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Sara Woods on 17 Aug 2024 at 13:41
Commented: Star Strider on 17 Aug 2024 at 14:48
Accepted Answer: Star Strider
- std_time.mat
- spikes_mtx.mat
Open in MATLAB Online
Hello all! Here my question:
I have a table displaying 1000 columns (spikes_mtx). Each column has 400 rows of data displaying exponential decay over 100s time (std_time).
I'd like to fit a three-coefficient power-fit model (y = a*x^b+c) to each column so as to obtain:
A table with x1000 a coefficient (coming from the 1000 exponential decays)
A table with x1000 b coefficient (coming from the 1000 exponential decays)
A table with x1000 c coefficient (coming from the 1000 exponential decays)
A table with x1000 fit goodness parameters (coming from the 1000 exponential decays)
All tables stored in one single structure (results.STD). My code doesn't function, any recommendation? Thanks in advance!
ft = fittype( 'power2' ); % Power Fit: y = a*x^b+c
opts = fitoptions(ft); % Power fit options
opts.StartPoint = [1 -1 0];
opts.Lower = [0 -Inf -Inf];
opts.Upper = [Inf 0 Inf];
for i=1:length (spikes_mtx)
[xData, yData] = prepareCurveData(std_time,spikes_mtx(:,i)); % x = std_time y = spikes_mtx
[fitresult, gof] = fit( xData, yData, ft, opts ); % Goodnes of the Fit R^2
results.STD.Coeff.a(1,i)=S.std.fitmodel.a;
results.STD.Coeff.b(1,i)=S.std.fitmodel.b;
results.STD.Coeff.c(1,i)=S.std.fitmodel.c;
results.STD.gof.sse(1,i)=S.std.gof.sse;
results.STD.gof.rsquare(1,i)=S.std.gof.rsquare;
results.STD.gof.dfe(1,i)=S.std.gof.dfe;
results.STD.gof.adjrsquare(1,i)=S.std.gof.adjrsquare;
results.STD.gof.rmse(1,i)=S.std.gof.rmse;
end
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Accepted Answer
Star Strider on 17 Aug 2024 at 14:03
Edited: Star Strider on 17 Aug 2024 at 14:33
- std_time.mat
- spikes_mtx.mat
It might be best to put all the parameters in a single table, and for that matter, put everything in a single table.
Since you want them in different tables, try this —
load('spikes_mtx.mat')
load('std_time')
% new_time = linspace(min(std_time), max(std_time), 1000*400);
% new_spikes = interp1(std_time, std_spk_avg, new_time);
%
% spikes_mtx = reshape(new_spikes, 1000, []).'
ft = fittype( 'power2' ); % Power Fit: y = a*x^b+c
opts = fitoptions(ft); % Power fit options
opts.StartPoint = [1 -1 0];
opts.Lower = [0 -Inf -Inf];
opts.Upper = [Inf 0 Inf];
for i=1:size(spikes_mtx,2)
[xData, yData] = prepareCurveData(std_time,spikes_mtx(:,i)); % x = std_time y = spikes_mtx
[fitresult, gof] = fit( xData, yData, ft, opts ); % Goodnes of the Fit R^2
results_a(i,:)=fitresult.a;
results_b(i,:)=fitresult.b;
results_c(i,:)=fitresult.c;
results_sse(i,:)=gof.sse;
results_rsquare(i,:)=gof.rsquare;
results_dfe(i,:)=gof.dfe;
results_adjrsquare(i,:)=gof.adjrsquare;
results_rmse(i,:)=gof.rmse;
end
Results_a = table(results_a)
Results_a = 1000x1 table
results_a _________ 0.41545 0.41543 0.41541 0.41538 0.41535 0.41531 0.41528 0.41524 0.41521 0.41519 0.41516 0.41514 0.41511 0.41509 0.41506 0.41504
Results_b = table(results_b)
Results_b = 1000x1 table
results_b _________ -0.36158 -0.36163 -0.36167 -0.3617 -0.3617 -0.3617 -0.3617 -0.3617 -0.36171 -0.36171 -0.36172 -0.36173 -0.36174 -0.36174 -0.36175 -0.36176
Results_c = table(results_c)
Results_c = 1000x1 table
results_c _________ 0.23473 0.23475 0.23478 0.23479 0.2348 0.23481 0.23482 0.23483 0.23483 0.23484 0.23485 0.23486 0.23487 0.23488 0.23489 0.2349
Results_GOF = table(results_sse, results_rsquare, results_dfe, results_adjrsquare, results_rmse)
Results_GOF = 1000x5 table
results_sse results_rsquare results_dfe results_adjrsquare results_rmse ___________ _______________ ___________ __________________ ____________ 0.52615 0.7517 397 0.75045 0.036405 0.52621 0.75167 397 0.75042 0.036407 0.52628 0.75164 397 0.75039 0.036409 0.52632 0.75161 397 0.75036 0.036411 0.52633 0.75158 397 0.75033 0.036411 0.52634 0.75155 397 0.7503 0.036411 0.52635 0.75151 397 0.75026 0.036412 0.52636 0.75148 397 0.75023 0.036412 0.52636 0.75145 397 0.7502 0.036412 0.52637 0.75143 397 0.75018 0.036412 0.52638 0.7514 397 0.75015 0.036413 0.52638 0.75138 397 0.75013 0.036413 0.52639 0.75136 397 0.75011 0.036413 0.52641 0.75134 397 0.75008 0.036414 0.52642 0.75131 397 0.75006 0.036414 0.52644 0.75128 397 0.75003 0.036415
Single table —
Results = table(results_a, results_b, results_c, results_sse, results_rsquare, results_dfe, results_adjrsquare, results_rmse)
Results = 1000x8 table
results_a results_b results_c results_sse results_rsquare results_dfe results_adjrsquare results_rmse _________ _________ _________ ___________ _______________ ___________ __________________ ____________ 0.41545 -0.36158 0.23473 0.52615 0.7517 397 0.75045 0.036405 0.41543 -0.36163 0.23475 0.52621 0.75167 397 0.75042 0.036407 0.41541 -0.36167 0.23478 0.52628 0.75164 397 0.75039 0.036409 0.41538 -0.3617 0.23479 0.52632 0.75161 397 0.75036 0.036411 0.41535 -0.3617 0.2348 0.52633 0.75158 397 0.75033 0.036411 0.41531 -0.3617 0.23481 0.52634 0.75155 397 0.7503 0.036411 0.41528 -0.3617 0.23482 0.52635 0.75151 397 0.75026 0.036412 0.41524 -0.3617 0.23483 0.52636 0.75148 397 0.75023 0.036412 0.41521 -0.36171 0.23483 0.52636 0.75145 397 0.7502 0.036412 0.41519 -0.36171 0.23484 0.52637 0.75143 397 0.75018 0.036412 0.41516 -0.36172 0.23485 0.52638 0.7514 397 0.75015 0.036413 0.41514 -0.36173 0.23486 0.52638 0.75138 397 0.75013 0.036413 0.41511 -0.36174 0.23487 0.52639 0.75136 397 0.75011 0.036413 0.41509 -0.36174 0.23488 0.52641 0.75134 397 0.75008 0.036414 0.41506 -0.36175 0.23489 0.52642 0.75131 397 0.75006 0.036414 0.41504 -0.36176 0.2349 0.52644 0.75128 397 0.75003 0.036415
EDIT — (17 Aug 2024 at 14:33)
Added code to create a single table.
.
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Sara Woods on 17 Aug 2024 at 14:15
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https://www.mathworks.com/matlabcentral/answers/2145954-how-to-fit-power-law-to-each-column-of-data-arranged-in-a-table#comment_3239329
Thanks again @Star Strider, your recommendations are always helpful!
Out of curiosity (as I'm striving to improve my coding skills and my understanding of data arrays), as you mention that it might be best to put all the parameters in a single table, how would you arrange all the results?
Star Strider on 17 Aug 2024 at 14:36
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https://www.mathworks.com/matlabcentral/answers/2145954-how-to-fit-power-law-to-each-column-of-data-arranged-in-a-table#comment_3239344
⋮
Edited: Star Strider on 17 Aug 2024 at 14:39
As always, my pleasure!
I would not edit your results. I would simply create a ‘Results’ table that includes all the variables.
See my edited answer (‘Single table’) for an example.
Sara Woods on 17 Aug 2024 at 14:48
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https://www.mathworks.com/matlabcentral/answers/2145954-how-to-fit-power-law-to-each-column-of-data-arranged-in-a-table#comment_3239369
Done! Thanks!!!
Star Strider on 17 Aug 2024 at 14:48
Direct link to this comment
https://www.mathworks.com/matlabcentral/answers/2145954-how-to-fit-power-law-to-each-column-of-data-arranged-in-a-table#comment_3239374
As always, my pleasure!
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More Answers (1)
John D'Errico on 17 Aug 2024 at 14:22
Edited: John D'Errico on 17 Aug 2024 at 14:22
Open in MATLAB Online
- spikes_mtx.mat
- std_time.mat
Why should it work? I see these lines:
[fitresult, gof] = fit( xData, yData, ft, opts ); % Goodnes of the Fit R^2
results(i).STD.Coeff.a(1,i)=S.std.fitmodel.a;
What is S.std.fitmodel? Where do you think that comes from? You create something called fitresult and gof. So use it. Use the variable gof as returned. Should MATLAB be able to know what you want? It tries to execute the code you give it.
load std_time.mat
load spikes_mtx.mat
ft = fittype( 'power2' ) % Power Fit: y = a*x^b+c
ft =
General model Power2: ft(a,b,c,x) = a*x^b+c
opts = fitoptions(ft); % Power fit options
opts.StartPoint = [1 -1 0];
opts.Lower = [0 -Inf -Inf];
opts.Upper = [Inf 0 Inf];
The results struct is a highly confusing thing you are creating. I have no clue what you really wanted to do, so I'll make a guess.
N = length(spikes_mtx);
results.Coeff.a = zeros(1,N);
results.Coeff.b = zeros(1,N);
results.Coeff.c = zeros(1,N);
results.gof.sse = zeros(1,N);
results.gof.rsquare = zeros(1,N);
results.gof.dfe = zeros(1,N);
results.gof.adjrsquare = zeros(1,N);
results.gof.rmse = zeros(1,N);
for i=1:10 % Should be N, but I've just used 10 here.
[xData, yData] = prepareCurveData(std_time,spikes_mtx(:,i)); % x = std_time y = spikes_mtx
[fitresult, gof] = fit( xData, yData, ft, opts ); % Goodnes of the Fit R^2
results.Coeff.a(i)=fitresult.a;
results.Coeff.b(i)=fitresult.b;
results.Coeff.c(i)=fitresult.c;
results.gof.sse(i) = gof.sse;
results.gof.rsquare(i) = gof.rsquare;
results.gof.dfe(i) = gof.dfe;
results.gof.adjrsquare(i) = gof.adjrsquare;
results.gof.rmse(i) = gof.rmse;
end
results.Coeff
ans = struct with fields:
a: [0.4155 0.4154 0.4154 0.4154 0.4153 0.4153 0.4153 0.4152 0.4152 0.4152 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... ] (1x1000 double) b: [-0.3616 -0.3616 -0.3617 -0.3617 -0.3617 -0.3617 -0.3617 -0.3617 -0.3617 -0.3617 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... ] (1x1000 double) c: [0.2347 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... ] (1x1000 double)
results.gof
ans = struct with fields:
sse: [0.5261 0.5262 0.5263 0.5263 0.5263 0.5263 0.5263 0.5264 0.5264 0.5264 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... ] (1x1000 double) rsquare: [0.7517 0.7517 0.7516 0.7516 0.7516 0.7515 0.7515 0.7515 0.7515 0.7514 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... ] (1x1000 double) dfe: [397 397 397 397 397 397 397 397 397 397 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... ] (1x1000 double) adjrsquare: [0.7504 0.7504 0.7504 0.7504 0.7503 0.7503 0.7503 0.7502 0.7502 0.7502 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... ] (1x1000 double) rmse: [0.0364 0.0364 0.0364 0.0364 0.0364 0.0364 0.0364 0.0364 0.0364 0.0364 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... ] (1x1000 double)
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Sara Woods on 17 Aug 2024 at 14:31
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https://www.mathworks.com/matlabcentral/answers/2145954-how-to-fit-power-law-to-each-column-of-data-arranged-in-a-table#comment_3239334
Thanks John! I think that code line is my mistake, as I'm a beginner! I'll delete it
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