Matlab stepwise fit wont run through loop - matlab

Question

I am a relatively new Matlab user and possibly biting off more than I can chew with this code. Basically it loops through a stepwise regression time in all of the 1505 columns that have data (it is important that the columns without data are kept)
At the start of the code, the length of some columns is changed due to a lag (predetermined in another code). I think this is causing the problem as I get a dimension mismatch further down in the code (indicated below by a comment). I am quite stuck and not sure how to fix the problem. I would really appreciate anything that is obvious being pointed out to me.
Thanks in advance for any help!
for j = 1:1505
m = lag (1,j) %this is a number between 1 and 6 indicating the best lag
Nc=NDVI(m+1:end,j);
A1c=Approx1 (1:end-m,j);
A2c=Approx2 (1:end-m,j);
A3c=Approx3 (1:end-m,j);
A4c=Approx4 (1:end-m,j);
D1c=Det1 (1:end-m,j);
D2c=Det2 (1:end-m,j);
D3c=Det3 (1:end-m,j);
D4c=Det4 (1:end-m,j);
xx=[A1c, A2c, A3c, A4c, D1c, D2c, D3c, D4c];
yy = Nc;
%Begin Stepwise Regression
if isnan(Nc)
continue
else
[B,SE,PVAL,INMODEL,STATS,NEXTSTEP,HISTORY]= ...
stepwisefit(xx,yy,'penter',.05);
inApprox1(j)=INMODEL(1);
inApprox2(j)=INMODEL(2);
inApprox3(j)=INMODEL(3);
inApprox4(j)=INMODEL(4);
inDpprox7(j)=INMODEL(5);
inDpprox8(j)=INMODEL(6);
inDpprox9(j)=INMODEL(7);
inDpprox10(j)=INMODEL(8);
sstotApprox1(j)=STATS.SStotal; %calculate R^2
ssresidApprox1(j)=STATS.SSresid;
rsq = 1- ssresidApprox1./sstotApprox1
rsq(rsq==Inf) = NaN %Set Inf to NaN
rmse(j)=STATS.rmse; %Extract rmse
rmse(rmse==Inf) = NaN; %Set Inf to NaN
% repeat regresson only on the sigificant variables
if sum(INMODEL,2)>0
xip=0;
for k=1:8 %8 refers to previous 8 variables including intecept
if INMODEL(1,k)==1
xip=xip+1;
xxn(:,xip)=xx(:,k); %ERROR HERE, xip AND k DIMENSION MISMATCH. UNSURE HOW TO SOLVE
end
end
[Bn,SEn,PVALn,INMODELn,STATSn,NEXTSTEPn,HISTORYn]= ...
stepwisefit(xxn,yy,'penter',.05);
rmsen(j)=STATSn.rmse; %Extract rmse
rmsen(rmse==Inf) = NaN; %Set Inf to NaN
end
end
end

Answer 1

Your code bombs because you are not clearing your xxn variable for each new lag. Instead, because your xxn is persisting, I believe that it will end up having the wrong size (the wrong number of rows) the next time you go through the big outermost loop.
In my opinion, you should initialize your xxn variable to zeros of the right size. You could do it in this section of your code...
% repeat regresson only on the sigificant variables
if sum(INMODEL,2)>0
xxn = zeros(size(xx,1),8); %ADD THIS LINE! Initializes to zero.
xip=0;
for k=1:8 %8 refers to previous 8 variables including intecept
if INMODEL(1,k)==1
xip=xip+1;
xxn(:,xip)=xx(:,k); %these should now be the correct dimension
end
end