R help - Jan 2013 - Survey package help with svystandardize

I am trying to age standardize using the svystandardize package in R. I
have successfully managed to hit my SUDAAN based targets for estimates by
sex, but not the total. The total is only a little different, but I'd like
some help knowing why it isn't exact. I've included the SUDAAN code that
generates the targets and my R script (and output) that I have so far. I
can't supply the data since that would violate usage agreements.

* Sorry if the output isn't formatted nicely.

SUDAAN results:

Variance Estimation Method: Taylor Series (WR)

Standardized estimates

by: Variable, Sex of respondent.



--------------------------------------------------------------------------------

|                         |                                            |
Sex of respondent                       |
| Variable        |
 |-----------------------------------------|
|                         |                                            |
Total                | Male              | Female      |

--------------------------------------------------------------------------------

|                                   |                                  |
                        |                        |
|
| Current Smoker: | Sample Size          |       14732        |        5582
      |        9150        |
| At risk                     | Weighted Size       | 18073876.32 |
 8917534.53 |  9156341.79 |
|                                  | Total                        |
 3477083.28   |  2105598.41 |  1371484.87 |
|                                  | Percent                   |
18.84         |       22.78        |       14.87       |

|                                  | SE Percent              |        0.60
         |        0.95         |        0.73         |

|                                  | Lower 95% Limit  |
     |                         |                         |

|                                  |  Percent                  |
17.69          |       20.97       |       13.50        |

|                                  | Upper 95% Limit  |
      |                        |                         |

|                                  |  Percent                 |       20.05
          |       24.69       |       16.34      |
--------------------------------------------------------------------------------



-------------------------


R Output:

Estimate for the total is off.
> svyciprop(~I(rfsmok=="At risk"), stdes, na.rm=TRUE, ci=TRUE,
vartype="ci")
                                                              2.5%
 97.5%
I(rfsmok == "At risk") 0.187685   0.176309   0.19962


Estimates by sex are a match.
> svyby(~I(rfsmok=="At risk"), ~sex, stdes, svyciprop, na.rm=TRUE,
ci=TRUE,
vartype="ci")
          sex I(rfsmok == "At risk")          ci_l
ci_u
Male     Male           0.2277632725      0.2097154285   0.2468790907
Female Female      0.1486524831     0.1349832066    0.1634444481


-------------------------

R Code:

sample.brfss <- svydesign(id=~brfss.2011$SEQNO, strata=~brfss.2011$ststr,
weights=~brfss.2011$LLCPWT, data=brfss.2011, nest=T)
popage <- c(0.128810, 0.182648, 0.219077, 0.299194, 0.170271)
options("survey.lonely.psu"="adjust")
testing_dataset <- subset(sample.brfss, (!is.na(agegr5) & !is.na(sex)
& !
is.na(rfsmok)))
stdes<-svystandardize(testing_dataset, by=~agegr5, over=~sex,
population=popage)

-------------------------


SUDAAN code:

PROC DESCRIPT DATA = "h:\\brfss_data\\brfss11\\pudf11\\txbrfss_pudf11"
DESIGN = WR FILETYPE= SUDXPORT NOTSORTED;
NEST STSTR SEQNO/MISSUNIT;
WEIGHT LLCPWT;
SETENV LINESIZE = 106 PAGESIZE = 45 COLWIDTH = 11 TOPMGN = 1;
VAR  RFSMOK;
CATLEVEL  2;
STDVAR AGEGR5;
STDWGT .128810  .182648  .219077  .299194  .170271;
SUBGROUP  SEX AGEGR5;
LEVELS  2  5  ;
TABLES  (SEX);

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Fixed with the help of Anthony Damico:

sample.brfss <- update( sample.brfss , one = 1 )
total_dataset <- subset(sample.brfss, (!is.na(agegr5) & !is.na(rfsmok)))
svystandardize(total_dataset, by=~agegr5, over=~one,population=popage)

Apparently SUDAAN doesn't age adjust over the variable sex if it doesn't
need to. The key to solving this problem is to add a variable=1 and adjust
over that variable if you want the total.

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