I have n forests that I want to compute Moristia index. I have the code for one forest but I do not know how to repeat the program n times. The code for Morisita Index is the following: data data;   infile "B:\sas\data.csv" delimiter=',' firstobs=2; * read the csv data with coordinates of the trees and multiple forests;   input forestid $ x y diameter;   if forestid ne 1 then delete; * select only one forest; run; *start Morosita's index computations; proc iml; /** Count the number of points in each cell of a 2D grid.     Input: p    - a kx2 matrix of 2D points            XDiv - a subdivision of the X coordinate direction            YDiv - a subdivision of the Y coordinate direction     Output: The number of points in p that are contained in             each cell of the grid defined by XDiv and YDiv. **/ start Bin2D(p, XDiv, YDiv);    x = p[,1];       y = p[,2];    Nx = ncol(XDiv); Ny = ncol(YDiv);    /** Loop over the cells. Do not loop over observations! **/    count = j(Ny-1, Nx-1, 0); /** initialize counts for each cell **/    do i = 1 to Ny-1; /** for each row in grid **/       idx = loc((y >= YDiv) & (y < YDiv[i+1]));       if ncol(idx) > 0 then do;          t = x[idx]; /** extract point in this row (if any) **/          do j = 1 to Nx-1; /** count points in each column **/             count[i,j] = sum((t >= XDiv ) & (t < XDiv[j+1]));          end;       end;    end;    return( count ); finish; use data ; *bring external data into IML environment (SAS data but not data curently inside IML procedure); read all var {x y} into p[colname=varNames]; *place your data in the matrix p that can be used by bining module; /** define bins: 50 columns and 50 rows on [0,1000]x[0,1000] **/ cellsize=100; XMin = 0; XMax = 1000; dx = cellsize; YMin = 0; YMax = 1000; dy = cellsize; XDiv = do(XMin, XMax, dx); YDiv = do(YMin, YMax, dy); count = Bin2D(p, XDiv, YDiv); *create the dataset for MORISITA Index of Dispersion (1959); create quad from count; append from count; close quad; run;quit; *MORISITA Index of Dispersion (1959); data quad;   set quad;   index=catx("_", "Row",_n_); *create a new varaibale that concatenate the word Row with the row counting; run; *prepare data for computing Morisita index; proc sort data=quad;   by index; run; *create one column from the matrix counting the points inside bins; Proc transpose data=quad out=mor (rename=(col1=count));   by index; run; *start computing Morisita index; data mor;   set mor;   c=count*(count-1); run; proc means data=mor noprint;   output out=morisita sum(c)=sumc sum(count)=points ; run;quit; data morisita;   set morisita;   MInd=sumc*_freq_/(points*(points-1));   pvalue=1-probchi((mind*(points-1)+_freq_-points)/(_freq_-1),_freq_-1); run; proc print data=morisita; run; quit; What a want is to have the Morisita's index computed for all forests and saved in one dataset. I tried macro, as they seems to be recommended to do repetitive tasks, but the tutorials and helps are designed for so simple tasks that I did not know why one should bother using them, while the complicated programs are so complicated that are useless. I was expecting some examples for this kind of situations, as it seems to be encountered very often in practice. Any advice? ... View more