Community Profile

Contact

Top 1% contributor

Hi Alice I just ran this same example using MATLAB and Excel. In both cases, the coefficients from the nonlinear regression ag...

accepted

3

Answered 5 years ago

46 downloads

6 years ago

40 downloads

5 years ago

Here's a pretty basic example X = linspace(0, 2*pi, 100); X = X'; Y = sin(X) + randn(100,1); foo = fit(X,Y, 's...

2

Answered 4 years ago

29 downloads

26 downloads

In general when I hear the expression parametric bootstrap I think "parametric residual bootstrap" I'm attaching some simple ...

1

For simplicity, this code is using a simple linear model. However, the same syntax with work for your nonlinear regression. ...

Statistics Toolbox includes two different algorithms (nlmefit and nlmefitsa) for fitting nonlinear mixed effects model. nlmefit...

Automatically detecting outliers is tricky stuff. You normally need fairly precise information regarding your data as well as t...

18 downloads

4 years ago

MathWorks tech support has a solution documenting various options for weighted regression. http://www.mathworks.com/support/s...

13 downloads

Alternatively, if you have the 12a version of Statistics Tbx X = [1,2,3,4,5,6,7,8,9]; Y = [4 5 6 9 8 7 4 1 2]; my...

MATLAB includes a wide variety of functions that can be used to simulate a random walk. Depending on what precisely you want to...

MATLAB and Statistics Toolbox provide a variety of ways to perform a regression. For example, if I am performing a simple linea...

Here's some simple code that illustrates how to perform nonlinear regression using the 12a release of Statistics Toolbox. Not...

I'm attaching code that shows a couple different ways to solve your problem. I prefer the second option. The R^2 is slightly...

I'm attaching some code that might provide helpful I also have a two part blog posting on this same subject that provides a b...

0

Local regression (aka LOWESS/LOESS) and interpolation don't lend themselves to parametric representations. There really isn't a...

Here's a simple example using the new regression functions in the 12a release. All you need to do is pass the appropriate weigh...

Hi Miguel fitensemble is able to handle multiple independent variables. You should be able to use this with the data set tha...

The SVM implementation in Bioinformatics Toolbox is limited to binary classification. The Naive Bayes classifier in Statistic...

Hi George Conveniently, 12a also has a function call NonLinearModel %% Generate some data X = 2* pi*rand(100,1); ...

Curve Fitting Toolbox allows you to specify constraints for individual regression coefficients. With this said and done, coul...

Here's the rub... When you fit a second order Fourier series, you're estimating values for six different regression coefficie...

nlinfit in Statistics Toolbox # Uses Levenberg-Marquardt under the hood # Allows you to input a vector of weights See <ht...

Have you looked into the dataset array that ships with Statistics Toolbox? The dataset array is a special data type that can ...

Try the following X = linspace(1, 2*pi, 50); X = X'; Y = sin(X) + randn(50,1); foo = fit(X,Y, 'sin1'); ...

I'd strongly recommend that you look at a file exchange submission by John D'Errico titled "Shape Modeling Language" http://w...

Load more

Choose your country to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .

You can also select a location from the following list:

See all countries