# Matlab regressions

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A lot of users use Matlab to perform regressions. Let's give some examples.

## Contents |

## [edit] Linear regression

You can use the built-in function regress

[B, Bint, R, Rint, Stats] = regress(y, x)

## [edit] Apparently nonlinear regressions

### [edit] Simple example

#### [edit] Setting the question

You can use an question asked on the Matlab newsgroup (Help to curve fitting):

I have some data (x,y)and one equation like y=(a*x)/(1+ax) and I need to plot the data and the curve [...]

When you have such a parametrized equation:
you can rewrite it in
so if you have some ( |

First generate a test set:

- f is defined as an
**anonymous function** - the real value of a is arbitrary chosen
- a noise coefficient s is chosen
- the domain of x is [0,10], the chosen resolution for x is 0.05
- y is the
*theoretical*value of y - ny the noisy (observed) values for y

<<generate test set>>=f = @(a,x)(a*x./(1+a*x)) a = sqrt(2) s = .05; x = (0:.1:10)'; y = f(a,x); ny = y + randn(size(y))*s; figure('Color',[0.9412 0.9412 0.9412 ]); h1 = subplot(1,2,1); plot(x,y,'-b',x,ny,'.k','linewidth',2);

#### [edit] Simple answer

Now we can get the implied values for a:

<<a implied values>>=g = @(x,y)(y./(x-x.*y)) ai = g(x,ny); h2 = subplot(1,2,2); boxplot(ai);

And a trivial estimator for a is the empirical expectation (the mean) of the implied a:

<<trivial estimation>>=ah = mean(ai(~isinf(ai))); title(sprintf('$\\hat a$ = %5.5f ; real a = %5.5f', ah, a), 'interpreter', 'latex'); axes(h1); hold on plot(x, f(ah, x), ':r', 'linewidth', 2); hold off legend({'theoretical', 'observed', 'estimation'});

Let's produce the whole code:

<<test_trivial_estimation.m>>=generate test set a implied values trivial estimation

(see the upper figure for the result).

But it's a **not so simple answer**, because from a statistical point of view, you have to master some of the properties of the estimator:

- is it efficient?
- is it biaised?
- etc

## [edit] External links

- Estimators on wikipedia

Download code |