Hi All,
I need to do some segmentation on variables based on correlation matrix. The correlation matrix is shown as below. If correlation coefficients of some variables in correlation matrix are larger than 0.75 or smaller than -0.75, they will be classified into one subgroup and be stored in one dataset, e.g. dataset_1. Then the variables in dataset_1 will be removed from correlation matrix. Then, if correlation coefficients of some variables in remaining correlation matrix are larger than 0.75 or smaller than -0.75, they will be classified into another subgroup and be stored in another dataset, e.g. dataset_2. Then the variables in dataset_2 will be removed from correlation matrix....................And repeat until no variables have correlation coefficients of above 0.75 or below -0.75. The remaining variables will be stored in one datsets, dataset_n.
_NAME_ | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 | V11 |
V1 | 1 | 0.795283 | 0.648228 | 0.702434 | 0.814356 | 0.898034 | 0.72141 | 0.545573 | 0.410562 | 0.67573 | 0.79505 |
V2 | 0.795282885 | 1 | 0.785185 | 0.852621 | 0.830391 | 0.75202 | 0.817556 | 0.682524 | 0.517509 | 0.8671 | 0.995331 |
V3 | 0.648227521 | 0.785185 | 1 | 0.711466 | 0.690316 | 0.60089 | 0.716786 | 0.552231 | 0.50897 | 0.695645 | 0.824341 |
V4 | 0.702433756 | 0.852621 | 0.711466 | 1 | 0.846026 | 0.662565 | 0.748602 | 0.701647 | 0.457522 | 0.757802 | 0.853448 |
V5 | 0.81435639 | 0.830391 | 0.690316 | 0.846026 | 1 | 0.817881 | 0.772293 | 0.523626 | 0.398964 | 0.778101 | 0.829205 |
V6 | 0.898034025 | 0.75202 | 0.60089 | 0.662565 | 0.817881 | 1 | 0.707077 | 0.457641 | 0.329565 | 0.679927 | 0.750266 |
V7 | 0.721409554 | 0.817556 | 0.716786 | 0.748602 | 0.772293 | 0.707077 | 1 | 0.604067 | 0.517373 | 0.788003 | 0.819051 |
V8 | 0.545573147 | 0.682524 | 0.552231 | 0.701647 | 0.523626 | 0.457641 | 0.604067 | 1 | 0.585093 | 0.584004 | 0.681971 |
V9 | 0.410562159 | 0.517509 | 0.50897 | 0.457522 | 0.398964 | 0.329565 | 0.517373 | 0.585093 | 1 | 0.485006 | 0.522883 |
V10 | 0.675730408 | 0.8671 | 0.695645 | 0.757802 | 0.778101 | 0.679927 | 0.788003 | 0.584004 | 0.485006 | 1 | 0.860323 |
V11 | 0.795050139 | 0.995331 | 0.824341 | 0.853448 | 0.829205 | 0.750266 | 0.819051 | 0.681971 | 0.522883 | 0.860323 | 1 |
Is there one solution to do this?
Thanks in advance
MT.
Are you doing some analysis Like decision tree ?
I pick up 0.2 as correlation coefficient benchmark .
data x;
array x{*} a1-a20;
do j=1 to 100;
do i=1 to dim(x);
x{i}=ranuni(1234);
end;
output;
end;
drop i j;
run;
%let corr=0.2;
proc sql noprint;
select name into : list separated by ' '
from dictionary.columns
where libname='WORK' and memname='X';
quit;
%macro decision;
%let i=1;
%do %while(1);
proc corr data=x outp=person(where=(_TYPE_='CORR')) noprint;
var &list ;
run;
data temp;
set person;
array x{*} a: ;
length var $ 40;
do i=1 to dim(x);
if x{i} eq 1 then leave;
else if abs(x{i}) ge &corr then do;
corr=x{i};
var=_name_; output;
var=vname(x{i}); output;
end;
end;
keep corr var;
run;
proc sql ;
create table data_&i as select distinct var from temp;
%if &sqlobs = 0 %then %do;
create table data_&i as select _name_ as var from person; quit;
%return;
%end;
select name into : list separated by ' '
from dictionary.columns
where libname='WORK' and memname='PERSON' and name not in (select distinct var from temp) and name not in ('_NAME_' '_TYPE_');
quit;
%let i=%eval(&i+1) ;
%end;
%mend decision;
%decision
Ksharp
消息编辑者为:xia keshan
Are you doing some analysis Like decision tree ?
I pick up 0.2 as correlation coefficient benchmark .
data x;
array x{*} a1-a20;
do j=1 to 100;
do i=1 to dim(x);
x{i}=ranuni(1234);
end;
output;
end;
drop i j;
run;
%let corr=0.2;
proc sql noprint;
select name into : list separated by ' '
from dictionary.columns
where libname='WORK' and memname='X';
quit;
%macro decision;
%let i=1;
%do %while(1);
proc corr data=x outp=person(where=(_TYPE_='CORR')) noprint;
var &list ;
run;
data temp;
set person;
array x{*} a: ;
length var $ 40;
do i=1 to dim(x);
if x{i} eq 1 then leave;
else if abs(x{i}) ge &corr then do;
corr=x{i};
var=_name_; output;
var=vname(x{i}); output;
end;
end;
keep corr var;
run;
proc sql ;
create table data_&i as select distinct var from temp;
%if &sqlobs = 0 %then %do;
create table data_&i as select _name_ as var from person; quit;
%return;
%end;
select name into : list separated by ' '
from dictionary.columns
where libname='WORK' and memname='PERSON' and name not in (select distinct var from temp) and name not in ('_NAME_' '_TYPE_');
quit;
%let i=%eval(&i+1) ;
%end;
%mend decision;
%decision
Ksharp
消息编辑者为:xia keshan
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 25. Read more here about why you should contribute and what is in it for you!
Learn how use the CAT functions in SAS to join values from multiple variables into a single value.
Find more tutorials on the SAS Users YouTube channel.