How do I stop overwriting in this "for" loop

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Joe Opdenaker 2022-10-13

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https://ww2.mathworks.cn/matlabcentral/answers/1825478-how-do-i-stop-overwriting-in-this-for-loop

评论： dpb 2022-10-13

I am trying to create a new datastructure call temp_All_2 from the for loops above. I can create the new data structure but for each participant it overwrites. How can I get the data to not overwrite?

temp_FixOn = FixOn; 
%     for tt = 1:length(trials)
%        trfix(tt,:) = nanmean(temp_FixOn(temp_FixOn(:,TRIAL_INDEX)==trials(tt), pupsize));
% 
%        %trfix(tt,1:length(fix_pup(unique(FixOn(:,TRIAL_INDEX)==trials(tt))))) = fix_pup(FixOn(:,TRIAL_INDEX)==trials(tt)); 
% 
%        %create means for constants becuase i'm too dumb to figure out
%        %another way. 
% 
%         trTrial(tt,:) = nanmedian(temp_FixOn(temp_FixOn(:,TRIAL_INDEX)==trials(tt), TRIAL_INDEX));
%         trBLOCK(tt,:) = nanmedian(temp_FixOn(temp_FixOn(:,TRIAL_INDEX)==trials(tt), BLOCK));
%         trCOMP(tt,:) = nanmedian(temp_FixOn(temp_FixOn(:,TRIAL_INDEX)==trials(tt), COMP));
%         trLINE(tt,:) = nanmedian(temp_FixOn(temp_FixOn(:,TRIAL_INDEX)==trials(tt), LINE));
%         trRESP(tt,:) = nanmedian(temp_FixOn(temp_FixOn(:,TRIAL_INDEX)==trials(tt), RESP));
% 
%     
%     end
%  
%         temp_CueOn = CueOn;
%     for tt = 1:length(trials)
%         trCueOn(tt,:) = nanmean(temp_CueOn(temp_CueOn(:,TRIAL_INDEX)==trials(tt), pupsize));
%     end
% 
%         temp_CTI = CTI;
%     for tt = 1:length(trials)
%          trCTI(tt,:) = nanmean(temp_CTI(temp_CTI(:,TRIAL_INDEX)==trials(tt), pupsize));
%     end
% 
%         temp_TargOn = TargOn;
%     for tt = 1:length(trials)
%           trTargOn(tt,:) = nanmean(temp_TargOn(temp_TargOn(:,TRIAL_INDEX)==trials(tt), pupsize));
%     end
% 
%         temp_RespMade = RespMade;
%     for tt = 1:length(trials)
%         trRespMade(tt,:) = nanmean(temp_RespMade(temp_RespMade(:,TRIAL_INDEX)==trials(tt), pupsize));
%     end
% 
%         temp_RespWind = RespWind;
%     for tt = 1:length(trials)
%           trRespWind(tt,:) = nanmean(temp_RespWind(temp_RespWind(:,TRIAL_INDEX)==trials(tt), pupsize));
%     end
% 
% 
% 
%   temp_All_2 = [trTrial, trBLOCK, trCOMP, trLINE, trRESP, trfix, trCueOn, trCTI, trTargOn, trRespMade, trRespWind]
% 
%   
% 
% 
% end

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dpb 2022-10-13

You indexed all the intermediate variables, but then put the compendium of them all inside another loop -- you're doing all the inner loops over and over and over ... length(trials) times. Once each should be sufficient. :)

It's not clear what you want the results to be and we don't have any data to work with to try...

Attach a (relatively small) dataset (as a .mat file) would be fine and then explain what results you're trying to get.

It looks like grouping variables might be very useful here to compute a bunch of different statistics/results over an input set of data by some other variable values.

If we knew what those were, probably could vectorize the whole thing, "the MATLAB way..."

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