Journal quaity regression output table

[GKati]

Hello Everyone,

Following on the post by zzecon

(https://communities.sas.com/t5/SAS-Procedures/Making-a-publication-quality-table-from-regression-res...)

I would also like to produce regression output like this one:

Using the following code, I get a slightly diffferent output.

/*write a dataset with standard error on the same column of coefficient*/

data parmest_;

set work.parmest;

if not missing(stderr) and variable not in ('R-Square','Root MSE','N. Obs.') then do;

value=estimate; type='coefficient'; output; end;

if not missing(stderr) then do; value=stderr; type='stderr' ;output; end;

if variable in ('R-Square','Root MSE','N. Obs.') then do; value=estimate; type=variable ;output; end;

run;

proc format;

picture stderrf (round)

low-high=' 9.9999)' (prefix='(')

.=' ';

run;

　

proc report data=parmest_ nowd out=table;

column numord variable type dependent, (probt value);

define numord /group order=data noprint;

define variable / group order=data ' ';

define type / group order=data noprint;

define dependent / across ' ';

define value /analysis sum;

define probt /analysis sum;

compute value;

array cc _c8_ _c6_ _c4_ ;

if type='stderr' then do;

call define(_col_,'format','stderrf.');

end;

else do;

call define(_col_,'format','8.4');

do i= 1 to 3;

if ~missing(cc(i)) then do;

if 0.05<cc(i) <= 0.1 then call define(_col_, "style", "style=[ posttext='*']" );

else if 0.01 <cc(i) <=0.05 then call define(_col_, "style", "style=[ posttext='**']" );

else if cc(i) <= 0.01 then call define(_col_, "style", "style=[ posttext='***']" );

leave; end;

end;

end;

endcomp;

run;

This is what my output table looks like:

I see two problems:

1. I have two regressions.  I would like to see the output side-by-side (as above) I only see one here.

2. the coefficients and standard error values are actually different than in my table (attached), as if I would changing it unintentionally.

Thanks for your help.

  indavgcostPr > |t| value

Intercept	<.0001	8453.102***
	<.0001	(1.3810)
tag13	0.7400	-127.069
	0.7400	(9.6254)
avgcontract	<.0001	-1224.34***
	<.0001	(8.2317)
tag13*avgcontract	1.5562	46.4737
	1.5562	(2.5511)
ses	0.0530	-42.3237*
	0.0530	(1.8749)
female	0.0064	-780.368***
	0.0064	(6.3947)
age_gr	<.0001	-2138.21***
	<.0001	(4.8519)
age_gr	<.0001	-1909.67***
	<.0001	(4.0391)
age_gr	<.0001	-1802.40***
	<.0001	(2.4117)
age_gr	<.0001	-1830.20***
	<.0001	(0.5354)
age_gr	<.0001	-1448.62***
	<.0001	(3.2364)
age_gr	<.0001	-1609.45***
	<.0001	(1.8672)
age_gr	<.0001	-1557.71***
	<.0001	(4.7518)
age_gr	<.0001	-1618.78***
	<.0001	(1.6792)
age_gr	<.0001	-1112.25***
	<.0001	(4.4247)
age_gr	<.0001	-1069.81***
	<.0001	(3.9204)
age_gr	<.0001	-1245.90***
	<.0001	(0.5962)
age_gr	<.0001	-1336.19***
	<.0001	(7.3560)
age_gr	<.0001	-1608.00***
	<.0001	(6.7212)
age_gr	<.0001	-1644.24***
	<.0001	(7.9121)
age_gr	<.0001	-993.303***
	<.0001	(8.1234)
age_gr	<.0001	-1310.02***
	<.0001	(2.4610)
age_gr	<.0001	-1282.29***
	<.0001	(9.3784)
age_gr	0.5106	210.1890
	0.5106	(9.4449)
age_gr	0.0015	-1615.85***
	0.0015	(0.1211)
female*age_gr	0.0013	1127.503***
	0.0013	(0.1875)
female*age_gr	0.0033	992.5148***
	0.0033	(7.4810)
female*age_gr	0.0353	694.6752**
	0.0353	(9.9497)
female*age_gr	0.0659	623.0732*
	0.0659	(8.8363)
female*age_gr	0.1377	504.2382
	0.1377	(9.6422)
female*age_gr	<.0001	1491.246***
	<.0001	(3.3474)
female*age_gr	<.0001	1339.971***
	<.0001	(8.7947)
female*age_gr	0.0010	1102.112***
	0.0010	(4.9940)
female*age_gr	0.0991	536.0772*
	0.0991	(5.0355)
female*age_gr	0.9996	-0.1699
	0.9996	(4.0705)
female*age_gr	0.0296	700.1651**
	0.0296	(1.9080)
female*age_gr	0.0951	534.5546*
	0.0951	(0.2453)
female*age_gr	0.0134	788.9965**
	0.0134	(9.1323)
female*age_gr	0.1294	491.9362
	0.1294	(4.3554)
female*age_gr	0.0600	624.4005*
	0.0600	(1.9166)
female*age_gr	0.0006	1197.305***
	0.0006	(8.4239)
female*age_gr	0.0184	860.5910**
	0.0184	(5.0921)
female*age_gr	0.0267	-959.616**
	0.0267	(2.9871)
female*age_gr	0.1352	921.4893
	0.1352	(6.853)

[PaigeMiller]

1. Both outputs need to be somehow merged into the same data set and then run PROC REPORT to get both outputs side-by-side.

2. Give us a specific example, so we don't have to scroll through this mass of data and find the discrepancy on our own.

[GKati · Accepted solution]

Thx for your reply. I've actually found some mistakes. i will close this question and reopen another one with new dataset and code. Tx.