## 在SAS中作相关矩阵图

Corrgrams (Corrgrams: Exploratory displays for correlation matrices，Friendly,2002)中提供了作相关矩阵图像的方法。我的方法是先用gplot作出图中的圆心，然后利用gplot中的标注(annotate)作出圆及扇形。其中红色表示两个变量正相关，而蓝色表示负相关。最后将坐标系的颜色设为白色。

%macro     plotgraph(corrdata);

data corrmatrix(drop= _TYPE_ _NAME_);
set &corrdata(firstobs=4);
run;

proc sql noprint;
select count(name) into :nvars
from dictionary.columns
where libname=upcase("work") and memname=upcase("corrmatrix");
select name into :name1-:name%left(&nvars)
from dictionary.columns
where libname=upcase("work") and memname=upcase("corrmatrix");
Quit;

data corrdata(keep=corr);
set corrmatrix;
%do i=1 %to &nvars;
corr=&&name&i;
output;
%end;
run;

%annomac;
data annodata;
set corrdata;
retain xsys ysys '2' hsys '3';
center_x=mod(_N_-1,&nvars);
center_y=&nvars-ceil(_N_/&nvars);
if _N_<=&nvars then do;
function='label';
color='black';
x=center_x;y=center_y+0.8;
style='SIMPLEX';
size=3;
rotate=0;
text=symget('name'||left(_N_));
output;
function='label';
color='black';
x=-0.8;y=&nvars-_N_;
style='SIMPLEX';
size=3;
rotate=0;
text=symget('name'||left(_N_));
output;
end;
if corr >0 then do;
x=center_x;
y=center_y;
angle=0;
rotate=corr*360;
color='red';
style='p3n45';
line=3;
output;
end;
else do;
x=center_x;
y=center_y;
angle=0;
rotate=abs(corr)*360;
color='blue';
style='p3n45';
line=3;
output;
end;
run;

/*remove duplicates*/
proc sql noprint;
create table plotdata as
select distinct center_x,center_y
from annodata;
quit;

goption reset=all;
goption device=gif hsize=400 pt vsize=400 pt;
symbol1 v=point;
AXIS1  ORDER =(-1 TO &nvars BY 1) origin=(10 pct,10 pct)
COLOR=white;/*disappear the axis*/
proc gplot data=plotdata;
plot center_y*center_x/ haxis=axis1 vaxis=axis1
nolegend
annotate=annodata;
run;
quit;
%mend;
%macro corrgraph(libname,dataset);
proc sql noprint;
select count(name) into :nvars
from dictionary.columns
where libname=upcase("&libname") and memname=upcase("&dataset");
select name into :varname separated by ' '
from dictionary.columns
where libname=upcase("&libname") and memname=upcase("&dataset");
quit;
proc corr data=&dataset pearson spearman hoeffding
outp=Pearson_data outs=Spearman_data
outh=Hoeffding_data;
var &varname;
run;
%plotgraph(Pearson_data);
/*%plotgraph(Spearman_data);
%plotgraph(Hoeffding_data);*/
%mend;

data fitness;
input Age Weight Runtime Oxygen @@;
datalines;
57 73.37 12.63 39.407   54 79.38 11.17 46.080
52 76.32 9.63  45.441   50 70.87 8.92    .
51 67.25 11.08 45.118   54 91.63 12.88 39.203
51 73.71 10.47 45.790   57 59.08 9.93  50.545
49 76.32  .    48.673   48 61.24 11.5  47.920
52 82.78 10.5  47.467   44 73.03 10.13 50.541
45 87.66 14.03 37.388   45 66.45 11.12 44.754
47 79.15 10.6  47.273   54 83.12 10.33 51.855
49 81.42 8.95  40.836   51 77.91 10.00 46.672
48 91.63 10.25 46.774   49 73.37 10.08 50.388
44 89.47 11.37 44.609   40 75.07 10.07 45.313
44 85.84 8.65  54.297   42 68.15 8.17  59.571
38 89.02 9.22  49.874   47 77.45 11.63 44.811
40 75.98 11.95 45.681   43 81.19 10.85 49.091
44 81.42 13.08 39.442   38 81.87 8.63  60.055
;
run;

%corrgraph(work,fitness);

posted on 2010-05-27 15:51 zgw21cn 阅读(...) 评论(...) 编辑 收藏

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