How do I stop overwriting in this "for" loop
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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
1 Kommentar
dpb
am 13 Okt. 2022
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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