statset
Create statistics options structure
Syntax
Description
statset()
displays all fields of a statistics options structure
and their possible values.
statset(
displays the fields and
default values used by the function or object specified by
statfun
)statfun
.
statset(___)
returns a statistics options structure
using any of the previous input argument combinations.
statset(
specifies the field
values of the options structure using one or more name-value arguments. For example, you
can specify the maximum number of objective function evaluations and the weight function
for robust fitting.Name=Value
)
statset(
returns a copy of oldopts
,Name=Value
)oldopts
with field values changed to those
specified by the name-value arguments.
Examples
Display Values of Fields
Display all fields of a statistics options structure and their possible values.
statset()
Display: [ {off} | final | iter ] MaxFunEvals: [ positive integer ] MaxIter: [ positive integer ] TolBnd: [ positive scalar ] TolFun: [ positive scalar ] TolTypeFun: ['abs' |'rel'] TolX: [ positive scalar ] TolTypeX: ['abs' |'rel'] GradObj: [ {off} | on ] Jacobian: [ {off} | on ] DerivStep: [ positive scalar or vector ] FunValCheck: [ off | {on} ] RobustWgtFun: [ {[]} | bisquare | andrews | cauchy | fair | huber | logistic | talwar | welsch | function handle ] Tune: [ positive scalar ] UseParallel: [ {false} | true ] UseSubstreams: [ {false} | true ] Streams: [ {} | RandStream or cell array ] OutputFcn: [ {[]} | function handle or cell array ]
The output shows the fields and their possible values. You can use the output as a reference when creating a statistics options structure.
Create Default Options Structure for Statistics Function
Create an options structure containing the default options for the fitglm
function.
options = statset("fitglm")
options = struct with fields:
Display: 'off'
MaxFunEvals: []
MaxIter: 100
TolBnd: []
TolFun: []
TolTypeFun: []
TolX: 1.0000e-06
TolTypeX: []
GradObj: []
Jacobian: []
DerivStep: []
FunValCheck: []
Robust: []
RobustWgtFun: []
WgtFun: []
Tune: []
UseParallel: []
UseSubstreams: []
Streams: {}
OutputFcn: []
The output shows that the default options for fitglm
include values for the display, maximum number of iterations, and parameter termination tolerance. You can pass options
to other functions that accept statistics options structures.
Create Custom Options Structure
Create a custom options structure by using name-value arguments to specify individual field values.
options = statset(FunValCheck="on",TolX=1e-8)
options = struct with fields:
Display: []
MaxFunEvals: []
MaxIter: []
TolBnd: []
TolFun: []
TolTypeFun: []
TolX: 1.0000e-08
TolTypeX: []
GradObj: []
Jacobian: []
DerivStep: []
FunValCheck: 'on'
Robust: []
RobustWgtFun: []
WgtFun: []
Tune: []
UseParallel: []
UseSubstreams: []
Streams: {}
OutputFcn: []
You can pass options
to an eligible statistics function to set the parameter termination tolerance to 1e-8
and check for invalid values.
Modify Default Options
Create a statistics options structure that contains the default options for the function nbinfit
. Then modify the structure by specifying field values using name-value arguments.
oldopts = statset("nbinfit")
oldopts = struct with fields:
Display: 'off'
MaxFunEvals: 400
MaxIter: 200
TolBnd: 1.0000e-06
TolFun: 1.0000e-06
TolTypeFun: []
TolX: 1.0000e-06
TolTypeX: []
GradObj: []
Jacobian: []
DerivStep: []
FunValCheck: []
Robust: []
RobustWgtFun: []
WgtFun: []
Tune: []
UseParallel: []
UseSubstreams: []
Streams: {}
OutputFcn: []
oldopts
is a statistics options structure that contains the default values for the nbinfit
function. Note that the default value for TolX
is 1e-6
.
Create a copy of oldopts
and specify 1e-8
for TolX
.
opts = statset(oldopts,TolX=1e-8)
opts = struct with fields:
Display: 'off'
MaxFunEvals: 400
MaxIter: 200
TolBnd: 1.0000e-06
TolFun: 1.0000e-06
TolTypeFun: []
TolX: 1.0000e-08
TolTypeX: []
GradObj: []
Jacobian: []
DerivStep: []
FunValCheck: []
Robust: []
RobustWgtFun: []
WgtFun: []
Tune: []
UseParallel: []
UseSubstreams: []
Streams: {}
OutputFcn: []
The output shows that the fields in opts
have the same values as the fields in oldopts
, with the exception of TolX
.
Input Arguments
statfun
— Statistics function
character vector | string scalar
Statistics function, specified as a character vector or string scalar, as follows:
Name of a function or object shown in the table below.
When you specify one of these function names,
statset
returns the default options for the function."mlecustom"
— Specifies options for themle
function when you specify a custom probability distribution."parallel"
— Specifies options for functions that support parallelization.
Example: "bootci"
Data Types: char
| string
oldopts
— Old options
options structure
Old options, specified as a statistics options structure.
Data Types: struct
newopts
— New options
options structure
New options, specified as a statistics options structure.
Data Types: string
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Example: statset(MaxFunEvals=200,TolBnd=1e-6,TolX=1e-6)
specifies the
maximum number of objective function evaluations as 200
, and specifies
both the parameter bound tolerance and parameter termination tolerance as
1e-6
.
DerivStep
— Relative difference
positive scalar (default) | vector of positive scalars
Relative difference for finite difference calculations, specified as a positive
scalar or a vector of positive scalars. If DerivStep
is a vector
of positive scalars, it must be the same size as the vector of parameters estimated by
the function using the options structure.
Example: DerivStep=0.1
Data Types: single
| double
Display
— Option to display algorithm information
"off"
| "final"
| "iter"
Option to display algorithm information, specified as one of the following:
"off"
— Display no information."final"
— Display the final output."iter"
— Display iterative output to the command window for some functions, and display the final output otherwise.
Example: Display="iter"
Data Types: char
| string
FunValCheck
— Flag to check for invalid values
"off"
| "on"
Flag to check for invalid values returned by the objective function, specified as
"off"
or "on"
. Examples of invalid values
include NaN
and Inf
.
Example: FunValCheck="on"
Data Types: char
| string
GradObj
— Flag to return gradient
"off"
| "on"
Flag to return a gradient vector as an additional output of the objective
function, specified as "off"
or "on"
.
Example: GradObj="on"
Data Types: char
| string
Jacobian
— Flag to return Jacobian
"off"
| "on"
Flag to return a Jacobian as an additional output of the objective function,
specified as "off"
or "on"
.
Example: Jacobian="on"
Data Types: char
| string
MaxFunEvals
— Maximum number of objective function evaluations
positive integer
Maximum number of objective function evaluations, specified as a positive integer.
Example: MaxFunEvals=200
Data Types: single
| double
MaxIter
— Maximum number of iterations
positive integer
Maximum number of iterations, specified as a positive integer.
Example: MaxIter=100
Data Types: single
| double
OutputFcn
— Output function
function handle | cell array of function handles
Output function, specified as a function handle or cell array of function handles.
Example: OutputFcn=@myfun
Data Types: cell
| function_handle
RobustWgtFun
— Weight function for robust fitting
character vector | string scalar
Weight function for robust fitting, specified as a character vector or string scalar for one of the following function names.
Weight Function | Equation | Default Tuning Constant |
---|---|---|
"andrews" | w = (abs(r)<pi) .* sin(r) ./ r | 1.339 |
"bisquare" (default) | w = (abs(r)<1) .* (1 - r.^2).^2 | 4.685 |
"cauchy" | w = 1 ./ (1 + r.^2) | 2.385 |
"fair" | w = 1 ./ (1 + abs(r)) | 1.400 |
"huber" | w = 1 ./ max(1, abs(r)) | 1.345 |
"logistic" | w = tanh(r) ./ r | 1.205 |
"talwar" | w = 1 * (abs(r)<1) | 2.795 |
"welsch" | w = exp(-(r.^2)) | 2.985 |
Example: RobustWgtFun="logistic"
Data Types: char
| string
Streams
— Random number stream
RandStream
object | cell array of RandStream
objects
Random number stream, specified as a RandStream
object or cell array of RandStream
objects.
If UseSubstreams
is true
, then
Streams
cannot be a cell array of RandStream
objects. If UseSubstreams
is false
and
UseParallel
is true
, then
Streams
must be empty or have the same length as the number of
processors in the calculation. The number of processors is a scalar or is equal to the
size of the parallel pool (if one is open).
TolBnd
— Parameter bound tolerance
positive scalar
Parameter bound tolerance, specified as a positive scalar.
Example: TolBnd=1e-6
Data Types: single
| double
TolFun
— Objective function termination tolerance
positive scalar
Objective function termination tolerance, specified as a positive scalar.
Example: TolFun=1.5
Data Types: single
| double
TolTypeFun
— Type of objective function termination tolerance
"abs"
| "rel"
Type of objective function termination tolerance, specified as
"abs"
or "rel"
. When
TolTypeFun
is "abs"
,
TolFun
is an absolute tolerance. When
TolTypeFun
is "rel"
,
TolFun
is a relative tolerance.
Example: TolTypeFun="rel"
Data Types: char
| string
TolX
— Parameter termination tolerance
positive scalar
Parameter termination tolerance, specified as a positive scalar.
Example: TolX=1e-6
Data Types: single
| double
TolTypeX
— Type of parameter termination tolerance
"abs"
| "rel"
Type of parameter termination tolerance, specified as "abs"
or
"rel"
. When TolTypeX
is
"abs"
, TolX
is an absolute tolerance. When
TolTypeX
is "rel"
, TolX
is a relative tolerance.
Example: TolTypeX="rel"
Data Types: char
| string
Tune
— Tuning constant for robust fitting
positive scalar
Tuning constant for robust fitting, specified as a positive scalar. The default
value of Tune
depends on the weight function specified by
RobustWgtFun
.
Example: Tune=1.5
Data Types: single
| double
UseParallel
— Flag to use parallel computing
false
or 0
| true
or 1
Flag to use parallel computing functionality (requires Parallel Computing Toolbox™), specified as a numeric or logical 0
(false
) or 1
(true
). To use
parallel computing when UseParallel
is true
,
Parallel Computing Toolbox must be installed and a parallel pool must be open.
Example: UseParallel=true
Data Types: logical
UseSubstreams
— Flag to use substreams
false
or 0
| true
or 1
Flag for the random number generator to use substreams, specified as a numeric or
logical 0
(false
) or 1
(true
).
When UseSubstreams
is true
, functions
using the options
output use the Substream
property of the RandStream
object. You can specify the
RandStream
object by using the Streams
name-value argument.
Using substreams helps to generate reproducible random number streams in parallel and serial mode computations. For more information, see Reproducibility in Parallel Statistical Computations.
Example: UseSubstreams=true
Data Types: logical
Version History
Introduced before R2006a
See Also
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