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The Distribution Fitter app provides a visual, interactive approach to fitting univariate distributions to data.

You can use the Distribution Fitter app to interactively fit
probability distributions to data imported from the MATLAB^{®} workspace.
You can choose from 22 built-in probability distributions, or create
your own custom distribution. The app displays the fitted distribution
over plots of the empirical distributions, including pdf, cdf, probability
plots, and survivor functions. You can export the fit data, including
fitted parameter values, to the workspace for further analysis.

To fit a probability distribution to your sample data:

On the MATLAB Toolstrip, click the Apps tab. In the Math, Statistics and Optimization group, open the Distribution Fitter app. Alternatively, at the command prompt, enter

`distributionFitter`

.Import your sample data, or create a data vector directly in the app. You can also manage your data sets and choose which one to fit. See Create and Manage Data Sets.

Create a new fit for your data. See Create a New Fit.

Display the results of the fit. You can choose to display the density (pdf), cumulative probability (cdf), quantile (inverse cdf), probability plot (choose one of several distributions), survivor function, and cumulative hazard. See Display Results.

You can create additional fits, and manage multiple fits from within the app. See Manage Fits.

Evaluate probability functions for the fit. You can choose to evaluate the density (pdf), cumulative probability (cdf), quantile (inverse cdf), survivor function, and cumulative hazard. See Evaluate Fits.

Improve the fit by excluding certain data. You can specify bounds for the data to exclude, or you can exclude data graphically using a plot of the values in the sample data. See Exclude Data.

Save your current Distribution Fitter app session so you can open it later. See Save and Load Sessions.

To open the Data dialog box, click the **Data** button
in the Distribution Fitter app.

Create a data set by importing a vector from the MATLAB workspace
using the **Import workspace vectors** pane.

**Data**— In the**Data**field, the drop-down list contains the names of all matrices and vectors, other than 1-by-1 matrices (scalars) in the MATLAB workspace. Select the array containing the data that you want to fit. The actual data you import must be a vector. If you select a matrix in the**Data**field, the first column of the matrix is imported by default. To select a different column or row of the matrix, click**Select Column or Row**. The matrix displays in the Variables editor. You can select a row or column by highlighting it.Alternatively, you can enter any valid MATLAB expression in the

**Data**field.When you select a vector in the

**Data**field, a histogram of the data appears in the**Data preview**pane.**Censoring**— If some of the points in the data set are censored, enter a Boolean vector of the same size as the data vector, specifying the censored entries of the data. A`1`

in the censoring vector specifies that the corresponding entry of the data vector is censored. A`0`

specifies that the entry is not censored. If you enter a matrix, you can select a column or row by clicking**Select Column or Row**. If you do not have censored data, leave the**Censoring**field blank.**Frequency**— Enter a vector of positive integers of the same size as the data vector to specify the frequency of the corresponding entries of the data vector. For example, a value of`7`

in the 15th entry of frequency vector specifies that there are 7 data points corresponding to the value in the 15th entry of the data vector. If all entries of the data vector have frequency 1, leave the**Frequency**field blank.**Data set name**— Enter a name for the data set that you import from the workspace, such as`My data`

.

After you have entered the information in the preceding fields,
click **Create Data Set** to create the data set ```
My
data
```

.

View and manage the data sets that you create using the **Manage
data sets** pane. When you create a data set, its name
appears in the **Data sets** list. The following
figure shows the **Manage data sets** pane
after creating the data set `My data`

.

For each data set in the **Data sets** list,
you can:

Select the

**Plot**check box to display a plot of the data in the main Distribution Fitter app window. When you create a new data set,**Plot**is selected by default. Clearing the**Plot**check box removes the data from the plot in the main window. You can specify the type of plot displayed in the**Display type**field in the main window.If

**Plot**is selected, you can also select**Bounds**to display confidence interval bounds for the plot in the main window. These bounds are pointwise confidence bounds around the empirical estimates of these functions. The bounds are displayed only when you set**Display Type**in the main window to one of the following:`Cumulative probability (CDF)`

`Survivor function`

`Cumulative hazard`

The Distribution Fitter app cannot display confidence bounds
on density (`PDF`

), quantile (```
inverse
CDF
```

), or probability plots. Clearing the **Bounds** check
box removes the confidence bounds from the plot in the main window.

When you select a data set from the list, you can access the following buttons:

**View**— Display the data in a table in a new window.**Set Bin Rules**— Defines the histogram bins used in a density (PDF) plot.**Rename**— Rename the data set.**Delete**— Delete the data set.

To set bin rules for the histogram of a data set, click **Set
Bin Rules** to open the **Set Bin Width Rules** dialog
box.

You can select from the following rules:

**Freedman-Diaconis rule**— Algorithm that chooses bin widths and locations automatically, based on the sample size and the spread of the data. This rule, which is the default, is suitable for many kinds of data.**Scott rule**— Algorithm intended for data that are approximately normal. The algorithm chooses bin widths and locations automatically.**Number of bins**— Enter the number of bins. All bins have equal widths.**Bins centered on integers**— Specifies bins centered on integers.**Bin width**— Enter the width of each bin. If you select this option, you can also select:**Automatic bin placement**— Place the edges of the bins at integer multiples of the**Bin width**.**Bin boundary at**— Enter a scalar to specify the boundaries of the bins. The boundary of each bin is equal to this scalar plus an integer multiple of the**Bin width**.

You can also:

**Apply to all existing data sets**— Apply the rule to all data sets. Otherwise, the rule is applied only to the data set currently selected in the Data dialog box.**Save as default**— Apply the current rule to any new data sets that you create. You can set default bin width rules by selecting**Set Default Bin Rules**from the**Tools**menu in the main window.

Click the **New Fit** button at the top of
the main window to open the New Fit dialog box. If you created the
data set `My data`

, it appears in the **Data** field.

Field Name | Description |
---|---|

Fit Name | Enter a name for the fit. |

Data | Select the data set to which you want to fit a distribution from the drop-down list. |

Distribution | Select the type of distribution to fit from the Only the distributions that apply to the values
of the selected data set appear in the You can specify either a parametric or a nonparametric distribution. When you select a parametric distribution from the drop-down list, a description of its parameters appears. Distribution Fitter estimates these parameters to fit the distribution to the data set. If you select the binomial distribution or the generalized extreme value distribution, you must specify a fixed value for one of the parameters. The pane contains a text field into which you can specify that parameter. When
you select |

Exclusion rule | Specify a rule to exclude some data. Create an exclusion rule
by clicking Exclude in the Distribution Fitter
app. For more information, see Exclude Data. |

Click **Apply** to fit the distribution.
For a parametric fit, the **Results** pane displays
the values of the estimated parameters. For a nonparametric fit, the **Results** pane
displays information about the fit.

When you click **Apply**, the Distribution
Fitter app displays a plot of the distribution and the corresponding
data.

When you click **Apply**, the title of the
dialog box changes to Edit Fit. You can now make changes to the fit
you just created and click **Apply** again to save
them. After closing the Edit Fit dialog box, you can reopen it from
the Fit Manager dialog box at any time to edit the fit.

After applying the fit, you can save the information to the
workspace using probability distribution objects by clicking **Save
to workspace**.

All of the distributions available in the Distribution Fitter
app are supported elsewhere in Statistics and Machine
Learning Toolbox™ software. You
can use the `fitdist`

function
to fit any of the distributions supported by the app. Many distributions
also have dedicated fitting functions. These functions compute the
majority of the fits in the Distribution Fitter app, and are referenced
in the following list. Other fits are computed using functions internal
to the Distribution Fitter app.

Not all of the distributions listed are available for all data
sets. The Distribution Fitter app determines the extent of the data
(nonnegative, unit interval, etc.) and displays appropriate distributions
in the **Distribution** drop-down list. Distribution
data ranges are given parenthetically in the following list.

Beta (unit interval values) distribution, fit using the function

`betafit`

.Binomial (nonnegative integer values) distribution, fit using the function

`binopdf`

.Birnbaum-Saunders (positive values) distribution.

Burr Type XII (positive values) distribution.

Exponential (nonnegative values) distribution, fit using the function

`expfit`

.Extreme value (all values) distribution, fit using the function

`evfit`

.Gamma (positive values) distribution, fit using the function

`gamfit`

.Generalized extreme value (all values) distribution, fit using the function

`gevfit`

.Generalized Pareto (all values) distribution, fit using the function

`gpfit`

.Inverse Gaussian (positive values) distribution.

Logistic (all values) distribution.

Loglogistic (positive values) distribution.

Lognormal (positive values) distribution, fit using the function

`lognfit`

.Nakagami (positive values) distribution.

Negative binomial (nonnegative integer values) distribution, fit using the function

`nbinpdf`

.Nonparametric (all values) distribution, fit using the function

`ksdensity`

.Normal (all values) distribution, fit using the function

`normfit`

.Poisson (nonnegative integer values) distribution, fit using the function

`poisspdf`

.Rayleigh (positive values) distribution using the function

`raylfit`

.Rician (positive values) distribution.

*t*location-scale (all values) distribution.Weibull (positive values) distribution using the function

`wblfit`

.

When you select `Non-parametric`

in
the **Distribution** field, a set of options
appears in the **Non-parametric** pane, as shown
in the following figure.

The options for nonparametric distributions are:

**Kernel**— Type of kernel function to use.`Normal`

`Box`

`Triangle`

`Epanechnikov`

**Bandwidth**— The bandwidth of the kernel smoothing window. Select**Auto**for a default value that is optimal for estimating normal densities. After you click**Apply**, this value appears in the**Fit results**pane. Select**Specify**and enter a smaller value to reveal features such as multiple modes or a larger value to make the fit smoother.**Domain**— The allowed*x*-values for the density.**Unbounded**— The density extends over the whole real line.**Positive**— The density is restricted to positive values.**Specify**— Enter lower and upper bounds for the domain of the density.

When you select

**Positive**or**Specify**, the nonparametric fit has zero probability outside the specified domain.

The Distribution Fitter app window displays plots of:

The data sets for which you select

**Plot**in the Data dialog box.The fits for which you select

**Plot**in the Fit Manager dialog box.Confidence bounds for:

The data sets for which you select

**Bounds**in the Data dialog box.The fits for which you select

**Bounds**in the Fit Manager dialog box.

Adjust the plot display using the buttons at the top of the tool:

— Toggle the legend on (default) or off.

— Toggle grid lines on or off (default).

— Restore default axes limits.

The following fields are available.

Specify the type of plot to display using the **Display
Type** field in the main app window. Each type corresponds
to a probability function, for example, a probability density function.
You can choose from the following display types:

`Density (PDF)`

— Display a probability density function (PDF) plot for the fitted distribution. The main window displays data sets using a probability histogram, in which the height of each rectangle is the fraction of data points that lie in the bin divided by the width of the bin. This makes the sum of the areas of the rectangles equal to 1.`Cumulative probability (CDF)`

— Display a cumulative probability plot of the data. The main window displays data sets using a cumulative probability step function. The height of each step is the cumulative sum of the heights of the rectangles in the probability histogram.`Quantile (inverse CDF)`

— Display a quantile (inverse CDF) plot.`Probability plot`

— Display a probability plot of the data. Specify the type of distribution used to construct the probability plot in the**Distribution**field. This field is only available when you select`Probability plot`

. The choices for the distribution are:`Exponential`

`Extreme value`

`Logistic`

`Log-Logistic`

`Lognormal`

`Normal`

`Rayleigh`

`Weibull`

You can also create a probability plot against a parametric fit that you create in the

**New Fit**pane. When you create these fits, they are added at the bottom of the**Distribution**drop-down list.`Survivor function`

— Display survivor function plot of the data.`Cumulative hazard`

— Display cumulative hazard plot of the data.### Note

If the plotted data includes

`0`

or negative values, some distributions are unavailable.

You can display confidence bounds for data sets and fits when
you set **Display Type** to ```
Cumulative
probability (CDF)
```

, `Survivor function`

, ```
Cumulative
hazard
```

, or, for fits only, ```
Quantile (inverse
CDF)
```

.

To display bounds for a data set, select

**Bounds**next to the data set in the**Data sets**pane of the Data dialog box.To display bounds for a fit, select

**Bounds**next to the fit in the Fit Manager dialog box. Confidence bounds are not available for all fit types.

To set the confidence level for the bounds, select ```
Confidence
Level
```

from the **View** menu
in the main window and choose from the options.

Click the **Manage Fits** button to open
the **Fit Manager** dialog box.

The **Table of fits** displays a list of the
fits that you create, with the following options:

**Plot**— Displays a plot of the fit in the main window of the Distribution Fitter app. When you create a new fit,**Plot**is selected by default. Clearing the**Plot**check box removes the fit from the plot in the main window.**Bounds**— If you select**Plot**, you can also select**Bounds**to display confidence bounds in the plot. The bounds are displayed when you set**Display Type**in the main window to one of the following:`Cumulative probability (CDF)`

`Quantile (inverse CDF)`

`Survivor function`

`Cumulative hazard`

The Distribution Fitter app cannot display confidence bounds on density (

`PDF`

) or probability plots. Bounds are not supported for nonparametric fits and some parametric fits.Clearing the

**Bounds**check box removes the confidence intervals from the plot in the main window.When you select a fit in the

**Table of fits**, the following buttons are enabled below the table:**New Fit**— Open a New Fit window.**Copy**— Create a copy of the selected fit.**Edit**— Open an Edit Fit dialog box, to edit the fit.### Note

You can edit only the currently selected fit in the Edit Fit dialog box. To edit a different fit, select it in the

**Table of fits**and click**Edit**to open another Edit Fit dialog box.**Save to workspace**— Save the selected fit as a distribution object.**Delete**— Delete the selected fit.

Use the **Evaluate** dialog box to evaluate
your fitted distribution at any data points you choose. To open the
dialog box, click the **Evaluate** button.

In the **Evaluate** dialog box, choose from
the following items:

**Fit**pane — Display the names of existing fits. Select one or more fits that you want to evaluate. Using your platform specific functionality, you can select multiple fits.**Function**— Select the type of probability function that you want to evaluate for the fit. The available functions are:`Density (PDF)`

— Computes a probability density function.`Cumulative probability (CDF)`

— Computes a cumulative probability function.`Quantile (inverse CDF)`

— Computes a quantile (inverse CDF) function.`Survivor function`

— Computes a survivor function.`Cumulative hazard`

— Computes a cumulative hazard function.`Hazard rate`

— Computes the hazard rate.

**At x =**— Enter a vector of points or the name of a workspace variable containing a vector of points at which you want to evaluate the distribution function. If you change**Function**to`Quantile (inverse CDF)`

, the field name changes to**At p =**, and you enter a vector of probability values.**Compute confidence bounds**— Select this box to compute confidence bounds for the selected fits. The check box is enabled only if you set**Function**to one of the following:`Cumulative probability (CDF)`

`Quantile (inverse CDF)`

`Survivor function`

`Cumulative hazard`

The Distribution Fitter app cannot compute confidence bounds for nonparametric fits and for some parametric fits. In these cases, it returns

`NaN`

for the bounds.**Level**— Set the level for the confidence bounds.**Plot function**— Select this box to display a plot of the distribution function, evaluated at the points you enter in the**At x =**field, in a new window.### Note

The settings for

**Compute confidence bounds**,**Level**, and**Plot function**do not affect the plots that are displayed in the main window of the Distribution Fitter app. The settings apply only to plots you create by clicking**Plot function**in the Evaluate window.

To apply these evaluation settings to the selected fit, click **Apply**.
The following figure shows the results of evaluating the cumulative
density function for the fit **My fit**, at the points
in the vector `-4:1:6`

.

The columns of the table to the right of the **Fit** pane
display the following values:

X — The entries of the vector that you enter in

**At x =**field.F(X)— The corresponding values of the CDF at the entries of X.

LB — The lower bounds for the confidence interval, if you select

**Compute confidence bounds**.UB — The upper bounds for the confidence interval, if you select

**Compute confidence bounds**.

To save the data displayed in the table to a matrix in the MATLAB workspace,
click **Export to Workspace**.

To exclude values from fit, open the **Exclude** window
by clicking the **Exclude** button. In the **Exclude** window,
you can create rules for excluding specified data values. When you
create a new fit in the **New Fit** window, you
can use these rules to exclude data from the fit.

To create an exclusion rule:

**Exclusion Rule Name**— Enter a name for the exclusion rule.**Exclude Sections**— Specify bounds for the excluded data:In the

**Lower limit: exclude data**drop-down list, select`<=`

or`<`

and enter a scalar value in the field to the right. Depending on which operator you select, the app excludes from the fit any data values that are less than or equal to the scalar value, or less than the scalar value, respectively.In the

**Upper limit: exclude data**drop-down list, select`>=`

or`>`

and enter a scalar value in the field to the right. Depending on which operator you select, the app excludes from the fit any data values that are greater than or equal to the scalar value, or greater than the scalar value, respectively.

**OR**Click the

**Exclude Graphically**button to define the exclusion rule by displaying a plot of the values in a data set and selecting the bounds for the excluded data. For example, if you created the data set`My data`

as described in Create and Manage Data Sets, select it from the drop-down list next to**Exclude graphically**, and then click the**Exclude graphically**button. The app displays the values in`My data`

in a new window.To set a lower limit for the boundary of the excluded region, click

**Add Lower Limit**. The app displays a vertical line on the left side of the plot window. Move the line to the point you where you want the lower limit, as shown in the following figure.Move the vertical line to change the value displayed in the

**Lower limit: exclude data**field in the**Exclude**window.The value displayed corresponds to the

*x*-coordinate of the vertical line.Similarly, you can set the upper limit for the boundary of the excluded region by clicking

**Add Upper Limit**, and then moving the vertical line that appears at the right side of the plot window. After setting the lower and upper limits, click**Close**and return to the Exclude window.**Create Exclusion Rule**—Once you have set the lower and upper limits for the boundary of the excluded data, click**Create Exclusion Rule**to create the new rule. The name of the new rule appears in the**Existing exclusion rules**pane.Selecting an exclusion rule in the

**Existing exclusion rules**pane enables the following buttons:**Copy**— Creates a copy of the rule, which you can then modify. To save the modified rule under a different name, click**Create Exclusion Rule**.**View**— Opens a new window in which you can see the data points excluded by the rule. The following figure shows a typical example.The shaded areas in the plot graphically display which data points are excluded. The table to the right lists all data points. The shaded rows indicate excluded points:

**Rename**— Rename the rule.**Delete**— Delete the rule.

After you define an exclusion rule, you can use it when you fit a distribution to your data. The rule does not exclude points from the display of the data set.

Save your work in the current session, and then load it in a subsequent session, so that you can continue working where you left off.

To save the current session, from the **File** menu
in the main window, select `Save Session`

.
A dialog box opens and prompts you to enter a file name, for example`my_session.dfit`

.
Click **Save** to save the following items created
in the current session:

Data sets

Fits

Exclusion rules

Plot settings

Bin width rules

To load a previously saved session, from the **File** menu
in the main window, select `Load Session`

.
Enter the name of a previously saved session. Click **Open** to
restore the information from the saved session to the current session.

Use the `Generate Code`

option in the **File** to
create a file that:

Fits the distributions in the current session to any data vector in the MATLAB workspace.

Plots the data and the fits.

After you end the current session, you can use the file to create plots in a standard MATLAB figure window, without reopening the Distribution Fitter app.

As an example, if you created the fit described in Create a New Fit, do the following steps:

From the

**File**menu, select`Generate Code`

.In the MATLAB Editor window, choose

**File > Save as**. Save the file as`normal_fit.m`

in a folder on the MATLAB path.

You can then apply the function `normal_fit`

to
any vector of data in the MATLAB workspace. For example, the
following commands:

new_data = normrnd(4.1, 12.5, 100, 1); newfit = normal_fit(new_data) legend('New Data', 'My fit')

generate `newfit`

, a fitted normal distribution
of the data. The commands also generate a plot of the data and the
fit.

newfit = normal distribution mu = 3.19148 sigma = 12.5631

By default, the file labels the data in the legend using the
same name as the data set in the Distribution Fitter app. You can
change the label using the `legend`

command, as illustrated
by the preceding example.

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