topic Re: Collinearity Diagnostics Using the Information Matrix. in Statistical Procedures

Collinearity Diagnostics Using the Information Matrix.

JonDickens1607 — Wed, 01 Jun 2016 22:20:14 GMT

I am looking for a SAS Macro that provides Collinearity Diagnostics Using the Information Matrix within Binary Logistic Regression.

I believe that such a macro was constructed in 2003 and then updated in 2010.

However searching via Google, LinkedIn and SAS.Support, I have only been able to find references to the macro

but not the actual code or any documentation on this macro.

I would be most grateful if someone could supply me with a well documented and functionally robust version of this SAS macro.

Thank You

Re: Collinearity Diagnostics Using the Information Matrix.

Reeza — Thu, 02 Jun 2016 03:58:34 GMT

What's the name of macro for starters? Do you have any more information, the original author perhaps?

@JonDickens1607 wrote:

I would be most grateful if someone could supply me with a well documented and functionally robust version of this SAS macro.

That may too big of a request, for a forum.

EDIT:

Is this helpful, it seems to be what you're asking for...within the limits of what you've provided.

http://support.sas.com/kb/32/471.html

Re: Collinearity Diagnostics Using the Information Matrix.

JonDickens1607 — Thu, 02 Jun 2016 09:12:06 GMT

Author's_Surnames IN ( 'Zack' , 'Singleton' , 'Wall' , 'Delaney' ) ;

Title 'Collinearity Diagnostics Using the Information Matrix.' ;

/* The Collinearity Diagnostic Problem is partially resolved when using
PROC REG and the Model Options: VIF COLLIN, in Linear Rgession.
However these diagnostic options are not available in PROC LOGISTIC.
The Multi-Collinearity Problem is a function of the "Independent"
variable
structure and does not really involve the dependent variable itself. */

/* I think that the name of the original macro was %COLLIN and I have
a copy of this macro but it does not work and I get a variety of errors
*/

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Re: Collinearity Diagnostics Using the Information Matrix.

Reeza — Thu, 02 Jun 2016 15:14:06 GMT

Do you have a SAS IML license? The macro requires this.

You can check by running:

Proc setinit;
Run;

Post how you called the macro as well as the macro.

Re: Collinearity Diagnostics Using the Information Matrix.

JonDickens1607 — Thu, 02 Jun 2016 19:33:06 GMT

SAS Macro Attached as requested.

Please check this line: FINAL = (MUSQR||CI||NMISSING||PI`)`; /* The single
quote commas?? */

/*
Specify the COVOUT and the OUTEST = SASDSN options in the PROC statement.

Then, specify the SAS Data Set (SASDSN) in the Macro Variable, COVDSN */

I ran Proc Logistic on my data set with the above options in order to
generate the COV Matrix as a SAS Data Set.

I then called the SAS Macro with the COVDSN = COV Matrix Data Set Parameter.

%COLLIN(COVDSN = );

I am using SAS University Edition 3.5

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Re: Collinearity Diagnostics Using the Information Matrix.

Reeza — Thu, 02 Jun 2016 19:43:35 GMT

You can only include attachments on the website not via email.

Re: Collinearity Diagnostics Using the Information Matrix.

Reeza — Thu, 02 Jun 2016 19:44:21 GMT

Please place the code in the editor, I dislike downloading attachments.

Re: Collinearity Diagnostics Using the Information Matrix.

JonDickens1607 — Thu, 02 Jun 2016 19:48:06 GMT

/*
=================================================================================
*/

/* logistic regression Collinearity diagnostics using the information
matrix */

/*
=================================================================================
*/

%macro collin(covdsn=);

/* Macro to calculate collinearity diagnostics from the
variance-covariance matrix in

nonlinear regression. in the nonlinear regression program (PROC
LOGISTIC or PROC

PHREG), specify the COVOUT and the OUTEST = SASdsn options in the PROC
statement.

Then, specify the SAS data set (SASdsn) in the macro variable, COVDSN,
when you

invoke this macro. This macro does not work with PROC GENMOD. */

%if (&covdsn ne )

%then %do; /* start of [ %if (&covdsn ne ) %then %do loop ] */

/* option mprint */

%let __stop = 0;

proc iml; /* read the variance – covariance matrix from proc
logistic */

use &covdsn;

read all var {_name_} into _varname;

_nrvname = nrow(_varname);

if (_nrvname>1)

then do;

_varnam2 = _varname(|2:_nrvname, |);

nmissing = j(nrow(_varnam2),1,.);

labels = {"Eigenval","CondIndx"," "};

_varnam2 = labels//_varnam2;

free _varname labels;

read all var _num_ into varcov(|colname=_nvname|);

_nrcvc = ncol(varcov);

lastvnam = _nvname(|1,_nrcvc|);

if (lastvnam = "_LNLIKE_")

then varcov2 = varcov(|2:_nrvname,1:_nrcvc-1|);

if (lastvnam^ = "_LNLIKE_") then varcov2 =
varcov(|2:_nrvname,|);

free varcov _nrcvc lastvnam;

covbinv = inv(varcov2);

scale = inv(sqrt(diag(covbinv)));

r = scale*covbinv*scale;

free covbinv scale;

call eigen(musqr,v,r);

free r;

srootmus = sqrt(musqr);

ci = 1/(srootmus/max(srootmus));

phi = (v##2)*diag(musqr##(-1));

sumphi = phi(|,+|);

pi = phi#(sumphi##(-1));

free phi sumphi srootmus v;

final = (musqr||ci||nmissing||pi`)`;

free pi musqr ci nmissing;

_ncfinal = ncol(final);

_nrfinal = nrow(final);

final2=j(_nrfinal,_ncfinal,0);

_ncfp1=_ncfinal+1;

__vdp="VDP";

do i=1 to _ncfinal;

final2(|,_ncfp1-i|)=final(|,i|);

x=char(i,3);

y=compress(concat(__vdp,x));

if i=1 then _vdpname=y;

else _vdpname=_vdpname||y;

end;

free final _nrfinal _ncfinal i x y;

create final2 from final2(|rowname=_varnam2
colname=_vdpname|);

append from final2(|rowname=_varnam2 colname=_vdpname|);

free _varnam2 _vdpname final2;

end;

if (_nrvname=1)

then do;

x="1";

call symput("__stop",left(x));

print " ";

print
"**********************************************************";

print "You need to specify the COVOUT option";

print " in either PROC LOGISTIC or PROC PHREG.";

print " This program will not calculate collinearity
diagnostics.";

print
"**********************************************************";

print " ";

end;

quit;

run;

%if (&__stop eq 0)

%then %do;

proc print

data=final2 label noobs;

id _varnam2;

title8 "Collinearity diagnostics for nonlinear models
using";

title9 "the information matrix: Eigenvalues, condition
indexes,";

title10 "and variance decomposition proportions (VDP's)";

label _varnam2="Variable";

run;

%end; /* end of proc print do loop */

%end; /* end of [ %if (&covdsn ne ) %then %do loop ] */

%else %do; /* start of [ %if (&covdsn ne ) %else %do loop ] */

%put;

%put "*******************************************************";

%put " When you invoke this macro, you have to specify the name";

%put " of a SAS data set that contains the variance-covariance";

%put " matrix from either PROC LOGISTIC or PROC PHREG.";

%put;

%put " You can create this matrix by including the following options";

%put " on the PROC statement: COVOUT and OUTEST=SASdsn,";

%put " where SASdsn is the name of the SAS data set containing";

%put " the variance-covariance matrix.";

%put "*******************************************************";

%put;

%end; /* end of [ %if (&covdsn ne ) else %do loop ] */

%mend collin; /* end of macro collin definition */

%collin(covdsn = ); /* call macro collin with variance-covariance data
set name */

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Re: Collinearity Diagnostics Using the Information Matrix.

ballardw — Thu, 02 Jun 2016 20:56:09 GMT

/* I think that the name of the original macro was %COLLIN and I have
a copy of this macro but it does not work and I get a variety of errors
*/

Post log with options MPRINT turned on with the error messages.

Re: Collinearity Diagnostics Using the Information Matrix.

JonDickens1607 — Thu, 02 Jun 2016 22:17:06 GMT

TITLE 'STEPWISE REGRESSION ON SQ01_WOE -- SQ59_WOE DATA'; PROC LOGISTIC
DATA = DATA12345_WOE OUTEST = COVBETAS COVOUT; MODEL GBFLAG(EVENT = '1') =
SQ01_WOE -- SQ59_WOE / SELECTION = STEPWISE SLENTRY = 0.10 SLSTAY = 0.15
DETAILS LACKFIT CTABLE ; OUTPUT OUT = PRED P = PHAT LOWER = LCL UPPER = UCL
PREDPROB = (INDIVIDUAL CROSSVALIDATE); RUN;

/* CHECK THE OUTPUT DATA SET COVBETAS */

PROC CONTENTS

DATA = COVBETAS ;
RUN;

PROC PRINT

DATA = COVBETAS ;
RUN;

%COLLIN*(COVDSN = COVBETAS );

1 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK;
55
56 %COLLIN*(COVDSN = COVBETAS );
"*******************************************************"
" WHEN YOU INVOKE THIS MACRO, YOU HAVE TO SPECIFY THE NAME"
" OF A SAS DATA SET THAT CONTAINS THE VARIANCE-COVARIANCE"
" MATRIX FROM EITHER PROC LOGISTIC OR PROC PHREG."
" YOU CAN CREATE THIS MATRIX BY INCLUDING THE FOLLOWING OPTIONS"
" ON THE PROC STATEMENT: COVOUT AND OUTEST=SASDSN,"
" WHERE SASDSN IS THE NAME OF THE SAS DATA SET CONTAINING"
" THE VARIANCE-COVARIANCE MATRIX."
"*******************************************************"
57
58 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK;
70

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Re: Collinearity Diagnostics Using the Information Matrix.

JonDickens1607 — Thu, 02 Jun 2016 22:27:29 GMT

I am using SAS University Edistion 3.5 which includes PROC IML

Re: Collinearity Diagnostics Using the Information Matrix.

JonDickens1607 — Thu, 02 Jun 2016 22:56:00 GMT

Previous post included a typing error in the SAS Code calling the Macro Collin.

I have attached the SAS LOG File that is generated when I call the macro

%COLLIN(COVDSN = COVBETAS );

I have also attached the SAS Program Code

Re: Collinearity Diagnostics Using the Information Matrix.

Reeza — Fri, 03 Jun 2016 03:23:10 GMT

Found one that worked. I have no idea if this is statistically correct, only program wise.

Macro definition:

%macro collin(covdsn=, procdr=, parminfo=);
%if %upcase(&procdr)=GENMOD %then %do;
data next_1;
 set &parminfo;
 attrib parnum format=$25.;
 parnum=parameter;
 if parnum='Prm1' then parnum='Prm01';
 if parnum='Prm2' then parnum='Prm02';
 if parnum='Prm3' then parnum='Prm03';
 if parnum='Prm4' then parnum='Prm04';
 if parnum='Prm5' then parnum='Prm05';
 if parnum='Prm6' then parnum='Prm06';
 if parnum='Prm7' then parnum='Prm07';
 if parnum='Prm8' then parnum='Prm08';
 if parnum='Prm9' then parnum='Prm09';
 if parnum='Prm10' then parnum='Prm10';
 if parnum='Prm11' then parnum='Prm11';
 if parnum='Prm12' then parnum='Prm12';
 if parnum='Prm13' then parnum='Prm13';
 if parnum='Prm14' then parnum='Prm14';
 if parnum='Prm15' then parnum='Prm15';
 if parnum='Prm16' then parnum='Prm16';
 if parnum='Prm17' then parnum='Prm17';
 if parnum='Prm18' then parnum='Prm18';
 if parnum='Prm19' then parnum='Prm19';
 if parnum='Prm20' then parnum='Prm20'
;
 rename parnum=parm;
run;
proc sort data=next_1;
 by parm;
run;
data next_1a;
 set &covdsn;
 attrib parm format=$25.
;
 parm=rowname;
 if parm='Prm1' then parm='Prm01'
;
 if parm='Prm2' then parm='Prm02'
;
 if parm='Prm3' then parm='Prm03'
;
 if parm='Prm4' then parm='Prm04'
;
 if parm='Prm5' then parm='Prm05'
;
 if parm='Prm6' then parm='Prm06'
;
 if parm='Prm7' then parm='Prm07'
;
 if parm='Prm8' then parm='Prm08'
;
 if parm='Prm9' then parm='Prm09'
;
 if parm='Prm10' then parm='Prm10'
;
 if parm='Prm11' then parm='Prm11'
;
 if parm='Prm12' then parm='Prm12'
;
 if parm='Prm13' then parm='Prm13'
;
 if parm='Prm14' then parm='Prm14'
;
 if parm='Prm15' then parm='Prm15'
;
 if parm='Prm16' then parm='Prm16'
;
 if parm='Prm17' then parm
='Prm17'
;
 if parm='Prm18' then parm='Prm18'
;
 if parm='Prm19' then parm='Prm19'
;
 if parm='Prm20' then parm='Prm20'
;
run;
proc sort data=next_1a;
 by parm;
run;
data next_2(drop=effect);
 merge next_1a( in=in1a)
 next_1 (in=in1);
 by parm;
 if in1a;
 parm=effect;
 rename parm=_name_;
run;
data next_3;
 set next_2;
 if _name_='SCALE' then delete;
run;
data next_4;
 length _name_ $25
;
 _name_= 'ESTIMATE'
;
 output;
run;

data next_5;
 set next_4
 next_3;
run;
proc print data=next_5;
 title Input dataset--GENMOD;
run;
%end;
%else %do;
data next_5;
 set &covdsn;
run;
proc print data=next_5;
 title Input dataset--LOGISTIC/PHREG;
run;
%end;
%if (next_5 ne ) %then %do;
%let __stop=0;
proc iml;
 use next_5;
 read all var {_name_} into _varname;
 _nrvname=nrow(_varname);
 if (_nrvname>1) then do;
 _varnam2=_varname(|2:_nrvname, |);
 nmissing=j(nrow(_varnam2),1,.);
 labels={"EIGENVAL","CONDINDX"," "};
 _varnam2=labels//_varnam2;
 free _varname labels;
 read all var _num_ into varcov(|colname=_nvname|);
 _nrcvc=ncol(varcov);
 lastvnam=_nvname(|1,_nrcvc|);
 if (lastvnam="_LNLIKE_") then
varcov2=varcov(|2:_nrvname,1:_nrcvc-1|);
 if (lastvnam^="_LNLIKE_") then varcov2=varcov(|2:_nrvname,|);
%* If the covariance matrix is from GENMOD using the repeated measured
design, ;
%* then the lower diagonal will have the correlations and the upper
diagonal will have;
%* the covariances. The next section of code replaces the lower
diagonal with the upper;
%* diagonal to make a symmetric matrix. If the matrix is symmetrical
already, then the;
%* next section of code will not affect anything.;
 vc2_c=ncol(varcov2);
 vc2_r=nrow(varcov2);
 do cl=1 to vc2_c;
 do rw=1 to vc2_r;
 varcov2(|rw,cl|) = varcov2(|cl,rw|);
 end;
 end;
 print varcov2;
 free varcov _nrcvc lastvnam vc2_c vc2_r cl;
 covbinv=inv(varcov2);
 scale=inv(sqrt(diag(covbinv)));
 r=scale*covbinv*scale;
 free covbinv scale;
 call eigen(musqr,v,r);
 free r;
 srootmus=sqrt(musqr);
 ci=1/(srootmus/max(srootmus));
 phi=(v##2)*diag(musqr##(-1));
 sumphi=phi(|,+|);
 pi=phi#(sumphi##(-1));
 free phi sumphi srootmus v;
 final=(musqr||ci||nmissing||pi`)`;
 free pi musqr ci nmissing;
 _ncfinal=ncol(final);
 _nrfinal=nrow(final);
 final2=j(_nrfinal,_ncfinal,0);
 _ncfp1=_ncfinal+1;
 __vdp="VDP";
 do i=1 to _ncfinal;
 final2(|,_ncfp1-i|)=final(|,i|);
 x=char(i,3);
 y=compress(concat(__vdp,x));
 if i=1 then _vdpname=y;
 else _vdpname=_vdpname||y;
 end;
 free final _nrfinal _ncfinal i x y;
 create final2 from final2(|rowname=_varnam2 colname=_vdpname|);
 append from final2(|rowname=_varnam2|);
 free _varnam2 _vdpname final2;
 end;
 if (_nrvname=1) then do;
 x="1";
 call symput("__stop",left(x));
 print " ";
 print
"**********************************************************";
 print "You need to specify the covout option";
 print "in either proc logistic or proc phreg.";
 print "This program will not calculate collinearity
diagnostics.";
 print
"**********************************************************";
 print " ";
 end;
 quit;
run;
%if (&__stop eq 0) %then %do;
proc print data=final2 label noobs;
 id _varnam2;
 title8 "Collinearity diagnostics for nonlinear models using";
 title9 "the information matrix: Eigenvalues, Condition Indexes,";
 title10 "and Variance Decomposition Proportions (VDPs)";
 label _varnam2="VARIABLE";
run;
%end;
%end;
%else %do;
 %put;
 %put "*******************************************************";
 %put "When you invoke this macro, you have to specify the name";
 %put "of a SAS data set that contains the variance-covariance";
 %put "matrix from LOGISTIC, PHREG, or GENMOD.";
 %put;
 %put "For more information, see the macro code (comments";
 %put "are included with instructions.";
 %put "*******************************************************";
 %put;
%end;
proc datasets;
 delete next_1 next_1a next_2 next_3 next_4 next_5;
run;
quit;
%mend collin;

Sample execution:

proc logistic data=sashelp.heart covout outest=sasdsn;
class chol_status sex;
model status= chol_status ageatstart sex height;
run;

options mprint symbolgen;
%collin(covdsn=sasdsn);

Source: https://etd.library.emory.edu/file/view/pid/emory:939x5/etd/emory:939w1/kharod_dissertation.pdf