statset - Create statistics options structure

Syntax

statset statset(statfun) options = statset(...) options = statset(fieldname1,val1,fieldname2,val2,...) options = statset(oldopts,fieldname1,val1,fieldname2,val2,...) options = statset(oldopts,newopts)

Description

statset with no input arguments and no output arguments displays all fields of a statistics options structure and their possible values.

statset(statfun) displays fields and default values used by the Statistics Toolbox function statfun. Specify statfun using a string name or a function handle.

options = statset(...) creates a statistics options structure options. With no input arguments, all fields of the options structure are an empty array ([]). With a specified statfun, function-specific fields are default values and the remaining fields are []. Function-specific fields set to [] indicate that the function is to use its default value for that parameter. For available options, see Inputs.

options = statset(fieldname1,val1,fieldname2,val2,...) creates an options structure in which the named fields have the specified values. Any unspecified values are []. Use strings for field names. For fields that are string-valued, you must input the complete string for the value. If you provide an invalid string for a value, statset uses the default.

options = statset(oldopts,fieldname1,val1,fieldname2,val2,...) creates a copy of oldopts with the named parameters changed to the specified values.

options = statset(oldopts,newopts) combines an existing options structure, oldopts, with a new options structure, newopts. Any parameters in newopts with nonempty values overwrite corresponding parameters in oldopts.

Inputs

`DerivStep`	Relative difference used in finite difference derivative calculations. A positive scalar, or a vector of positive scalars the same size as the vector of parameters estimated by the Statistics Toolbox function using the options structure.
`Display`	Amount of information displayed by the algorithm. `'off'` — Displays no information. `'final'` — Displays the final output. `'iter'` — Displays iterative output to the command window for some functions; otherwise displays the final output.
`FunValCheck`	Check for invalid values, such as `NaN` or `Inf`, from the objective function. `'off'` `'on'`
`GradObj`	Flags whether the objective function returns a gradient vector as a second output. `'off'` `'on'`
`Jacobian`	Flags whether the objective function returns a Jacobian as a second output. `'off'` `'on'`
`MaxFunEvals`	Maximum number of objective function evaluations allowed. Positive integer.
`MaxIter`	Maximum number of iterations allowed. Positive integer.
`OutputFcn`	The solver calls all output functions after each iteration. Function handle specified using @ a cell array with function handles an empty array (default)
`Robust`	Invoke robust fitting option. `'off'` `'on'`
`TolBnd`	Parameter bound tolerance. Positive scalar.
`TolFun`	Termination tolerance for the objective function value. Positive scalar.
`TolTypeFun`	Use `TolFun` for absolute or relative objective function tolerances. `'abs'` `'rel'`
`TolTypeX`	Use `TolX` for absolute or relative parameter tolerances. `'abs'` `'rel'`
`TolX`	Termination tolerance for the parameters. Positive scalar.
`Tune`	The tuning constant used in robust fitting to normalize the residuals before applying the weight function. The default value depends upon the weight function. This parameter is necessary if you specify the weight function as a function handle. Positive scalar.
`WgtFun`	A weight function for robust fitting. Valid only when `Robust` is `'on'`. Can also be a function handle that accepts a normalized residual as input and returns the robust weights as output. `'bisquare'` `'andrews'` `'cauchy'` `'fair'` `'huber'` `'logistic'` `'talwar'` `'welsch'`

Examples

Suppose you want to change the default parameter values for the function evfit, which fits an extreme value distribution to data. The defaults parameter values are:

statset('evfit')
ans = 
        Display: 'off'
    MaxFunEvals: []
        MaxIter: []
         TolBnd: []
         TolFun: []
           TolX: 1.0000e-006
        GradObj: []
      DerivStep: []
    FunValCheck: []
         Robust: []
         WgtFun: []
           Tune: []

The only parameters that evfit uses are Display and TolX. To create an options structure with the value of TolX set to 1e-8, enter:

options = statset('TolX',1e-8)
% Pass options to evfit:
mu = 1;
sigma = 1;
data = evrnd(mu,sigma,1,100);

paramhat = evfit(data,[],[],[],options)

statset - Create statistics options structure

Syntax

Description

Inputs

Examples

See Also

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