I am trying to run a repeated measures ANOVA in Matlab with 4 factors including one factor representing my subjects which I want as a random factor.
The code I have is as follows:
[p,table,stats] = anovan(COORDS_SUBJ_II,{group_hand,group_stim,group_time,group_subs},'random',4,'varnames',{'HAND','STIM','TIME','SUBS'});
Here, all variables have the same dimension, which is 1350x1(all types are 'double'). I checked my code with some proposed code on the net and it matches, yet I keep getting the following error...
Error using chi2inv (line 3)
P and V must be of common size or scalars
Error in anovan>varcompest (line 838)
L = msTerm .* dfTerm ./ chi2inv(1-alpha/2,dfTerm);
Error in anovan>getRandomInfo (line 811)
[varest,varci] = varcompest(ems,randomterms,msTerm,dfTerm,alpha);
Error in anovan (line 296)
getRandomInfo(msterm,dfterm,mse,dfe,emsMat,randomterm,...
My dependent variable (COORDS_SUBJ_II) has a couple of NaN's in it, although I ran the code once where I replaced those NaN's with random numbers and it still gives me the same error. I am kind of lost right now and would appreciate any help.
Best
ty
Found it out. A toolbox I downloaded a while ago also had the chi2inv command and this prompted the error =)
Related
This question already exists:
Error using symengine>#()0.0 Too many input arguments
Closed 3 years ago.
Exact error is:
Error using symengine>#()0.0
Too many input arguments.
Error in bdipuniodefun (line 18)
bdip = [s(4); s(5); s(6); (q/m_e)*(Ex(s(1),s(2),s(3)) + s(5)*Bz(s(1),s(2),s(3)) - s(6)*By(s(1),s(2),s(3))); (q/m_e)*(Ey(s(1),s(2),s(3)) +
s(6)*Bx(s(1),s(2),s(3)) - s(4)*Bz(s(1),s(2),s(3))); (q/m_e)*(Ez(s(1),s(2),s(3)) + s(4)*By(s(1),s(2),s(3)) - s(5)*Bx(s(1),s(2),s(3)))];
Error in odearguments (line 90)
f0 = feval(ode,t0,y0,args{:}); % ODE15I sets args{1} to yp0.
Error in ode15s (line 150)
odearguments(FcnHandlesUsed, solver_name, ode, tspan, y0, options, varargin);
Error in collisions6 (line 219)
[T,S] = ode15s(#bdipuniodefun, tspan, icv);
So we know where the error occurs, but I have been unable to resolve it nonetheless. This is an error that has also been inconsistent too. It does not always appear when I run the main script "collisions.m" but happens only when certain things get altered. Some examples: I start with too many electrons/particles, usually 1000+. Or I try to input a non-zero electric field. The electric field function is still being referenced but it is zero (Here comes the strange part). The reason I did this is because although the error goes away, the ode solver does not compute even close to the correct solutions I am looking for. I know for a fact that the results are wrong due to my knowledge of physics. However, when it does this, it does not give an error.
A Matlab staff member suggested that it may have something to due with my persistent variable definition but that is the only possible lead I have.
Troubleshooting methods I have already been through:
First if you look at the documentation from Matlab, it shows a specific syntax example to define the set of ODEs. In Matlab's syntax, they define each equation individually and then the vector of equations using the definitions. When I tried this syntax, the solver either did not solve the equations correctly, or it gave me the "too many input arguments" error. The way I made it run at all was to define every equation outright on a single line in "bdipodefun.m". Only that syntax worked and there is no good reason in my mind why this is the case because the definition that is used in one of the input arguments for the solver is the same. Using the Matlab recommended syntax even in simpler code, makes the ode solution incorrect.
I tried all ode solvers Matlab has to offer. The same issues persist or it cannot solve the equations at all.
I tried re-coding the entire simulation from scratch. I have found that this error still occurs even in more simpler codes.
Equation solver will also not solve the system correctly if there is no E-field. I found that if I create an E-field function and even just define it as zero within the file, the system solves correctly. It absolutely makes no sense as to why this is occurring.
Here is a description of what each file does in the code:
"collisionsmain.m" = Main script that references other files
"B_test.m" = B field function file referenced by collisionsmain
"E_test.m" = E field function file referenced by collisionsmain
"engen.m" = Generates energies for electrons based on a Maxwellian probability distribution curve
"maxdis.m" = Maxwellian distribution function used by engen
"engen.m" = Generates energies for electrons based on a Maxwellian
"posgen.m" = Generates a random, initial position on a sphere "bdipodefun.m" = ODE function set referenced by ODE solver in collisionsmain
Link to files: https://drive.google.com/drive/folders/19eJrgWYL4LQFwTY-VwguCCkta-pimZlL?usp=sharing
I didn't understand the code because it is complicated and big. Anyway, I tracked the error and find the source of the issue. The error comes from the number of parameters that have been sent to Ex, Ey, Ez in bdipuniodefun.m function. Ex, Ey, Ez functions receives 3 parameters while they can handle one parameter only.
The following code didn't send error and it shows the results.
bdip_r1 = [s(4); s(5); s(6)];
bdip_r2 = (q/m_e) * (Ex(s(1)) + s(5)*Bz(s(1),s(2),s(3))- s(6)*By(s(1),s(2),s(3)));
bdip_r3 = (q/m_e)*(Ey(s(1)) + s(6)*Bx(s(1),s(2),s(3)) - s(4)*Bz(s(1),s(2),s(3)));
bdip_r4 = (q/m_e)*(Ez(s(1)) + s(4)*By(s(1),s(2),s(3)) - s(5)*Bx(s(1),s(2),s(3)));
bdip =[bdip_r1;bdip_r2;bdip_r3;bdip_r4];
I have this matlab code for creating averages of several images:
cd('C:\images')
all_images=dir('*.jpg');
[sj,xx]=size(all_images);
j=1;
file_name=char(all_images(j).name);
tmp=imread(file_name);
[m,n]=size(tmp);
template=zeros(m,n);
for i=1:length(all_images)
file_name=char(all_images(i).name);
tmp=double(imread(file_name));
template=template+tmp;
end
template=template/length(all_images);
imagesc(template)
imwrite(uint8(template),'template.jpg','jpg')
The case is that I obtained the following error:
Error
using + Matrix dimensions must agree.
Error
in averagetemplate (line 15) template=template+tmp;
Any idea? I have to say that I am newbie in matlab coding.
Thanks
Just change [m,n]=size(tmp) in line 6 to [m,n,~]=size(tmp)and your problem will be solved.
In the below code :
[m,n]=size(tmp)
Matlab is computing n*3 as the column of matrix so you will get the dimension error in next few line.
You are overwriting your temp variable with what appears to be a single value here:
tmp=double(imread(file_name));
and it may be different dimensions M x N regardless. Matlab is saying to add matricies they have to have same number of rows and cols.
I am trying to use the NonLinearModel.fit() function in Matlab to regress two variables. However, I am getting the following error:
Error using internal.stats.getscheffeparam>ValidateParameters
(line 182)
If non-empty, JW must be a numeric, real matrix.
Error in internal.stats.getscheffeparam (line 110)
[J,VF,VP,JW,Intopt,TolSVD,TolE,VQ,usingJ] =
ValidateParameters(J,VF,VP,JW,Intopt,TolSVD,TolE,VQ,allowedIntopt);
Error in nlinfit (line 340)
sch =
internal.stats.getscheffeparam('WeightedJacobian',J(~nans,:),'Intopt','observation','VQ',VQ);
Error in NonLinearModel/fitter (line 1121)
[model.Coefs,~,J_r,model.CoefficientCovariance,model.MSE,model.ErrorModelInfo,~]
= ...
Error in classreg.regr.FitObject/doFit (line 219)
model = fitter(model);
Error in NonLinearModel.fit (line 1484)
model = doFit(model);
Error in getMatrix (line 101)
nlm =
NonLinearModel.fit(regressorMatrix',temp2',modelfun,beta0);
My regressorMatrix is 2-by-N (so the transpose is N-by-2), temp2' is N-by-1, and beta0 and model are given by:
model =#(b,x)b(1).*x(:,1).*x(:,2).^b(2);
beta0=[.14 .6];
nlm = NonLinearModel.fit(regressorMatrix',temp2',model,beta0);
Could someone please help me in figuring out what is causing this error?
EDIT: ok, no help so far so I will try to be more specific. I know that this error is referring to the weighted Jacobian matrix. I am just not sure why this Jacobian would not be real-valued.
Here are the first few rows of my regressor matrix:
regressorMatrix =
1.0e+07 *
0.000000000776613 3.762601240855837
0.000000001683014 3.762601240855837
0.000000001496807 3.762601240855837
0.000000000753495 3.762601240855837
and my response matrix:
temp2 =
-0.011811061934317
0.987582922964869
0.010621342764736
0.135001167018444
0.091950680609212
I can see that something is wrong here (the orders of magnitude in col2 of my regressor matrix are off. I will fix this and get back with an explanation if it turns out to the the cause. I am also working on printing out J. and JW
EDIT2:
I was able to print out JW before the error occurs and discovered that JW is a Nx2 complex matrix. So the specific reason the error is occurring is that the weighted Jacobian matrix is not real valued. Not sure why...
After a lot of digging, I found that the answer to this problem is quite obvious. I didn't realize that some of the values for one of my regressor variables was going negative sometimes. This lead (jumping a few steps ahead) to a complex Jacobian.
I have to carry out the following operation
R=[0,0.5,-0.25;-0.25,0,0.25;0,0,0.25];
B=[0,k21,k31;k12,0,k32;0,0,k];
G=inv(R).*B;
g=det(G);
but Matlab is showing the following error
??? Error using ==> horzcat
CAT arguments dimensions are not consistent.
Error in ==> g at 60
B=[0,k21,k31;k12,0,k32;0,0,k];
K21,K31,K12,K32 and k all have dimensions of 923334 by 1. Can anyone help me how can I carry out the following operation.
Your code works well for me. Check that the k-values (k12,k31,k32...) are scalars (or 1x1 dimension)
EDIT :
For the case you mention, k's are nx1, one simple way is to perform a loop:
R=[0,0.5,-0.25;-0.25,0,0.25;0,0,0.25];
for ii=1:length(k)
B=[0,k21(ii),k31(ii);k12(ii),0,k32(ii);0,0,k(ii)];
G=inv(R).*B;
g(ii)=det(G);
end
There is also a "vectorized" way to do that, but it seems to be good enough...
I am working on a project that needs to use hidden markov models. I downloaded Kevin Murphy's toolbox. I have some problems about the usage. In the toolbox webpage, he says that first input of dhmm_em and dhmm_logprob are symbol sequence data. On their examples, they give row vectors as data. So, when I give my symbol sequence as row vector, I get error;
??? Error using ==> assert at 9
assertion violated:
Error in ==> fwdback at 105
assert(approxeq(sum(alpha(:,t)),1))
Error in ==> dhmm_logprob at 17
[alpha, beta, gamma, ll] = fwdback(prior,
transmat, obslik, 'fwd_only', 1);
Error in ==> mainCourseProject at 110
loglik(train_act) =
dhmm_logprob(orderedSymbols,
hmm{train_act}.prior,
hmm{train_act}.trans,
hmm{act}.emiss);
However, before giving this error, code works for some symbol vectors. When I give my data as column vector, functions work fine, no errors. So why exactly am I getting this error?
You might say that I should be giving not single vectors, but vector sets, I also tried to collect my feature vectors in a struct and give row vectors as such, but nothing changed, I still get assertion error.
By the way, my symbol sequence does not have any zeros, I am doing everything almost the same as they showed in their examples, so I would be greatful if anyone could help me please.
Im not sure, but from the function call stack shown above, shouldn't the last line be hmm{train_act}.emiss instead of hmm{act}.emiss.
In other words when you computing the log-probability of a sequence, you should pass components that belong to the same HMM model (transition matrix, emission matrix, and prior probabilities).
By the way, the ASSERT in the code is a sanity check that a vector of probabilities should sum to 1. Oftentimes, when working with very small values (log-probabilities), numerical stability issues can creep in... You could edit the APPROXEQ function to relax the comparison a bit, by giving it a bigger margin of error
This error message and the code it refers to are human-readable. An assertion is a guard put in by the programmer, to ensure that certain conditions are met. In this case, what is the condition? approxeq(sum(alpha(:,t)),1) I'd venture to say that approxeq wants the values to be approximately equal, so this boils down to: sum(alpha(:,t)) ~= 1
Without knowing anything about the code, I'd also guess that these refer to probabilities. The probabilities of a node's edges must sum to one. Hopefully this starts you down a productive debugging path. If you can't figure out what's wrong with your input that produces this condition, start wading into the code a bit to see where this alpha vector comes from, and how it ended up invalid.