fit
Fit curve or surface to data
Syntax
Description
creates a fit to the data using the algorithm options specified by the
fitobject
= fit(x
,y
,fitType
,fitOptions
)fitOptions
object.
creates a fit to the data using the library model fitobject
= fit(x
,y
,fitType
,Name,Value
)fitType
with additional options specified by one or more Name,Value
pair arguments. Use fitoptions
to display
available property names and default values for the specific library
model.
Examples
Fit a Quadratic Curve
Load some data, fit a quadratic curve to variables cdate
and pop
, and plot the fit and data.
load census; f=fit(cdate,pop,'poly2')
f = Linear model Poly2: f(x) = p1*x^2 + p2*x + p3 Coefficients (with 95% confidence bounds): p1 = 0.006541 (0.006124, 0.006958) p2 = -23.51 (-25.09, -21.93) p3 = 2.113e+04 (1.964e+04, 2.262e+04)
plot(f,cdate,pop)
For a list of library model names, see fitType
.
Fit a Polynomial Surface
Load some data and fit a polynomial surface of degree 2 in x
and degree 3 in y
. Plot the fit and data.
load franke sf = fit([x, y],z,'poly23')
Linear model Poly23: sf(x,y) = p00 + p10*x + p01*y + p20*x^2 + p11*x*y + p02*y^2 + p21*x^2*y + p12*x*y^2 + p03*y^3 Coefficients (with 95% confidence bounds): p00 = 1.118 (0.9149, 1.321) p10 = -0.0002941 (-0.000502, -8.623e-05) p01 = 1.533 (0.7032, 2.364) p20 = -1.966e-08 (-7.084e-08, 3.152e-08) p11 = 0.0003427 (-0.0001009, 0.0007863) p02 = -6.951 (-8.421, -5.481) p21 = 9.563e-08 (6.276e-09, 1.85e-07) p12 = -0.0004401 (-0.0007082, -0.0001721) p03 = 4.999 (4.082, 5.917)
plot(sf,[x,y],z)
Fit a Surface Using Variables in a MATLAB Table
Load the franke
data and convert it to a MATLAB® table.
load franke
T = table(x,y,z);
Specify the variables in the table as inputs to the fit
function, and plot the fit.
f = fit([T.x, T.y],T.z,'linearinterp');
plot( f, [T.x, T.y], T.z )
Create Fit Options and Fit Type Before Fitting
Load and plot the data, create fit options and fit type using the fittype
and fitoptions
functions, then create and plot the fit.
Load and plot the data in census.mat
.
load census plot(cdate,pop,'o')
Create a fit options object and a fit type for the custom nonlinear model , where a and b are coefficients and n is a problem-dependent parameter.
fo = fitoptions('Method','NonlinearLeastSquares',... 'Lower',[0,0],... 'Upper',[Inf,max(cdate)],... 'StartPoint',[1 1]); ft = fittype('a*(x-b)^n','problem','n','options',fo);
Fit the data using the fit options and a value of n = 2.
[curve2,gof2] = fit(cdate,pop,ft,'problem',2)
curve2 = General model: curve2(x) = a*(x-b)^n Coefficients (with 95% confidence bounds): a = 0.006092 (0.005743, 0.006441) b = 1789 (1784, 1793) Problem parameters: n = 2
gof2 = struct with fields:
sse: 246.1543
rsquare: 0.9980
dfe: 19
adjrsquare: 0.9979
rmse: 3.5994
Fit the data using the fit options and a value of n = 3.
[curve3,gof3] = fit(cdate,pop,ft,'problem',3)
curve3 = General model: curve3(x) = a*(x-b)^n Coefficients (with 95% confidence bounds): a = 1.359e-05 (1.245e-05, 1.474e-05) b = 1725 (1718, 1731) Problem parameters: n = 3
gof3 = struct with fields:
sse: 232.0058
rsquare: 0.9981
dfe: 19
adjrsquare: 0.9980
rmse: 3.4944
Plot the fit results with the data.
hold on plot(curve2,'m') plot(curve3,'c') legend('Data','n=2','n=3') hold off
Fit a Cubic Polynomial Specifying Normalize and Robust Options
Load some data and fit and plot a cubic polynomial with center and scale (Normalize
) and robust fitting options.
load census; f=fit(cdate,pop,'poly3','Normalize','on','Robust','Bisquare')
f = Linear model Poly3: f(x) = p1*x^3 + p2*x^2 + p3*x + p4 where x is normalized by mean 1890 and std 62.05 Coefficients (with 95% confidence bounds): p1 = -0.4619 (-1.895, 0.9707) p2 = 25.01 (23.79, 26.22) p3 = 77.03 (74.37, 79.7) p4 = 62.81 (61.26, 64.37)
plot(f,cdate,pop)
Fit a Curve Defined by a File
Define a function in a file and use it to create a fit type and fit a curve.
Define a function in a MATLAB® file.
function y = piecewiseLine(x,a,b,c,d,k) % PIECEWISELINE A line made of two pieces % that is not continuous. y = zeros(size(x)); % This example includes a for-loop and if statement % purely for example purposes. for i = 1:length(x) if x(i) < k, y(i) = a + b.* x(i); else y(i) = c + d.* x(i); end end end
Save the file.
Define some data, create a fit type specifying the function
piecewiseLine
, create a fit using the fit type
ft
, and plot the results.
x = [0.81;0.91;0.13;0.91;0.63;0.098;0.28;0.55;... 0.96;0.96;0.16;0.97;0.96]; y = [0.17;0.12;0.16;0.0035;0.37;0.082;0.34;0.56;... 0.15;-0.046;0.17;-0.091;-0.071]; ft = fittype( 'piecewiseLine( x, a, b, c, d, k )' ) f = fit( x, y, ft, 'StartPoint', [1, 0, 1, 0, 0.5] ) plot( f, x, y )
Exclude Points from Fit
Load some data and fit a custom equation specifying points to exclude. Plot the results.
Load data and define a custom equation and some start points.
[x, y] = titanium;
gaussEqn = 'a*exp(-((x-b)/c)^2)+d'
gaussEqn = 'a*exp(-((x-b)/c)^2)+d'
startPoints = [1.5 900 10 0.6]
startPoints = 1×4
1.5000 900.0000 10.0000 0.6000
Create two fits using the custom equation and start points, and define two different sets of excluded points, using an index vector and an expression. Use Exclude
to remove outliers from your fit.
f1 = fit(x',y',gaussEqn,'Start', startPoints, 'Exclude', [1 10 25])
f1 = General model: f1(x) = a*exp(-((x-b)/c)^2)+d Coefficients (with 95% confidence bounds): a = 1.493 (1.432, 1.554) b = 897.4 (896.5, 898.3) c = 27.9 (26.55, 29.25) d = 0.6519 (0.6367, 0.6672)
f2 = fit(x',y',gaussEqn,'Start', startPoints, 'Exclude', x < 800)
f2 = General model: f2(x) = a*exp(-((x-b)/c)^2)+d Coefficients (with 95% confidence bounds): a = 1.494 (1.41, 1.578) b = 897.4 (896.2, 898.7) c = 28.15 (26.22, 30.09) d = 0.6466 (0.6169, 0.6764)
Plot both fits.
plot(f1,x,y)
title('Fit with data points 1, 10, and 25 excluded')
figure
plot(f2,x,y)
title('Fit with data points excluded such that x < 800')
Exclude Points and Plot Fit Showing Excluded Data
You can define the excluded points as variables before supplying them as inputs to the fit function. The following steps recreate the fits in the previous example and allow you to plot the excluded points as well as the data and the fit.
Load data and define a custom equation and some start points.
[x, y] = titanium;
gaussEqn = 'a*exp(-((x-b)/c)^2)+d'
gaussEqn = 'a*exp(-((x-b)/c)^2)+d'
startPoints = [1.5 900 10 0.6]
startPoints = 1×4
1.5000 900.0000 10.0000 0.6000
Define two sets of points to exclude, using an index vector and an expression.
exclude1 = [1 10 25]; exclude2 = x < 800;
Create two fits using the custom equation, startpoints, and the two different excluded points.
f1 = fit(x',y',gaussEqn,'Start', startPoints, 'Exclude', exclude1); f2 = fit(x',y',gaussEqn,'Start', startPoints, 'Exclude', exclude2);
Plot both fits and highlight the excluded data.
plot(f1,x,y,exclude1)
title('Fit with data points 1, 10, and 25 excluded')
figure;
plot(f2,x,y,exclude2)
title('Fit with data points excluded such that x < 800')
For a surface fitting example with excluded points, load some surface data and create and plot fits specifying excluded data.
load franke f1 = fit([x y],z,'poly23', 'Exclude', [1 10 25]); f2 = fit([x y],z,'poly23', 'Exclude', z > 1); figure plot(f1, [x y], z, 'Exclude', [1 10 25]); title('Fit with data points 1, 10, and 25 excluded')
figure plot(f2, [x y], z, 'Exclude', z > 1); title('Fit with data points excluded such that z > 1')
Fit a Smoothing Spline Curve and Return Goodness-of-Fit Information
Load some data and fit a smoothing spline curve through variables month
and pressure
, and return goodness of fit information and the output structure. Plot the fit and the residuals against the data.
load enso; [curve, goodness, output] = fit(month,pressure,'smoothingspline'); plot(curve,month,pressure); xlabel('Month'); ylabel('Pressure');
Plot the residuals against the x-data (month
).
plot( curve, month, pressure, 'residuals' ) xlabel( 'Month' ) ylabel( 'Residuals' )
Use the data in the output
structure to plot the residuals against the y-data (pressure
).
plot( pressure, output.residuals, '.' ) xlabel( 'Pressure' ) ylabel( 'Residuals' )
Fit a Single-Term Exponential
Generate data with an exponential trend, and then fit the data using the first equation in the curve fitting library of exponential models (a single-term exponential). Plot the results.
x = (0:0.2:5)';
y = 2*exp(-0.2*x) + 0.5*randn(size(x));
f = fit(x,y,'exp1');
plot(f,x,y)
Fit a Custom Model Using an Anonymous Function
You can use anonymous functions to make it easier to pass
other data into the fit
function.
Load data and set Emax
to 1
before defining your anonymous function:
data = importdata( 'OpioidHypnoticSynergy.txt' );
Propofol = data.data(:,1);
Remifentanil = data.data(:,2);
Algometry = data.data(:,3);
Emax = 1;
Define the model equation as an anonymous function:
Effect = @(IC50A, IC50B, alpha, n, x, y) ... Emax*( x/IC50A + y/IC50B + alpha*( x/IC50A )... .* ( y/IC50B ) ).^n ./(( x/IC50A + y/IC50B + ... alpha*( x/IC50A ) .* ( y/IC50B ) ).^n + 1);
Use the anonymous function Effect
as an input to
the fit
function, and plot the results:
AlgometryEffect = fit( [Propofol, Remifentanil], Algometry, Effect, ... 'StartPoint', [2, 10, 1, 0.8], ... 'Lower', [-Inf, -Inf, -5, -Inf], ... 'Robust', 'LAR' ) plot( AlgometryEffect, [Propofol, Remifentanil], Algometry )
For more examples using anonymous functions and other custom models
for fitting, see the fittype
function.
Find Coefficient Order to Set Start Points and Bounds
For the properties Upper
, Lower
, and StartPoint
, you need to find the order of the entries for coefficients.
Create a fit type.
ft = fittype('b*x^2+c*x+a');
Get the coefficient names and order using the coeffnames
function.
coeffnames(ft)
ans = 3x1 cell
{'a'}
{'b'}
{'c'}
Note that this is different from the order of the coefficients in the expression used to create ft
with fittype
.
Load data, create a fit and set the start points.
load enso fit(month,pressure,ft,'StartPoint',[1,3,5])
ans = General model: ans(x) = b*x^2+c*x+a Coefficients (with 95% confidence bounds): a = 10.94 (9.362, 12.52) b = 0.0001677 (-7.985e-05, 0.0004153) c = -0.0224 (-0.06559, 0.02079)
This assigns initial values to the coefficients as follows: a = 1
, b = 3
, c = 5
.
Alternatively, you can get the fit options and set start points and lower bounds, then refit using the new options.
options = fitoptions(ft)
options = Normalize: 'off' Exclude: [] Weights: [] Method: 'NonlinearLeastSquares' Robust: 'Off' StartPoint: [1x0 double] Lower: [1x0 double] Upper: [1x0 double] Algorithm: 'Trust-Region' DiffMinChange: 1.0000e-08 DiffMaxChange: 0.1000 Display: 'Notify' MaxFunEvals: 600 MaxIter: 400 TolFun: 1.0000e-06 TolX: 1.0000e-06
options.StartPoint = [10 1 3]; options.Lower = [0 -Inf 0]; fit(month,pressure,ft,options)
ans = General model: ans(x) = b*x^2+c*x+a Coefficients (with 95% confidence bounds): a = 10.23 (9.448, 11.01) b = 4.335e-05 (-1.82e-05, 0.0001049) c = 5.523e-12 (fixed at bound)
Input Arguments
x
— Data to fit
matrix
Data to fit, specified as a matrix with either one (curve fitting) or
two (surface fitting) columns. You can specify variables in a
MATLAB table using tablename.varname
. Cannot
contain Inf
or NaN
. Only the real
parts of complex data are used in the fit.
Example: x
Example: [x,y]
Data Types: double
y
— Data to fit
vector
Data to fit, specified as a column vector with the same number of rows
as x
. You can specify a variable in a MATLAB table using tablename.varname
. Cannot
contain Inf
or NaN
. Only the real
parts of complex data are used in the fit.
Use prepareCurveData
or
prepareSurfaceData
if your data is not in
column vector form.
Data Types: double
z
— Data to fit
vector
Data to fit, specified as a column vector with the same number of rows
as x
. You can specify a variable in a MATLAB table using tablename.varname
. Cannot
contain Inf
or NaN
. Only the real
parts of complex data are used in the fit.
Use prepareSurfaceData
if your data is not in
column vector form. For example, if you have 3 matrices, or if your data
is in grid vector form, where length(X) = n, length(Y) =
m
and size(Z) = [m,n]
.
Data Types: double
fitType
— Model type to fit
character vector | string scalar | string array | cell array of character vectors | anonymous function | fittype
Model type to fit, specified as a character vector or string scalar
representing a library model name or MATLAB expression, a cell array or string array of linear models
terms, an anonymous function, or a fittype
constructed with the fittype
function. You
can use any of the valid first inputs to fittype
as an input to
fit
.
For a list of library model names, see Model Names and Equations. This table shows some common examples.
Library Model Name | Description |
---|---|
| Linear polynomial curve |
| Linear polynomial surface |
| Quadratic polynomial curve |
| Piecewise linear interpolation |
| Piecewise cubic interpolation |
| Smoothing spline (curve) |
| Local linear regression (surface) |
To fit custom models, use a MATLAB expression, a cell array of linear model terms, an
anonymous function, or create a fittype
with the
fittype
function and
use this as the fitType
argument. For an example,
see Fit a Custom Model Using an Anonymous Function. For
examples of linear model terms, see the fitType
function.
Example: 'poly2'
fitOptions
— Algorithm options
fitoptions
Algorithm options constructed using the fitoptions
function.
This is an alternative to specifying name-value pair arguments for fit
options.
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.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: 'Lower',[0,0],'Upper',[Inf,max(x)],'StartPoint',[1
1]
specifies fitting method, bounds, and start
points.
Normalize
— Option to center and scale data
'off'
(default) | 'on'
Option to center and scale the data, specified as the
comma-separated pair consisting of 'Normalize'
and 'on'
or 'off'
.
Data Types: char
Exclude
— Points to exclude from fit
expression | index vector | logical vector | empty
Points to exclude from the fit, specified as the comma-separated
pair consisting of 'Exclude'
and one of:
An expression describing a logical vector, e.g.,
x > 10
.A vector of integers indexing the points you want to exclude, e.g.,
[1 10 25]
.A logical vector for all data points where
true
represents an outlier, created byexcludedata
.
For an example, see Exclude Points from Fit.
Data Types: logical
| double
problem
— Values to assign to problem-dependent constants
cell array | double
Values to assign to the problem-dependent constants, specified as
the comma-separated pair consisting of 'problem'
and a cell array with one element per problem dependent constant.
For details, see fittype
.
Data Types: cell
| double
SmoothingParam
— Smoothing parameter
scalar value in the range (0,1)
Smoothing parameter, specified as the comma-separated pair
consisting of 'SmoothingParam'
and a scalar value
between 0 and 1. The default value depends on the data set. Only
available if the fit type is
smoothingspline
.
Data Types: double
Span
— Proportion of data points to use in local regressions
0.25 (default) | scalar value in the range (0,1)
Proportion of data points to use in local regressions, specified
as the comma-separated pair consisting of 'Span'
and a scalar value between 0 and 1. Only available if the fit type
is lowess
or loess
.
Data Types: double
Robust
— Robust linear least-squares fitting method
'off'
(default) | LAR
| Bisquare
Robust linear least-squares fitting method, specified as the
comma-separated pair consisting of 'Robust'
and
one of these values:
'LAR'
specifies the least absolute residual method.'Bisquare'
specifies the bisquare weights method.
Available when the fit type
Method
is
LinearLeastSquares
or
NonlinearLeastSquares
.
Data Types: char
Lower
— Lower bounds on coefficients to be fitted
[ ] (default) | vector
Lower bounds on the coefficients to be fitted, specified as the
comma-separated pair consisting of 'Lower'
and a
vector. The default value is an empty vector, indicating that the
fit is unconstrained by lower bounds. If bounds are specified, the
vector length must equal the number of coefficients. Find the order
of the entries for coefficients in the vector value by using the
coeffnames
function. For an example, see Find Coefficient Order to Set Start Points and Bounds.
Individual unconstrained lower bounds can be specified by
-Inf
.
Available when the Method
is
LinearLeastSquares
or
NonlinearLeastSquares
.
Data Types: double
Upper
— Upper bounds on coefficients to be fitted
[ ] (default) | vector
Upper bounds on the coefficients to be fitted, specified as the
comma-separated pair consisting of 'Upper'
and a
vector. The default value is an empty vector, indicating that the
fit is unconstrained by upper bounds. If bounds are specified, the
vector length must equal the number of coefficients. Find the order
of the entries for coefficients in the vector value by using the
coeffnames
function. For an example, see Find Coefficient Order to Set Start Points and Bounds.
Individual unconstrained upper bounds can be specified by
+Inf
.
Available when the Method
is
LinearLeastSquares
or
NonlinearLeastSquares
.
Data Types: logical
StartPoint
— Initial values for the coefficients
[ ] (default) | vector
Initial values for the coefficients, specified as the
comma-separated pair consisting of 'StartPoint'
and a vector. Find the order of the entries for coefficients in the
vector value by using the coeffnames
function. For an example, see Find Coefficient Order to Set Start Points and Bounds.
If no start points (the default value of an empty vector) are
passed to the fit
function,
starting points for some library models are determined
heuristically. For rational and Weibull models, and all custom
nonlinear models, the toolbox selects default initial values for
coefficients uniformly at random from the interval (0,1). As a
result, multiple fits using the same data and model might lead to
different fitted coefficients. To avoid this, specify initial values
for coefficients with a fitoptions
object
or a vector value for the StartPoint
value.
Available when the Method
is
NonlinearLeastSquares
.
Data Types: double
Algorithm
— Algorithm to use for fitting procedure
'Trust-Region' (default) | 'Levenberg-Marquardt'
Algorithm to use for the fitting procedure, specified as the
comma-separated pair consisting of 'Algorithm'
and either 'Levenberg-Marquardt'
or
'Trust-Region'
.
Available when the Method
is
NonlinearLeastSquares
.
Data Types: char
DiffMaxChange
— Maximum change in coefficients for finite difference gradients
0.1 (default)
Maximum change in coefficients for finite difference gradients,
specified as the comma-separated pair consisting of
'DiffMaxChange'
and a scalar.
Available when the Method
is
NonlinearLeastSquares
.
Data Types: double
DiffMinChange
— Minimum change in coefficients for finite difference gradients
10–8 (default)
Minimum change in coefficients for finite difference gradients,
specified as the comma-separated pair consisting of
'DiffMinChange'
and a scalar.
Available when the Method
is
NonlinearLeastSquares
.
Data Types: double
Display
— Display option in Command Window
'notify'
(default) | 'final'
| 'iter'
| 'off'
Display option in the command window, specified as the
comma-separated pair consisting of 'Display'
and
one of these options:
'notify'
displays output only if the fit does not converge.'final'
displays only the final output.'iter'
displays output at each iteration.'off'
displays no output.
Available when the Method
is
NonlinearLeastSquares
.
Data Types: char
MaxFunEvals
— Maximum number of evaluations of model allowed
600
(default)
Maximum number of evaluations of the model allowed, specified as
the comma-separated pair consisting of
'MaxFunEvals'
and a scalar.
Available when the Method
is
NonlinearLeastSquares
.
Data Types: double
MaxIter
— Maximum number of iterations allowed for fit
400
(default)
Maximum number of iterations allowed for the fit, specified as the
comma-separated pair consisting of 'MaxIter'
and
a scalar.
Available when the Method
is
NonlinearLeastSquares
.
Data Types: double
TolFun
— Termination tolerance on model value
10–6 (default)
Termination tolerance on the model value, specified as the
comma-separated pair consisting of 'TolFun'
and a
scalar.
Available when the Method
is
NonlinearLeastSquares
.
Data Types: double
TolX
— Termination tolerance on coefficient values
10–6 (default)
Termination tolerance on the coefficient values, specified as the
comma-separated pair consisting of 'TolX'
and a
scalar.
Available when the Method
is
NonlinearLeastSquares
.
Data Types: double
Output Arguments
fitobject
— Fit result
cfit
| sfit
Fit result, returned as a cfit
(for curves) or
sfit
(for surfaces)
object. See Fit Postprocessing for functions for plotting, evaluating,
calculating confidence intervals, integrating, differentiating, or
modifying your fit object.
gof
— Goodness-of-fit statistics
gof
structure
Goodness-of-fit statistics, returned as the gof
structure including the fields in this table.
Field | Value |
---|---|
| Sum of squares due to error |
| R-squared (coefficient of determination) |
| Degrees of freedom in the error |
| Degree-of-freedom adjusted coefficient of determination |
| Root mean squared error (standard error) |
output
— Fitting algorithm information
output
structure
Fitting algorithm information, returned as the
output
structure containing information
associated with the fitting algorithm.
Fields depend on the algorithm. For example, the
output
structure for nonlinear least-squares
algorithms includes the fields shown in this table.
Field | Value |
---|---|
| Number of observations (response values) |
| Number of unknown parameters (coefficients) to fit |
| Vector of residuals |
| Jacobian matrix |
| Describes the exit condition of the algorithm. Positive flags indicate convergence, within tolerances. Zero flags indicate that the maximum number of function evaluations or iterations was exceeded. Negative flags indicate that the algorithm did not converge to a solution. |
| Number of iterations |
| Number of function evaluations |
| Measure of first-order optimality (absolute maximum of gradient components) |
| Fitting algorithm employed |
Version History
Introduced before R2006a
See Also
Apps
Functions
fittype
|fitoptions
|prepareCurveData
|prepareSurfaceData
|feval
|plot
|confint
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