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how to select random rows from a matrix?

Statistics Toolbox includes a nice function called randsample % Generate a matrix named foo foo = randn(10000,2); ...

how to select random rows from a matrix?

Statistics Toolbox includes a nice function called randsample % Generate a matrix named foo foo = randn(10000,2); ...

presque 13 ans il y a | 4

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Linear data fitting

From my perspective, the easiest way to solve this one is # Fit a linear model to the complete data set # Apply a clustering al...

Linear data fitting

From my perspective, the easiest way to solve this one is # Fit a linear model to the complete data set # Apply a clustering al...

presque 13 ans il y a | 0

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Which curve fitting function do I need?

nlinfit uses optimization solvers under the hood, so it's not too surprising that you're getting the same answer. nlinfit is pa...

Which curve fitting function do I need?

nlinfit uses optimization solvers under the hood, so it's not too surprising that you're getting the same answer. nlinfit is pa...

presque 13 ans il y a | 1

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General Least Squares Fit

Thanks for clarifying. From the sounds of things, you need some kind of solution for non-parametric fitting. The choice of...

General Least Squares Fit

Thanks for clarifying. From the sounds of things, you need some kind of solution for non-parametric fitting. The choice of...

environ 13 ans il y a | 0

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General Least Squares Fit

I am somewhat confused by the question. Regression analysis is used to estimate a set of regression coefficients than minimiz...

General Least Squares Fit

I am somewhat confused by the question. Regression analysis is used to estimate a set of regression coefficients than minimiz...

environ 13 ans il y a | 0

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Multinomial logistic regression

The following example deals with Poisson regression rather than logistic regression. I'm posting this because it includes a fair...

Multinomial logistic regression

The following example deals with Poisson regression rather than logistic regression. I'm posting this because it includes a fair...

environ 13 ans il y a | 0

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How to estimate Standard Error for the coefficients in ridge regression aproach

If you're working with a ridge regression model (as opposed to lasso or elastic net) then its relatively easy to code up a paire...

How to estimate Standard Error for the coefficients in ridge regression aproach

If you're working with a ridge regression model (as opposed to lasso or elastic net) then its relatively easy to code up a paire...

environ 13 ans il y a | 1

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Populating a 3D lookup table

Hi Stephen I did a webinar a couple years back focusing on using sftool to generate lookup tables for Simulink. The webi...

Populating a 3D lookup table

Hi Stephen I did a webinar a couple years back focusing on using sftool to generate lookup tables for Simulink. The webi...

environ 13 ans il y a | 0

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Markov Chain - Hidden Markov Model; how to create markov chains and combine them to a hidden markov model

When I am working with Markov Chains I'm normally looking at stationary distributions which, by definition, don't depend on the ...

Markov Chain - Hidden Markov Model; how to create markov chains and combine them to a hidden markov model

When I am working with Markov Chains I'm normally looking at stationary distributions which, by definition, don't depend on the ...

environ 13 ans il y a | 1

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Bootstrap sampling depending on portfolio

Hi Philip I attached a couple different examples of residual bootstraps using MATLAB The following reference provides some...

Bootstrap sampling depending on portfolio

Hi Philip I attached a couple different examples of residual bootstraps using MATLAB The following reference provides some...

plus de 13 ans il y a | 0

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Curve fitting to data sets with multiple parameters

The easiest way to solve this type of problem is the nlinfit function inside Statistics Toolbox. Here's a simple example that d...

Curve fitting to data sets with multiple parameters

The easiest way to solve this type of problem is the nlinfit function inside Statistics Toolbox. Here's a simple example that d...

plus de 13 ans il y a | 1

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Optimal(default) bandwidth estimation method

You can open ksdensity.m in your editor and inspect the bandwidth estimation code. This section of the code starts at line 29...

Optimal(default) bandwidth estimation method

You can open ksdensity.m in your editor and inspect the bandwidth estimation code. This section of the code starts at line 29...

plus de 13 ans il y a | 0

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Bootstrap for surface fit goodness of fit stats

Few quick observations First: There are a lot of different ways to bootstrap a regression model. Some of the more common exam...

Bootstrap for surface fit goodness of fit stats

Few quick observations First: There are a lot of different ways to bootstrap a regression model. Some of the more common exam...

plus de 13 ans il y a | 1

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Multi-Dimensional Data for SVM

Hi Ziggy First: Statistics Toolbox has a number of good classification algorithms. The 11a release includes a variety of di...

Multi-Dimensional Data for SVM

Hi Ziggy First: Statistics Toolbox has a number of good classification algorithms. The 11a release includes a variety of di...

plus de 13 ans il y a | 0

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how to use cfit to get fitobject and gof from a separately provided line

The aren't any constructor options for the Fit Objects in Curve Fitting Toolbox. The easiest way to accomplish your goal is t...

how to use cfit to get fitobject and gof from a separately provided line

The aren't any constructor options for the Fit Objects in Curve Fitting Toolbox. The easiest way to accomplish your goal is t...

plus de 13 ans il y a | 0

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Interpolating Multivariate time series

Handling missing data is a very complicated topic. There are a number of different approaches that you can use including listwi...

Interpolating Multivariate time series

Handling missing data is a very complicated topic. There are a number of different approaches that you can use including listwi...

plus de 13 ans il y a | 0

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Problem of robust fitting using the "robustfit" function

Hi Massinissa In your first example, you are fitting Z as a function of X and Y. In the second you are fitting X as a function...

Problem of robust fitting using the "robustfit" function

Hi Massinissa In your first example, you are fitting Z as a function of X and Y. In the second you are fitting X as a function...

plus de 13 ans il y a | 0

| A accepté

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bhhh method for nonlinear regression

BHHH is a specific optimization algorithm. Under certain conditions, the BHHH algorithms is guaranteed to converge. I am not...

bhhh method for nonlinear regression

BHHH is a specific optimization algorithm. Under certain conditions, the BHHH algorithms is guaranteed to converge. I am not...

plus de 13 ans il y a | 0

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Need Help in Monte Carlo and comparing operational sequence

Hi Hammad It's difficult to answer this without more information about your model. The easiest way to implement this would...

Need Help in Monte Carlo and comparing operational sequence

Hi Hammad It's difficult to answer this without more information about your model. The easiest way to implement this would...

plus de 13 ans il y a | 0

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Multi level regression analysis

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

Multi level regression analysis

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

plus de 13 ans il y a | 1

| A accepté

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Appending dataset of varying length

Hi Oleg This strikes me as more of a data representation issue than a question of MATLAB syntax. Your eventual solution will...

Appending dataset of varying length

Hi Oleg This strikes me as more of a data representation issue than a question of MATLAB syntax. Your eventual solution will...

plus de 13 ans il y a | 0

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Bioinformatics GO Ontology, leave-one-out cross validation

Here's a simple example that shows how to do leave one out cross validation using the cvpartition and crossval commands in Stati...

Bioinformatics GO Ontology, leave-one-out cross validation

Here's a simple example that shows how to do leave one out cross validation using the cvpartition and crossval commands in Stati...

plus de 13 ans il y a | 0

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Residuals from Regress

% Generate some random data X = linspace(1,100,100)'; Y = X + randn(100,1); % Use Curve Fitting Toolbox to genera...

Residuals from Regress

% Generate some random data X = linspace(1,100,100)'; Y = X + randn(100,1); % Use Curve Fitting Toolbox to genera...

plus de 13 ans il y a | 0

| A accepté

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Nonlinear fit to multiple data sets with shared parameters

Hi Kenneth Coincidentially, I did a webinar a couple weeks back that uses lsqcurvefit to solve just this type of problem. You ...

Nonlinear fit to multiple data sets with shared parameters

Hi Kenneth Coincidentially, I did a webinar a couple weeks back that uses lsqcurvefit to solve just this type of problem. You ...

plus de 13 ans il y a | 0

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removing outliers

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

removing outliers

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

plus de 13 ans il y a | 4

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Fitting data from differential scanning calorimetry to the Lumry-Eryring equation

Much as I love Curve Fitting Toolbox, I think that this is a case where Optimization Tbx will suffice....

Fitting data from differential scanning calorimetry to the Lumry-Eryring equation

Much as I love Curve Fitting Toolbox, I think that this is a case where Optimization Tbx will suffice....

plus de 13 ans il y a | 0

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Fitting data from differential scanning calorimetry to the Lumry-Eryring equation

Hi William I did a quick Internet search on "Lumry-Eyring". I found a lot of references to kinetic models. While the curves ...

Fitting data from differential scanning calorimetry to the Lumry-Eryring equation

Hi William I did a quick Internet search on "Lumry-Eyring". I found a lot of references to kinetic models. While the curves ...

plus de 13 ans il y a | 0

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Ridge regression and MSE

Use the "scaled" option to restore the coefficient estimates to the scale of the original data. You can then use b to estimat...

Ridge regression and MSE

Use the "scaled" option to restore the coefficient estimates to the scale of the original data. You can then use b to estimat...

plus de 13 ans il y a | 0

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nonlinear regression

The quality of a nonlinear regression is often highly dependent on the starting conditions that you provide to the optimization ...

nonlinear regression

The quality of a nonlinear regression is often highly dependent on the starting conditions that you provide to the optimization ...

plus de 13 ans il y a | 0

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Curve fitting to experimental sets of data

1. Start by using the Curve Fitting Tool to fit one of your data sets. 2. Select "Generate Code" from the file menu. This ...

Curve fitting to experimental sets of data

1. Start by using the Curve Fitting Tool to fit one of your data sets. 2. Select "Generate Code" from the file menu. This ...

plus de 13 ans il y a | 1