plot
Plot cfit or sfit object
Syntax
Description
Note
These syntaxes are available for surfaces, or sfit
objects.
plot(
plots the sfit)sfit object over the range of the current axes, if
any, or otherwise over the range stored in the fit.
H = plot(
selects which way to plot the surface fit object sfit, ..., Name,Value)sfit.
H = plot( returns a vector
of handles of the plotted objects.sfit, ...)
plot(cfit,
plots the predictor and response data using the color, marker symbol, and line
style specified by the x,y,DataLineSpec)DataLineSpec formatting character
vector.
plot(
plots cfit,FitLineSpec,x,y,DataLineSpec)fun using the color, marker symbol, and line style
specified by the FitLineSpec formatting character vector, and
plots x and y using the color, marker
symbol, and line style specified by the DataLineSpec
formatting character vector.
plot(
plots data indicated by cfit,x,y,outliers)outliers in a different color.
outliers can be 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], or a logical
array where true represents an outlier. You can create the
array with excludedata.
plot(
plots cfit,x,y,outliers,OutlierLineSpec)outliers using the color, marker symbol, and line style
specified by the OutlierLineSpec.
plot(...,
uses the plot type specified by ptype,...)ptype.
Note
This syntax is available for both curves and surfaces.
Plot types can be single or multiple, with multiple plot types
specified as a cell array of character vectors. With a single plot type,
plot draws to the current axes and can be used
with commands like hold and subplot. With multiple
plot types, plot creates subplots for each plot
type.
H = plot(...)
Examples
Plot data, outliers, and the results of the fits using the plot function
This example shows how to plot the data, the outliers, and the results of three fit objects with different colors and line styles.
Create a baseline sinusoidal signal.
xdata = (0:0.1:2*pi)'; y0 = sin(xdata);
Add noise to the signal with non-constant variance.
% Response-dependent Gaussian noise gnoise = y0.*randn(size(y0)); % Salt-and-pepper noise spnoise = zeros(size(y0)); p = randperm(length(y0)); sppoints = p(1:round(length(p)/5)); spnoise(sppoints) = 5*sign(y0(sppoints)); ydata = y0 + gnoise + spnoise;
Fit the noisy data with a baseline sinusoidal model.
f = fittype('a*sin(b*x)'); fit1 = fit(xdata,ydata,f,'StartPoint',[1 1]);
Identify “outliers” as points at a distance greater than 1.5 standard deviations from the baseline model, and refit the data with the outliers excluded.
fdata = feval(fit1,xdata); I = abs(fdata - ydata) > 1.5*std(ydata); outliers = excludedata(xdata,ydata,'indices',I); fit2 = fit(xdata,ydata,f,'StartPoint',[1 1],... 'Exclude',outliers);
Compare the effect of excluding the outliers with the effect of giving them lower bisquare weight in a robust fit.
fit3 = fit(xdata,ydata,f,'StartPoint',[1 1],'Robust','on');
Plot the data, the outliers, and the results of the fits.
plot(fit1,'r-',xdata,ydata,'k.',outliers,'m*') hold on plot(fit2,'c--') plot(fit3,'b:') xlim([0 2*pi])
Plot the residuals for the two fits considering outliers.
figure plot(fit2,xdata,ydata,'co','residuals') hold on plot(fit3,xdata,ydata,'bx','residuals') hold off
Load data and fit a Gaussian, excluding some data with an expression, then plot the fit, data and the excluded points.
[x, y] = titanium; f1 = fit(x',y','gauss2', 'Exclude', x<800); plot(f1,x,y,x<800)
