matlab articles

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  I am using the standard Matlab function canoncorr.m to calculate canonical correlations between two large data sets. However, I get error messages on lines 145 and 149. This error also appears with basic random 3x9 matrices for both x and y.   line 145: stats.pF = fpval(stats.F, stats.df1, stats.df2);   line 149: stats.pChisq = chi2pval(stats.chisq, stats.df1);   they both throw an error of "function not defined for argument of type double"   then if I search for them or try to find help files I get "function not found" errors.   I fear the files were overwritten but I cannot even seem to find them on other computers with matlab in order to replace them on my machine.   Perhaps parts of my path were inadvertently deleted?   Does anyone know the location of these built in files? Any help would be greatly appreciated.   NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and re

  i have undergone an experiment and do not know which test to use. i want to see if there is a relationship between age, gender and height with grip strength.   NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research. Doing a study without a statistical design first will almost guarantee a useless result. Please, in the future, give some thought to how you will analyse your data before you do the study! At the very least, you will determine how many observations are needed in order to produce a statistically valid result.   First go to the literature in   PubMed  to see what previous research has been done and the statistical techniques others have used to ana

  Hello, How can I generate a uniformly distributed random vector with its sum to be equal to 1?   NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research. Too many people think that generating a uniform sample, then normalizing by the sum will generate a uniform sample. In fact, this is NOT at all true.   A good way to visualize this is to generate that sample for the 2-d case. For example, suppose we do it the wrong way first?   xy = rand(100,2); plot(xy(:,1),xy(:,2),'.') Now, lets do the sum projection that virtually everyone poses. (Yes, it is the obvious choice. Now we will see why it is the wrong approach.)   xys = bsxfun(@rdivide,xy,sum(xy,2)); h

  #Terrain File Terrain.mat #Start Point (xs,ys) -4.8 4.0 From the code above I am trying to extract "-4.8" and save it as xs, and "4.0" saved as ys. I don't know how to tell matlab to look for the "#" that will be at the beginning of every category. this is the project text file code please convert to project.text file for solving. #This is a project file for MAE8 Spring 2014 #Terrain File Terrain.mat #Start Point (xs,ys) -4.8 4.0 #End Point (xe,ye) 4.8 -4.8 #Number of Way Points 2 #Way Points (xwp,ywp) -0.5 4.8 4.7 0.8     NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research. If all you need is that start p

  Hi there, I am working with the neural network toolbox in matlab. My problem is not with the toolbox but with the data preparation before it is used and my resulting output. I standardise the input and targte output on which I train the network but this means that my estimated output, when I simulate the network on a new set of values has also a zero-mean, unit-vairance. I do not want this because while the estimated output correctly follows the trend and amplitude changes of my target output I want the physical values not these standardised values. Is there a way to 'de-standardise' my output i.e. add on the mean and multiply by the standard deviation. The mean of my time series is not constant so I am sure this is not as straightforward as I have said.   NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 1

  Good afternoon, I'm currently trying to generate random numbers from a uniform distribution, but I'm not sure how to input probabilities.   For example, Let S be uniformly distributed between 70 and 130: a=70; b=130; S=a+(b-a).*rand However, let's say we want to generate T which is uniformly distributed: between 0.1 and 1.0 (with probability 0.75) between 1.0 and 5.0 (with probability 0.25)   Is it possible to use the rand function as normal? i.e. a=0.1; b=1.0; c=5.0 T=([a+(b-a).*rand]*0.75)+([b+(c-b).*rand]*0.25) I appreciate any feedback.   NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research. N = 1000000; a=0.1; b=1.0; c=5.0; r1 = rand(N,

  I wrote some codes to run a linear discriminant analysis based classification:   %%Construct a LDA classifier with selected features and ground truth information LDAClassifierObject = ClassificationDiscriminant.fit(featureSelcted, groundTruthGroup, 'DiscrimType', 'linear'); LDAClassifierResubError = resubLoss(LDAClassifierObject); Thus, I can get Resubstitution Error of LDA (Training Error): 1.7391e-01 Resubstitution Accuracy of LDA: 82.61% Confusion Matrix of LDA: 14 3 1 5 Then I run a ROC analysis for the LDA classifier: % Predict resubstitution response of LDA classifier [LDALabel, LDAScore] = resubPredict(LDAClassifierObject); % Fit probabilities for scores (the groundTruthGroup contains lables either 'Good' or 'Bad') [FPR, TPR, Thr, AUC, OPTROCPT] = perfcurve(groundTruthGroup(:,1), LDAScore(:,1), 'Good'); I have got: OPTROCPT = 0.1250 0.8667 Therefore