Zach asked . 2022-06-11

Avoiding for loop with anova1

I have 2 large 3D matrices, and I've noticed that passing a DIM argument for the ttest2 function decreases the computational time on my computer substantially as opposed to looping through each index in the first 2 dimensions (near 60-fold for the data I'm working with):

 [~,~,~,stats]=ttest2(A,B,[],[],[],3);

vs.

 stats=cell(rmax,cmax);
 for r=1:rmax
    for c=1:cmax
        [~,~,~,stats{r,c}]=ttest2(A(r,c,:),B(r,c,:));
    end
 end

Now I would like to do the same thing, but adding another matrix C and using anova1 instead of ttest2. The documentation for anova1 suggests that it won't accept a similar DIM argument, but I'm wondering if there's a similar way to cut down on time and avoid looping through each element.

ttest2 , ttest , anova1 , Statistics and Machine Learning Toolbox , ANOVA , Analysis of Variance

Expert Answer

Prashant Kumar answered . 2024-12-21 15:54:43

I tooled around a bit with the anova1 function and reacquainted myself with the math. The below function appears to return the same values as looping through anova1, but a fair bit faster (I didn't time it), as I originally wanted. It could probably be improved substantially by vectorizing, etc., but it gets the job done. As Star Strider pointed out, it should never be used for significance testing unless coupled with a correction for multiple comparisons.

GDIM is the dimension along which groups are defined (columns in anova1) and MDIM is the dimension along which within groups measures are defined (rows in anova1). All F-statistics are calculated independently.

function [F,DFR,DFE]=NDfstat(X,GDIM,MDIM)

if exist('GDIM','var') && exist('MDIM','var')
   D=size(X);
   X=permute(X,[GDIM MDIM setdiff(1:numel(D),[GDIM MDIM])]);
end
D=size(X);
DFE = D(1)-1;
DFR = D(1)*(D(2)-1);

% X = X-repmat(mean(X,2),[1 D(2) ones(1,numel(D)-2)]);
   % anova1 does this, supposedly to improve accuracy, but it
   % seems to destroy calculation. Not sure what the deal is.

xm = squeeze(mean(X,2));
gm = squeeze(mean(xm));
SSE = squeeze(D(2)*sum((xm-repmat(shiftdim(gm,-1),[D(1) ones(1,numel(D)-1)])).^2));
SST = squeeze(sum(sum((X-repmat(shiftdim(gm,-2),[D(1:2) ones(1,numel(D)-1)])).^2)));
SSR = SST-SSE;
F = (SSE/DFE)./(SSR/DFR);

end

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