distribution. The (`i`,`j`) element is the covariance between the estimates of the `i`th parameter and the `j`th parameter. The (`i`,`i`) element is the estimated variance of the `i`th parameter. If parameter `i` is fixed rather than estimated by fitting the distribution to data, then the (`i`,`i`) elements of the covariance matrix are 0.

Data Types: `double`

Logical flag for fixed parameters, specified as an array of logical values. If `0`, the corresponding parameter in the `ParameterNames` array is not fixed. If `1`, the corresponding parameter in the `ParameterNames` array is fixed.

Data Types: `logical`

Distribution parameter values, specified as a vector of scalar values.

Data Types: `single` | `double`

Truncation interval for the probability distribution, specified as a vector of scalar values containing the lower and upper truncation boundaries.

Data Types: `single` | `double`

### Other Object Properties

Probability distribution name, specified as a character vector.

Data Types: `char`

Data used for distribution fitting, specified as a structure containing the following:

• `data`: Data vector used for distribution fitting.

• `cens`: Censoring vector, or empty if none.

• `freq`: Frequency vector, or empty if none.

Data Types: `struct`

Distribution parameter descriptions, specified as a cell array of character vectors. Each cell contains a short description of one distribution parameter.

Data Types: `char`

Distribution parameter names, specified as a cell array of character vectors.

Data Types: `char`

## Object Functions

 `cdf` Cumulative distribution function `gather` Gather properties of Statistics and Machine Learning Toolbox object from GPU `icdf` Inverse cumulative distribution function `iqr` Interquartile range of probability distribution `mean` Mean of probability distribution `median` Median of probability distribution `negloglik` Negative loglikelihood of probability distribution `paramci` Confidence intervals for probability distribution parameters `pdf` Probability density function `plot` Plot probability distribution object `proflik` Profile likelihood function for probability distribution `random` Random numbers `std` Standard deviation of probability distribution `truncate` Truncate probability distribution object `var` Variance of probability distribution

## Examples

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Create a Nakagami distribution object using the default parameter values.

`pd = makedist('Nakagami')`
```pd = NakagamiDistribution Nakagami distribution mu = 1 omega = 1 ```

Create a Nakagami distribution object by specifying parameter values.

`pd = makedist('Nakagami','mu',5,'omega',2)`
```pd = NakagamiDistribution Nakagami distribution mu = 5 omega = 2 ```

Compute the mean of the distribution.

`m = mean(pd)`
```m = 1.3794 ```

## Version History

Introduced in R2013a