NOTE: If you want to see the design effect or the misspecification effect, use estat effects after the command.
Table 6.2, page 216.
NOTE: You need to increase the amount of memory available to Stata before opening this data file because it is so large.
use nhanes3.dta, clear
svyset SDPPSU6 [pweight = WTPFHX6], strata(SDPSTRA6)
pweight: WTPFHX6
VCE: linearized
Strata 1: SDPSTRA6
SU 1: SDPPSU6
FPC 1: <zero>
svy: logit HBP HSAGEIR HSSEX I.DMARACER BMPWTLBS BMPHTIN I.SMOKE
(running logit on estimation sample)
Survey: Logistic regression
Number of strata = 49 Number of obs = 16963
Number of PSUs = 98 Population size = 1.772e+08
Design df = 49
F( 8, 42) = 193.50
Prob > F = 0.0000
------------------------------------------------------------------------------
| Linearized
HBP | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
HSAGEIR | .0807254 .0024847 32.49 0.000 .0757323 .0857185
HSSEX | .2040417 .0754752 2.70 0.009 .0523686 .3557149
_IDMARACER_2 | .558488 .0743918 7.51 0.000 .4089921 .7079839
_IDMARACER_3 | .0436902 .3004571 0.15 0.885 -.5601009 .6474814
BMPWTLBS | .0116062 .0008349 13.90 0.000 .0099284 .013284
BMPHTIN | -.0592606 .0126097 -4.70 0.000 -.0846008 -.0339204
_ISMOKE_2 | -.0764019 .0949624 -0.80 0.425 -.2672361 .1144323
_ISMOKE_3 | .0610105 .1050502 0.58 0.564 -.1500959 .2721169
_cons | -4.257218 .8040119 -5.29 0.000 -5.87294 -2.641496
------------------------------------------------------------------------------
Table 6.3 , page 218.
svy: logit HBP HSAGEIR HSSEX _IDMARACER_2 _IDMARACER_3 BMPWTLBS BMPHTIN
(running logit on estimation sample)
Survey: Logistic regression
Number of strata = 49 Number of obs = 16964
Number of PSUs = 98 Population size = 1.772e+08
Design df = 49
F( 6, 44) = 205.76
Prob > F = 0.0000
------------------------------------------------------------------------------
| Linearized
HBP | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
HSAGEIR | .0799522 .0026616 30.04 0.000 .0746036 .0853008
HSSEX | .1938372 .0790581 2.45 0.018 .0349641 .3527104
_IDMARACER_2 | .5715161 .0709902 8.05 0.000 .4288559 .7141762
_IDMARACER_3 | .0519777 .3006959 0.17 0.863 -.5522933 .6562486
BMPWTLBS | .0114421 .0008405 13.61 0.000 .0097531 .0131311
BMPHTIN | -.0589891 .0126859 -4.65 0.000 -.0844824 -.0334958
_cons | -4.211455 .7940002 -5.30 0.000 -5.807058 -2.615852
------------------------------------------------------------------------------
Table 6.43, page 219.
logit HBP HSAGEIR HSSEX _IDMARACER_2 _IDMARACER_3 BMPWTLBS BMPHTIN
Iteration 0: log likelihood = -8602.8989
Iteration 1: log likelihood = -6870.2255
Iteration 2: log likelihood = -6671.2868
Iteration 3: log likelihood = -6663.7359
Iteration 4: log likelihood = -6663.7081
Logit estimates Number of obs = 16964
LR chi2(6) = 3878.38
Prob > chi2 = 0.0000
Log likelihood = -6663.7081 Pseudo R2 = 0.2254
------------------------------------------------------------------------------
HBP | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
HSAGEIR | .0696379 .0013966 49.86 0.000 .0669007 .0723751
HSSEX | .0904745 .0613365 1.48 0.140 -.0297429 .2106919
_IDMARACER_2 | .4765584 .0509377 9.36 0.000 .3767225 .5763944
_IDMARACER_3 | .0916109 .1430926 0.64 0.522 -.1888453 .3720672
BMPWTLBS | .0083715 .0006091 13.74 0.000 .0071776 .0095654
BMPHTIN | -.0451575 .0085049 -5.31 0.000 -.0618268 -.0284882
_cons | -3.871509 .529282 -7.31 0.000 -4.908883 -2.834135
------------------------------------------------------------------------------
Table 6.5, page 221.
Design-based analysis:
svy: logit HBP HSAGEIR HSSEX _IDMARACER_2 _IDMARACER_3 BMPWTLBS BMPHTIN, or
(running logit on estimation sample)
Survey: Logistic regression
Number of strata = 49 Number of obs = 16964
Number of PSUs = 98 Population size = 1.772e+08
Design df = 49
F( 6, 44) = 205.76
Prob > F = 0.0000
------------------------------------------------------------------------------
| Linearized
HBP | Odds Ratio Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
HSAGEIR | 1.083235 .0028831 30.04 0.000 1.077457 1.089045
HSSEX | 1.213899 .0959685 2.45 0.018 1.035583 1.422919
_IDMARACER_2 | 1.77095 .1257201 8.05 0.000 1.5355 2.042503
_IDMARACER_3 | 1.053352 .3167386 0.17 0.863 .5756282 1.927548
BMPWTLBS | 1.011508 .0008502 13.61 0.000 1.009801 1.013218
BMPHTIN | .942717 .0119592 -4.65 0.000 .9189878 .9670589
------------------------------------------------------------------------------
Model-based analysis:
logit HBP HSAGEIR HSSEX _IDMARACER_2 _IDMARACER_3 BMPWTLBS BMPHTIN, or
Iteration 0: log likelihood = -8602.8989
Iteration 1: log likelihood = -6870.2255
Iteration 2: log likelihood = -6671.2868
Iteration 3: log likelihood = -6663.7359
Iteration 4: log likelihood = -6663.7081
Logit estimates Number of obs = 16964
LR chi2(6) = 3878.38
Prob > chi2 = 0.0000
Log likelihood = -6663.7081 Pseudo R2 = 0.2254
------------------------------------------------------------------------------
HBP | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
HSAGEIR | 1.07212 .0014973 49.86 0.000 1.069189 1.075059
HSSEX | 1.094694 .0671447 1.48 0.140 .9706951 1.234532
_IDMARACER_2 | 1.610522 .0820362 9.36 0.000 1.4575 1.77961
_IDMARACER_3 | 1.095938 .1568206 0.64 0.522 .8279146 1.45073
BMPWTLBS | 1.008407 .0006142 13.74 0.000 1.007203 1.009611
BMPHTIN | .9558469 .0081294 -5.31 0.000 .9400457 .9719138
------------------------------------------------------------------------------