Documentation

# paramci

Confidence intervals for probability distribution parameters

## Syntax

``ci = paramci(pd)``
``ci = paramci(pd,Name,Value)``

## Description

example

````ci = paramci(pd)` returns the array `ci` containing the lower and upper boundaries of the 95% confidence interval for each parameter in probability distribution `pd`.```
````ci = paramci(pd,Name,Value)` returns confidence intervals with additional options specified by one or more name-value pair arguments. For example, you can specify a different percentage for the confidence interval, or compute confidence intervals only for selected parameters.```

## Examples

collapse all

Load the sample data. Create a vector containing the first column of students’ exam grade data.

```load examgrades x = grades(:,1);```

Fit a normal distribution object to the data.

`pd = fitdist(x,'Normal')`
```pd = NormalDistribution Normal distribution mu = 75.0083 [73.4321, 76.5846] sigma = 8.7202 [7.7391, 9.98843] ```

The intervals next to the parameter estimates are the 95% confidence intervals for the distribution parameters.

You can also obtain these intervals by using the function `paramci`.

`ci = paramci(pd)`
```ci = 2×2 73.4321 7.7391 76.5846 9.9884 ```

Column 1 of `ci` contains the lower and upper 95% confidence interval boundaries for the mu parameter, and column 2 contains the boundaries for the sigma parameter.

Load the sample data. Create a vector containing the first column of students’ exam grade data.

```load examgrades x = grades(:,1);```

Fit a normal distribution object to the data.

`pd = fitdist(x,'Normal')`
```pd = NormalDistribution Normal distribution mu = 75.0083 [73.4321, 76.5846] sigma = 8.7202 [7.7391, 9.98843] ```

Compute the 99% confidence interval for the distribution parameters.

`ci = paramci(pd,'Alpha',.01)`
```ci = 2×2 72.9245 7.4627 77.0922 10.4403 ```

Column 1 of `ci` contains the lower and upper 99% confidence interval boundaries for the mu parameter, and column 2 contains the boundaries for the sigma parameter.

## Input Arguments

collapse all

Probability distribution, specified as a probability distribution object created using one of the following.

Function or AppDescription
`makedist`Create a probability distribution object using specified parameter values.
`fitdist`Fit a probability distribution object to sample data.
Distribution FitterFit a probability distribution to sample data using the interactive Distribution Fitter app and export the fitted object to the workspace.

### Name-Value Pair Arguments

Specify optional comma-separated pairs of `Name,Value` arguments. `Name` is the argument name and `Value` is the corresponding value. `Name` must appear inside quotes. You can specify several name and value pair arguments in any order as `Name1,Value1,...,NameN,ValueN`.

Example: `'Alpha',0.01` specifies a 99% confidence interval.

Significance level for the confidence interval, specified as the comma-separated pair consisting of `'Alpha'` and a scalar value in the range (0,1). The confidence level of `ci` is `100(1–Alpha)`%. The default value `0.05` corresponds to a 95% confidence interval.

Example: `'Alpha',0.01`

Data Types: `single` | `double`

Parameter list for which to compute confidence intervals, specified as the comma-separated pair consisting of `'Parameter'` and a character vector, string array, or cell array of character vectors containing the parameter names. By default, `paramci` computes confidence intervals for all distribution parameters.

Example: `'Parameter','mu'`

Data Types: `char` | `string` | `cell`

Computation method for the confidence intervals, specified as the comma-separated pair consisting of `'Type'` and `'exact'`, `'Wald'`, or `'lr'`.

`'exact'` computes the confidence intervals using an exact method, and is available for the following distributions.

DistributionComputation Method
BinomialCompute using the Clopper-Pearson method based on exact probability calculations. This method does not provide exact coverage probabilities.
ExponentialCompute using a method based on a chi-square distribution. This method provides exact coverage for complete and Type 2 censored samples.
NormalComputation method based on t and chi-square distributions for uncensored samples provides exact coverage for uncensored samples. For censored samples, `paramci` uses the Wald method if `Type` is `exact`.
LognormalComputation method based on t and chi-square distributions for uncensored samples provides exact coverage. For censored samples, `paramci` uses the Wald method if `Type` is `exact`.
PoissonComputation method based on a chi-square distribution provides exact coverage. For large degrees of freedom, the chi-square is approximated by a normal distribution for numerical efficiency.
RayleighComputation method based on a chi-square distribution provides exact coverage probabilities.

`'exact'` is the default when it is available. Alternatively, you can specify `'Wald'` to compute the confidence intervals using the Wald method, or `'lr'` to compute the confidence intervals using the likelihood ratio method.

Example: `'Type','Wald'`

Boolean flag for the log scale, specified as the comma-separated pair consisting of `'LogFlag'` and a vector containing Boolean values corresponding to each distribution parameter. The flag specifies which Wald intervals to compute on a log scale. The default values depend on the distribution.

Example: `'LogFlag',[0,1]`

Data Types: `logical`

## Output Arguments

collapse all

Confidence interval, returned as a p-by-2 array containing the lower and upper bounds of the `100(1–Alpha)`% confidence interval for each distribution parameter. p is the number of distribution parameters.

If you create `pd` by using `makedist` and specifying the distribution parameters, the lower and upper bounds are equal to the specified parameters.