I have been all over Google trying to find a good function/package to perform multivariate regression (i.e. predict multiple continuous variables given another set of multiple continuous variables).
I wish to use something like fitlm(), since that also gives me p-value statistics and R squared statistics. Does anything like that exist?
Matlab has a bundle of tools for this, see this page.
I believe that mvregress is the most rounded and mainstream tool. See this page for setting up an analysis with it.
Also, a comment in this post may be useful for alternatives, if needed: it is possible to approach this via separate regression analyses, one for each response variable.
Related
I am trying to implement bayesian optimization using gauss process regression, and I want to try the multiple output GP firstly.
There are many softwares that implemented GP, like the fitrgp function in MATLAB and the ooDACE toolbox.
But I didn't find any available softwares that implementd the so called multiple output GP, that is, the Gauss Process Model that predict vector valued functions.
So, Are there any softwares that implemented the multiple output gauss process that I can use directly?
I am not sure my answer will help you as you seem to search matlab libraries.
However, you can do co-kriging in R with gstat. See http://www.css.cornell.edu/faculty/dgr2/teach/R/R_ck.pdf or https://github.com/cran/gstat/blob/master/demo/cokriging.R for more details about usage.
The lack of tools to do cokriging is partly due to the relative difficulty to use it. You need more assumptions than for simple kriging: in particular, modelling the dependence between in of the cokriged outputs via a cross-covariance function (https://stsda.kaust.edu.sa/Documents/2012.AGS.JASA.pdf). The covariance matrix is much bigger and you still need to make sure that it is positive definite, which can become quite hard depending on your covariance functions...
I would like to create a tool for generating a stochastic time-series distribution, for which I can provide the parameters (for a normal distribution) the mean, standard deviation, skewness and kurtosis. There is a similar question here using R, but I am not able to interpret this and put it in MATLAB.
Is there something that someone knows can do this already? (I haven't been able to find anything)
If not, what would be some good advice for starting something of my own? Any known useful functions? I would also like to be able to build upon it afterwards, for example: adding outliers, clusters of volatility, adjusting heteroscedasticity.
I realise me saying 'stochastic' and then in the same sentence 'given parameters' may seem odd, but it isn't - I want each time point to be random, but the parameters to describe, say 10,000 time points.
If you're looking for the equivalent of the solution in R, Matlab's Statistics Toolbox has limited support for the Johnson and Pearson distribution systems. In particular, the johnsrnd function produces random variates for the Johnson system. The Pearson system and pearsrnd, however, takes moments directly.
A big caveat. Using moments to describe or fit or produce random variates – often referred to as moment matching – is not robust and poorly regarded by statisticians. They're not guaranteed to uniquely define a distribution unless you have the entire moment generating function.
I am doing a project on Open Modelica and i have to simulate filters on it using active elements(op amp). Modelica plots graph with respect to time but i want my graphs with respect to frequency to analyze the frequency response of the system. I searched the internet but couldn't find anything useful. Please reply as soon as possible.
If you want to plot a variable with respect to another variable you can use plotParameteric from OMShell (OpenModelica Shell). In OMEdit (OpenModelica Connection Editor) you can click on parametric plot button x(y) and then select 2 variables.
I assume that what you want is a Bode plot. If so, it is important to understand that such a plot does not arise from a transient simulation. It is necessary to transform your system into a linear, time-invariant representation in order to express the response of your system in the frequency domain.
I do not know what specific features OpenModelica has in this regard. But those are at least the kinds of things you should search the documentation for. If you have access to MATLAB, then all you really need to do is extract the linearized version of the model (normally expressed as the so-called "ABCD" matrices) and MATLAB can get you the rest of the way.
There is also the Modelica_LinearSystems2 library which might be compatible with OpenModelica (I have no idea). It includes many types of operations you would typically perform on linear systems.
When I tried to train a SVM(trainsvm function) with RBF kernel,
The libSVM library outputs "Line search fails in two-class probability estimates" during training.
After training, the training accuracy of the model is just 20%.
I think I might miss something and it is related to the message.
For more information about my project,
I'm dealing with PASCAL VOC action classification problem.
I'm trying to follow this method.
http://www.ifp.illinois.edu/~jyang29/papers/CVPR09-ScSPM.pdf
There are 1300 training images and 11 classes.
After making codebooks and sparse coding,
The dimension of feature vector is 2688.
The number of training example is 1370.
You need to do a grid search, either using cross validation, or using a separate validation data set to get good values for C and gamma. Libsvm has a script called grid.py that is useful for this. I noticed you tagged this with matlab, using grid.py needs command line tools and a python installation (IMO this generally works out better than with matlab, especially if you have a some big machines to run many jobs in parallel).
I recommend that you read the libsvm guide if you haven't already done so: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
I also suggest you initially use the same dataset as used for the paper as occasionally published algorithms only work well on the dataset chosen for the paper.
Lastly, you could contact the authors of the paper.
I asked about this warning the author of LIBSVM, and he replied that this warning can be ignored.
I am trying to do some text classification with SVMs in MATLAB and really would to know if MATLAB has any methods for feature selection(Chi Sq.,MI,....), For the reason that I wan to try various methods and keeping the best method, I don't have time to implement all of them. That's why I am looking for such methods in MATLAB.Does any one know?
svmtrain
MATLAB has other utilities for classification like cluster analysis, random forests, etc.
If you don't have the required toolbox for svmtrain, I recommend LIBSVM. It's free and I've used it a lot with good results.
The Statistics Toolbox has sequentialfs. See also the documentation on feature selection.
A similar approach is dimensionality reduction. In MATLAB you can easily perform PCA or Factor analysis.
Alternatively you can take a wrapper approach to feature selection. You would search through the space of features by taking a subset of features each time, and evaluating that subset using any classification algorithm you decide (LDA, Decision tree, SVM, ..). You can do this as an exhaustively or using some kind of heuristic to guide the search (greedy, GA, SA, ..)
If you have access to the Bioinformatics Toolbox, it has a randfeatures function that does a similar thing. There's even a couple of cool demos of actual use cases.
May be this might help:
There are two ways of selecting the features in the classification:
Using fselect.py from libsvm tool directory (http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#feature_selection_tool)
Using sequentialfs from statistics toolbox.
I would recommend using fselect.py as it provides more options - like automatic grid search for optimum parameters (using grid.py). It also provides an F-score based on the discrimination ability of the features (see http://www.csie.ntu.edu.tw/~cjlin/papers/features.pdf for details of F-score).
Since fselect.py is written in python, either you can use python interface or as I prefer, use matlab to perform a system call to python:
system('python fselect.py <training file name>')
Its important that you have python installed, libsvm compiled (and you are in the tools directory of libsvm which has grid.py and other files).
It is necessary to have the training file in libsvm format (sparse format). You can do that by using sparse function in matlab and then libsvmwrite.
xtrain_sparse = sparse(xtrain)
libsvmwrite('filename.txt',ytrain,xtrain_sparse)
Hope this helps.
For sequentialfs with libsvm, you can see this post:
Features selection with sequentialfs with libsvm