Stepwise fit wont run through loop, can't find a way around the problem.

You will see from my code below That I load some data, then in a for loop a adjust it according to a pre-determined lag and run a stepwise regression.

The loop runs through 1505 columns of 96 rows. however, the first 47 columns are all NaN (though I need to keep them) and there are other columns full of NaN in there too.

The problem is that I get the following error

"Attempted to access R(1); index out of bounds because numel®=0."

This makes sense, because the first column is all NaN. I have tried getting around the problem using the following

         if isnan(Nc)
            continue
        else
%stepwise regression performed
        end

But that results in the following error

"Subscripted assignment dimension mismatch.

Error in Shift_decomposed_data2_hdg (line 101) xxn(:,xip)=xx(:,k);"

So I am stumped and would like a solution if anyone can think of one. Thank you in advance.

Here is the code, Including loading the data

    clear all;
  clc

lag = load ('lagtime.txt'); %load the lag for each pixel 

      A1 = load ('Lag_Results\data\Anomalies\TWSA1anomalies.mat');
      Approx1x = A1.TWSA1anomalies;
      Approx1y = reshape(Approx1x,[1505,96]);
      Approx1 = Approx1y';

      A2 = load ('Lag_Results\data\Anomalies\TWSA2anomalies.mat');
      Approx2x = A2.TWSA2anomalies;
      Approx2y = reshape(Approx2x,[1505,96]);
      Approx2 = Approx2y';

      A3 = load ('Lag_Results\data\Anomalies\TWSA3anomalies.mat');
      Approx3x = A3.TWSA3anomalies;
      Approx3y = reshape(Approx3x,[1505,96]);
      Approx3 = Approx3y';

      A4 = load ('Lag_Results\data\Anomalies\TWSA4anomalies.mat');
      Approx4x = A4.TWSA4anomalies;
      Approx4y = reshape(Approx4x,[1505,96]);
      Approx4 = Approx4y';

          D1 = load ('Lag_Results\data\Anomalies\TWSD1anomalies.mat');
      Det1x = D1.TWSD1anomalies;
      Det1y = reshape(Det1x,[1505,96]);
      Det1 = Det1y';

      D2 = load ('Lag_Results\data\Anomalies\TWSD2anomalies.mat');
      Det2x = D2.TWSD2anomalies;
      Det2y = reshape(Det2x,[1505,96]);
      Det2 = Det2y';

      D3 = load ('Lag_Results\data\Anomalies\TWSD3anomalies.mat');
      Det3x = D3.TWSD3anomalies;
      Det3y = reshape(Det3x,[1505,96]);
      Det3 = Det3y';

      D4 = load ('Lag_Results\data\Anomalies\TWSD4anomalies.mat');
      Det4x = D4.TWSD4anomalies;
      Det4y = reshape(Det4x,[1505,96]);
      Det4 = Det4y';

      NDVIdata = load ('Lag_Results\data\Anomalies\NDVIanomalies.mat');
      NDVIx = NDVIdata.NDVIanomalies;
      NDVIy = reshape(NDVIx,[1505,96]);
      NDVI = NDVIy';

      for j = 1:1505

         m = lag (1,j)

        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   %9 refers to previous 9 variables including intecept
              if INMODEL(1,k)==1
                  xip=xip+1;
                  xxn(:,xip)=xx(:,k);
              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

[dpb]

Subscripted assignment dimension mismatch.
Error in Shift_decomposed_data2_hdg (line 101) xxn(:,xip)=xx(:,k);

Not easy to tell just from code w/o any data to be able to know sizes but the problem is the length of the LHS in the assignment is not the same as that of the RHS. Set a breakpoint at the line and see why...I'd guess there's a logic error that will be an "aha!" kind of thing when you see it.

[Robert]

I still can't nut it out, here is the data, any extra assistance would be a fantastic help

[dpb]

So, what were the sizes in question when you ran the debugging session (specifically, xip and k)?

[Robert]

So you're saying xxn(:,xip) cannot equal xx(:,k) because xip and k are different lengths? Sorry to be so naive, I am by no means a programmer and this stuff is totally foreign to me, to be honest I don't understand what a lot of the code actually means

[dpb]

If'en

length(xip)~=length(k)

then, no, you can't store the k values on the RHS of the assignment into the the xip target locations on the LHS. There are either too many to fit or to few to assign one for each depending upon which way the inequality works.

One way to hold disparate-sized objects/arrays is in a cell array...

What is a cell array?