gamcdf

Gamma cumulative distribution function

Syntax

``p = gamcdf(x,a)``
``p = gamcdf(x,a,b)``
``[p,pLo,pUp] = gamcdf(x,a,b,pCov)``
``[p,pLo,pUp] = gamcdf(x,a,b,pCov,alpha)``
``___ = gamcdf(___,'upper')``

Description

````p = gamcdf(x,a)` returns the cumulative distribution function (cdf) of the standard gamma distribution with the shape parameters in `a`, evaluated at the values in `x`.```

example

````p = gamcdf(x,a,b)` returns the cdf of the gamma distribution with the shape parameters in `a` and scale parameters in `b`, evaluated at the values in `x`.```

example

````[p,pLo,pUp] = gamcdf(x,a,b,pCov)` also returns the 95% confidence interval [`pLo`,`pUp`] of `p` when `a` and `b` are estimates. `pCov` is the covariance matrix of the estimated parameters.```
````[p,pLo,pUp] = gamcdf(x,a,b,pCov,alpha)` specifies the confidence level for the confidence interval [`pLo` `pUp`] to be `100(1–alpha)`%.```

example

````___ = gamcdf(___,'upper')` returns the complement of the cdf, evaluated at the values in `x`, using an algorithm that more accurately computes the extreme upper-tail probabilities than subtracting the lower tail value from 1. `'upper'` can follow any of the input argument combinations in the previous syntaxes.```

Examples

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Compute the cdf of the mean of the gamma distribution, which is equal to the product of the parameters ab.

```a = 1:6; b = 5:10; prob = gamcdf(a.*b,a,b)```
```prob = 1×6 0.6321 0.5940 0.5768 0.5665 0.5595 0.5543 ```

As `ab` increases, the distribution becomes more symmetric, and the mean approaches the median.

Find a confidence interval estimating the probability that an observation is in the interval `[0 10]` using gamma distributed data.

Generate a sample of `1000` gamma distributed random numbers with shape `2` and scale `5`.

`x = gamrnd(2,5,1000,1);`

Compute estimates for the parameters.

`[params,~] = gamfit(x)`
```params = 1×2 2.1089 4.8147 ```

Store the parameters as `ahat` and `bhat`.

```ahat = params(1); bhat = params(2);```

Find the covariance of the parameter estimates.

`[~,nCov] = gamlike(params,x)`
```nCov = 2×2 0.0077 -0.0176 -0.0176 0.0512 ```

Create a confidence interval estimating the probability that an observation is in the interval `[0 10]`.

`[prob,pLo,pUp] = gamcdf(10,ahat,bhat,nCov)`
```prob = 0.5830 ```
```pLo = 0.5587 ```
```pUp = 0.6069 ```

Determine the probability that an observation from the gamma distribution with shape parameter `2` and scale parameter 3 will is in the interval `[150 Inf]`.

`p1 = 1 - gamcdf(150,2,3)`
```p1 = 0 ```

`gamcdf(150, 2, 3)` is nearly `1`, so `p1` becomes `0`. Specify `'upper'` so that `gamcdf` computes the extreme upper-tail probabilities more accurately.

`p2 = gamcdf(150,2,3,'upper')`
```p2 = 9.8366e-21 ```

Input Arguments

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Values at which to evaluate the cdf, specified as a nonnegative scalar value or an array of nonnegative scalar values.

If you specify `pCov` to compute the confidence interval `[pLo,pUp]`, then `x` must be a scalar value.

• To evaluate the cdf at multiple values, specify `x` using an array.

• To evaluate the cdfs of multiple distributions, specify `a` and `b` using arrays.

If one or more of the input arguments `x`, `a`, and `b` are arrays, then the array sizes must be the same. In this case, `gamcdf` expands each scalar input into a constant array of the same size as the array inputs. Each element in `p` is the cdf value of the distribution specified by the corresponding elements in `a` and `b`, evaluated at the corresponding element in `x`.

Example: `[3 4 7 9]`

Data Types: `single` | `double`

Shape of the gamma distribution, specified as a positive scalar value or an array of positive scalar values.

• To evaluate the cdf at multiple values, specify `x` using an array.

• To evaluate the cdfs of multiple distributions, specify `a` and `b` using arrays.

If one or more of the input arguments `x`, `a`, and `b` are arrays, then the array sizes must be the same. In this case, `gamcdf` expands each scalar input into a constant array of the same size as the array inputs. Each element in `p` is the cdf value of the distribution specified by the corresponding elements in `a` and `b`, evaluated at the corresponding element in `x`.

Example: `[1 2 3 5]`

Data Types: `single` | `double`

Scale of the gamma distribution, specified as a positive scalar value or an array of positive scalar values.

• To evaluate the cdf at multiple values, specify `x` using an array.

• To evaluate the cdfs of multiple distributions, specify `a` and `b` using arrays.

If one or more of the input arguments `x`, `a`, and `b` are arrays, then the array sizes must be the same. In this case, `gamcdf` expands each scalar input into a constant array of the same size as the array inputs. Each element in `p` is the cdf value of the distribution specified by the corresponding elements in `a` and `b`, evaluated at the corresponding element in `x`.

Example: `[1 1 2 2]`

Data Types: `single` | `double`

Covariance of the estimates `a` and `b`, specified as a 2-by-2 matrix.

If you specify `pCov` to compute the confidence interval `[pLo,pUp]`, then `x`, `a`, and `b` must be scalar values.

You can estimate `a` and `b` by using `gamfit` or `mle`, and estimate the covariance of `a` and `b` by using `gamlike`. For an example, see Confidence Interval of Gamma cdf Value.

Data Types: `single` | `double`

Significance level for the confidence interval, specified as a scalar in the range (0,1). The confidence level is `100(1–alpha)`%, where `alpha` is the probability that the confidence interval does not contain the true value.

Example: `0.01`

Data Types: `single` | `double`

Output Arguments

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cdf values evaluated at the values in `x`, returned as a scalar value or an array of scalar values. `p` is the same size as `x`, `a`, and `b` after any necessary scalar expansion. Each element in `p` is the cdf value of the distribution specified by the corresponding elements in `a` and `b`, evaluated at the corresponding element in `x`.

Lower confidence bound for `p`, returned as a scalar value or an array of scalar values. `pLo` has the same size as `p`.

Upper confidence bound for `p`, returned as a scalar value or an array of scalar values. `pUp` has the same size as `p`.

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Gamma cdf

The gamma distribution is a two-parameter family of curves. The parameters a and b are shape and scale, respectively.

The gamma cdf is

`$p=F\left(x|a,b\right)=\frac{1}{{b}^{a}\Gamma \left(a\right)}\underset{0}{\overset{x}{\int }}{t}^{a-1}{e}^{\frac{-t}{b}}dt.$`

The result p is the probability that a single observation from a gamma distribution with parameters a and b falls in the interval [0,x].

The gamma cdf is related to the incomplete gamma function `gammainc` by

`$f\left(x|a,b\right)=\text{gammainc}\left(\frac{x}{b},a\right).$`

The standard gamma distribution occurs when b = 1, which coincides with the incomplete gamma function precisely.

Alternative Functionality

• `gamcdf` is a function specific to the gamma distribution. Statistics and Machine Learning Toolbox™ also offers the generic function `cdf`, which supports various probability distributions. To use `cdf`, create a `GammaDistribution` probability distribution object and pass the object as an input argument or specify the probability distribution name and its parameters. Note that the distribution-specific function `gamcdf` is faster than the generic function `cdf`.

• Use the Probability Distribution Function app to create an interactive plot of the cumulative distribution function (cdf) or probability density function (pdf) for a probability distribution.

Version History

Introduced before R2006a