Dear SAS user forum,
I came across the coding below that is very useful for my work. However, being a basic SAS user I am stuck with getting it to run on SAS 9.4. It seems like there is an issue in then of PROC IML where the table “random” is created.
I really hope that someone can sort this out for me
Kind regards
Hans
DESCRIPTION
This code performs a multi-sample rarefaction. A dataset should consist of columns of values. Each column represents a sample and each row represents a different taxon (e.g., species). Each value represents a number of specimens representing that species. The number of rows with non-zero values represents sample diversity (taxon richness).
************************************************************************
SAS/IML Code written by Michal Kowalewski (Virginia Tech). This version last updated on March 15, 2010
************************************************************************
* THE CODE BEGINS HERE;
OPTIONS linesize=256 pagesize=5000;
*--ENTER THESE MACROVARIABLES;
%let times=100; * - number of iterations to estimate confidence intervals;
*--RAREFACTION MACROVARIABLES;
%let sam=3; * - number of samples to be included in the analysis;
%let cut=3; * - standardized number of specimens per sample;
**********************************************************************;
*ENTER DATA
Note 1: Replace columns of numbers below with your data.
Note 2: You can simply copy-and-paste from Excel or other file containing your data.
Note 3: Modify in according to number of columns present in your dataset, the Loc1-Loc4 line (e.g., if your dataset is composed by 9 columns you should write: loc.1-loc.9).
Note 4: No missing values are allowed.
Note 5: Zero values are allowed.
Note 6: Only integers are allowed.
After completing all above steps you should be ready to run your data. Click on a running man on the SAS taskbar;
**********************************************************************;
Title1'Multi-sample rarefaction (SAS/IML)';
Title2'written by M. Kowalewski, 12/02/2004';
DATA new;
infile cards;
input loc1-loc4;
cards; * this data step will generate a sas-dataset called 'new';
1 3 17 5
2 4 0 4
5 5 0 143
6 6 0 0
17 7 45 0
42 4 8 12
6 2 32 0
0 9 194 0
32 0 4 0
0 17 1 1
1 0 0 2
;
run;
PROC iml;
USE new;
READ all var _num_ into V;
START weight (X,Y);
k=ncol(X);
do i=1 to k;
s=X[1,i];
z=repeat(i,1,s);
zz=zz||z;
end;
zzz=t(zz);
Y=zzz;
FINISH weight;
START ranvec(in,frac,v_out);
k=nrow(in);
v_index=in;
do i=1 to k;
rand=floor((k-i+1)*ranuni(0) + 1);
v_ran=v_ran||v_index[rand];
v_index=remove(v_index,rand);
end;
temp=t(v_ran[,1:frac]);
v_out=temp;
FINISH ranvec;
START recr(X,frac,out);
Z=t(X);
k=ncol(Z);
run weight(Z,Y);
run ranvec(Y,frac,YY);
do i=1 to k;
a=i#(YY=i);
ab=sum(a)/i;
abun=abun//ab;
end;
*out=abun[loc(abun>0),];
out=abun;
FINISH recr;
START FINAL(X,out);
tot=&sam*&cut; *-total number of specimens that will be resampled;
Z=X; * - original data matrix (columns=samples) (rows=species);
index=t(1:ncol(Z));
do i=1 to ×
run ranvec(index,&sam,sam);
rand=Z[,sam]; *-subset of samples, with number of samples defined by the macrovariable "sam";
do j=1 to ncol(rand);
a=rand[,j];
run recr(a,&cut,random);
b=b||random;
c=b[,+];
end;
b=shape(0,nrow(rand),1);
DivR=ncol(loc(c>0));
outt=DivR||&sam||&cut||tot||i;
output=output//outt;
end;
out=output;
FINISH FINAL;
RUN FINAL(V,out);
create random from out;
append from out;
close random;
QUIT;
run;
data report;
set random;
div=col1;
samples=col2;
sam_n=col3;
tot_n=col4;
iter=col5;
drop col1-col5;
proc print;
proc univariate noprint;
var div;
output out=final1 n=iter mean=diver std=std PCTLPRE=P_ PCTLPTS=2.5 25 75 97.5;
proc print;
run;
quit;
