fitoptions
Create or modify fit options object
Syntax
Description
fitOptions = fitoptionsfitOptions.
fitOptions = fitoptions(libraryModelName)
fitOptions = fitoptions(libraryModelName,Name,Value)Name,Value pair arguments.
fitOptions = fitoptions(fitType)fitType. Use this syntax to work with fit options for custom models.
fitOptions = fitoptions(Name,Value)Name,Value pair arguments.
newOptions = fitoptions(fitOptions,Name,Value)fitOptions and returns updated fit options in newOptions with new options specified by one or more Name,Value pair arguments.
newOptions = fitoptions(options1,options2)options1 and options2 in newOptions.
If
Methodagrees, the nonempty values for the properties inoptions2override the corresponding values inoptions1innewOptions.If
Methoddiffers,newOptionscontains theoptions1value forMethodand values fromoptions2forNormalize,Exclude, andWeights.
Examples
Create the default fit options object and set the option to center and scale the data before fitting.
options = fitoptions;
options.Normal = 'on'options =
basefitoptions with properties:
Normalize: 'on'
Exclude: []
Weights: []
Method: 'None'
options = fitoptions('gauss2')options =
nlsqoptions with properties:
StartPoint: []
Algorithm: 'Trust-Region'
DiffMinChange: 1.0000e-08
DiffMaxChange: 0.1000
Display: 'Notify'
MaxFunEvals: 600
MaxIter: 400
TolFun: 1.0000e-06
TolX: 1.0000e-06
Lower: [-Inf -Inf 0 -Inf -Inf 0]
Upper: []
ConstraintPoints: []
TolCon: 1.0000e-06
Robust: 'Off'
Normalize: 'off'
Exclude: []
Weights: []
Method: 'NonlinearLeastSquares'
Create fit options for a cubic polynomial and set center and scale and robust fitting options.
options = fitoptions('poly3', 'Normalize', 'on', 'Robust', 'Bisquare')
options =
llsqoptions with properties:
Lower: []
Upper: []
ConstraintPoints: []
TolCon: 1.0000e-06
Robust: 'Bisquare'
Normalize: 'on'
Exclude: []
Weights: []
Method: 'LinearLeastSquares'
options = fitoptions('Method', 'LinearLeastSquares')
options =
llsqoptions with properties:
Lower: []
Upper: []
ConstraintPoints: []
TolCon: 1.0000e-06
Robust: 'Off'
Normalize: 'off'
Exclude: []
Weights: []
Method: 'LinearLeastSquares'
Create a fitoptions object for a linear interpolant fit with nearest neighbor extrapolation.
linearoptions = fitoptions("linearinterp",ExtrapolationMethod="nearest")
linearoptions =
linearinterpoptions with properties:
ExtrapolationMethod: 'nearest'
Normalize: 'off'
Exclude: []
Weights: []
Method: 'LinearInterpolant'
Create a second fitoptions object for a cubic interpolant fit with nearest neighbor extrapolation.
cubicoptions = fitoptions("cubicinterp",ExtrapolationMethod="nearest")
cubicoptions =
cubicsplineinterpoptions with properties:
ExtrapolationMethod: 'nearest'
Normalize: 'off'
Exclude: []
Weights: []
Method: 'CubicSplineInterpolant'
You can use the fit options in linearoptions to create a linearinterp fit object using the fit function. Use cubicoptions to create a cubicinterp fit.
Modifying the default fit options object is useful when you want to set the Normalize, Exclude, or Weights properties, and then fit your data using the same options with different fitting methods. For example, the following uses the same fit options to fit different library model types.
load census options = fitoptions; options.Normalize = 'on'; f1 = fit(cdate,pop,'poly3',options); f2 = fit(cdate,pop,'exp1',options); f3 = fit(cdate,pop,'cubicspline',options)
f3 =
Cubic interpolating spline:
f3(x) = piecewise polynomial computed from p
with cubic extrapolation
where x is normalized by mean 1890 and std 62.05
Coefficients:
p = coefficient structure
Find the smoothing parameter. Data-dependent fit options such as the smooth parameter are returned in the third output argument of the fit function.
load census [f,gof,out] = fit(cdate,pop,'SmoothingSpline'); smoothparam = out.p
smoothparam = 0.0089
Modify the default smoothing parameter for a new fit.
options = fitoptions('Method','SmoothingSpline',... 'SmoothingParam',0.0098); [f,gof,out] = fit(cdate,pop,'SmoothingSpline',options);
Create a Gaussian fit, inspect the confidence intervals, and specify lower bound fit options to help the algorithm.
Create a noisy sum of two Gaussian peaks, one with a small width, and one with a large width.
a1 = 1; b1 = -1; c1 = 0.05; a2 = 1; b2 = 1; c2 = 50; x = (-10:0.02:10)'; gdata = a1*exp(-((x-b1)/c1).^2) + ... a2*exp(-((x-b2)/c2).^2) + ... 0.1*(rand(size(x))-.5); plot(x,gdata)
Fit the data using the two-term Gaussian library model.
gfit = fit(x,gdata,'gauss2') gfit =
General model Gauss2:
gfit(x) = a1*exp(-((x-b1)/c1)^2) + a2*exp(-((x-b2)/c2)^2)
Coefficients (with 95% confidence bounds):
a1 = -0.1451 (-1.485, 1.195)
b1 = 9.725 (-14.7, 34.15)
c1 = 7.117 (-15.84, 30.07)
a2 = 14.08 (-1.962e+04, 1.965e+04)
b2 = 607.4 (-3.197e+05, 3.209e+05)
c2 = 376 (-9.745e+04, 9.82e+04)
plot(gfit,x,gdata)
The algorithm is having difficulty, as indicated by the wide confidence intervals for several coefficients.
To help the algorithm, specify lower bounds for the nonnegative amplitudes a1 and a2 and widths c1, c2.
options = fitoptions('gauss2', 'Lower', [0 -Inf 0 0 -Inf 0]);
Alternatively, you can set properties of the fit options using the form options.Property = NewPropertyValue.
options = fitoptions('gauss2');
options.Lower = [0 -Inf 0 0 -Inf 0];Recompute the fit using the bound constraints on the coefficients.
gfit = fit(x,gdata,'gauss2',options) gfit =
General model Gauss2:
gfit(x) = a1*exp(-((x-b1)/c1)^2) + a2*exp(-((x-b2)/c2)^2)
Coefficients (with 95% confidence bounds):
a1 = 1.005 (0.966, 1.044)
b1 = -1 (-1.002, -0.9988)
c1 = 0.0491 (0.0469, 0.0513)
a2 = 0.9985 (0.9958, 1.001)
b2 = 0.8059 (0.3879, 1.224)
c2 = 50.6 (46.68, 54.52)
plot(gfit,x,gdata)
This is a much better fit. You can further improve the fit by assigning reasonable values to other properties in the fit options object.
Create fit options and set lower bounds.
options = fitoptions('gauss2', 'Lower', [0 -Inf 0 0 -Inf 0])
options =
nlsqoptions with properties:
StartPoint: []
Algorithm: 'Trust-Region'
DiffMinChange: 1.0000e-08
DiffMaxChange: 0.1000
Display: 'Notify'
MaxFunEvals: 600
MaxIter: 400
TolFun: 1.0000e-06
TolX: 1.0000e-06
Lower: [0 -Inf 0 0 -Inf 0]
Upper: []
ConstraintPoints: []
TolCon: 1.0000e-06
Robust: 'Off'
Normalize: 'off'
Exclude: []
Weights: []
Method: 'NonlinearLeastSquares'
Make a new copy of the fit options and modify the robust parameter.
newoptions = fitoptions(options, 'Robust','Bisquare')
newoptions =
nlsqoptions with properties:
StartPoint: []
Algorithm: 'Trust-Region'
DiffMinChange: 1.0000e-08
DiffMaxChange: 0.1000
Display: 'Notify'
MaxFunEvals: 600
MaxIter: 400
TolFun: 1.0000e-06
TolX: 1.0000e-06
Lower: [0 -Inf 0 0 -Inf 0]
Upper: []
ConstraintPoints: []
TolCon: 1.0000e-06
Robust: 'Bisquare'
Normalize: 'off'
Exclude: []
Weights: []
Method: 'NonlinearLeastSquares'
Combine fit options.
options2 = fitoptions(options, newoptions)
options2 =
nlsqoptions with properties:
StartPoint: []
Algorithm: 'Trust-Region'
DiffMinChange: 1.0000e-08
DiffMaxChange: 0.1000
Display: 'Notify'
MaxFunEvals: 600
MaxIter: 400
TolFun: 1.0000e-06
TolX: 1.0000e-06
Lower: [0 -Inf 0 0 -Inf 0]
Upper: []
ConstraintPoints: []
TolCon: 1.0000e-06
Robust: 'Bisquare'
Normalize: 'off'
Exclude: []
Weights: []
Method: 'NonlinearLeastSquares'
Create a linear model fit type.
lft = fittype({'x','sin(x)','1'})lft =
Linear model:
lft(a,b,c,x) = a*x + b*sin(x) + c
Get the fit options for the fit type lft.
fo = fitoptions(lft)
fo =
llsqoptions with properties:
Lower: []
Upper: []
ConstraintPoints: []
TolCon: 1.0000e-06
Robust: 'Off'
Normalize: 'off'
Exclude: []
Weights: []
Method: 'LinearLeastSquares'
Set the normalize fit option.
fo.Normalize = 'on'fo =
llsqoptions with properties:
Lower: []
Upper: []
ConstraintPoints: []
TolCon: 1.0000e-06
Robust: 'Off'
Normalize: 'on'
Exclude: []
Weights: []
Method: 'LinearLeastSquares'
