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The class statement is available in SUDAAN 9. You use it in the same way that you use the class statement works in SAS: you list categorical variables on this statement so that those variables are not treated as continuous variables by the program. Dummy variables (0/1 variables) do not need to be listed on the class statement. If you include srsex on the class statement, the results will exactly match those obtained using the subgroup and levels statements. In this example, srsex is coded 1 = male and 2 = female, and racehpra is coded 1 = Latino, 2 = Pacific Islander, 2 = AIAN, 4 = Asian, 5 = African American, 6 = White and 7 = Other.
NOTE: The class statement in SUDAAN uses dummy coding (0 1, or what SAS calls reference coding). The class statement in SAS uses effect coding (-1 1). Assuming that you had a two-level categorical variable, in SUDAAN the reference category is coded 0 0, while in SAS it is coded -1 -1. This means that if you run an analysis in both SAS and SUDAAN using the class statement, the coefficients of the dummies of the categorical variable will not match. To get the results to match, use the / param = ref option on the class statement in SAS. You can tell what kind of coding is being used by looking at the top of the output (in either SUDAAN or SAS).
proc regress data=chis filetype=sas design = jackknife; weight rakedw0; jackwgts rakedw1--rakedw80 / adjjack=1; model ab1 = srsex racehpra; class racehpra; run;S U D A A N Software for the Statistical Analysis of Correlated Data Copyright Research Triangle Institute February 2005 Release 9.0.1 Number of observations read : 55428 Weighted count: 23847415 Observations used in the analysis : 55383 Weighted count: 23829382 Denominator degrees of freedom : 80 Maximum number of estimable parameters for the model is 8 Weighted mean response is 2.502603 Multiple R-Square for the dependent variable AB1: 0.043591Frequencies and Values for CLASS Variables by: SRSEX. ----------------------------------- SRSEX Frequency Value ----------------------------------- Ordered Position: 1 23002 1 Ordered Position: 2 32426 2 -----------------------------------Frequencies and Values for CLASS Variables by: RACEHPRA. ------------------------------------------------- RACEHPRA Frequency Value ------------------------------------------------- Ordered Position: 1 9458 1 Ordered Position: 2 219 2 Ordered Position: 3 781 3 Ordered Position: 4 3956 4 Ordered Position: 5 2764 5 Ordered Position: 6 36729 6 Ordered Position: 7 1521 7 -------------------------------------------------Variance Estimation Method: Replicate Weight Jackknife Working Correlations: Independent Link Function: Identity Response variable AB1: AB1 by: Independent Variables and Effects.------------------------------------------------------------------------------------- Independent Variables and Beta Lower 95% Upper 95% Effects Coeff. SE Beta Limit Beta Limit Beta T-Test B=0 ------------------------------------------------------------------------------------- Intercept 2.398056 0.047290 2.303947 2.492165 50.710087 SRSEX 0.078808 0.011561 0.055801 0.101815 6.816660 RACEHPRA LATINO 0.347818 0.041945 0.264346 0.431291 8.292332 PACIFIC ISLANDER 0.000548 0.115201 -0.228710 0.229806 0.004759 AIAN 0.221383 0.071965 0.078169 0.364598 3.076281 ASIAN -0.005809 0.043640 -0.092656 0.081038 -0.133110 AFRICAN AMERICAN 0.103354 0.044022 0.015748 0.190960 2.347804 WHITE -0.184855 0.041252 -0.266949 -0.102761 -4.481135 OTH SINGL/MULTI RACE 0.000000 0.000000 0.000000 0.000000 . -------------------------------------------------------------------------------------------------------------------- Independent P-value Variables and T-Test Effects B=0 ------------------------------- Intercept 0.000000 SRSEX 0.000000 RACEHPRA LATINO 0.000000 PACIFIC ISLANDER 0.996215 AIAN 0.002869 ASIAN 0.894440 AFRICAN AMERICAN 0.021355 WHITE 0.000024 OTH SINGL/MULTI RACE . -------------------------------------------------------------------------------------- Contrast Degrees of P-value Freedom Wald F Wald F ------------------------------------------------------- OVERALL MODEL 8.000000 ********** 0.000000 MODEL MINUS INTERCEPT 7.000000 178.070752 0.000000 INTERCEPT . . . SRSEX 1.000000 46.466848 0.000000 RACEHPRA 6.000000 183.206577 0.000000 -------------------------------------------------------
If you are using an earlier version of SUDAAN or if you want more control over the handling of your categorical variables, you can use the subgroup and levels statements. For each variable listed on the subgroup statement, you need to list the number of levels of categories that each variable has.
By default, the last category (i.e., the highest numbered category) is used as the reference category when you have categorical predictors in a regression model.
proc regress data=chis filetype=sas design = jackknife; weight rakedw0; jackwgts rakedw1--rakedw80 / adjjack=1; model ab1 = srsex racehpra; subgroup srsex racehpra; levels 2 7; run;S U D A A N Software for the Statistical Analysis of Correlated Data Copyright Research Triangle Institute February 2005 Release 9.0.1 Number of observations read : 55428 Weighted count: 23847415 Observations used in the analysis : 55383 Weighted count: 23829382 Denominator degrees of freedom : 80 Maximum number of estimable parameters for the model is 8 Weighted mean response is 2.502603 Multiple R-Square for the dependent variable AB1: 0.043591Variance Estimation Method: Replicate Weight Jackknife Working Correlations: Independent Link Function: Identity Response variable AB1: AB1 by: Independent Variables and Effects. ------------------------------------------------------------------------------------- Independent Variables and Beta Lower 95% Upper 95% Effects Coeff. SE Beta Limit Beta Limit Beta T-Test B=0 ------------------------------------------------------------------------------------- Intercept 2.555672 0.040901 2.474277 2.637067 62.484879 SRSEX MALE -0.078808 0.011561 -0.101815 -0.055801 -6.816660 FEMALE 0.000000 0.000000 0.000000 0.000000 . RACEHPRA LATINO 0.347818 0.041945 0.264346 0.431291 8.292332 PACIFIC ISLANDER 0.000548 0.115201 -0.228710 0.229806 0.004759 AIAN 0.221383 0.071965 0.078169 0.364598 3.076281 ASIAN -0.005809 0.043640 -0.092656 0.081038 -0.133110 AFRICAN AMERICAN 0.103354 0.044022 0.015748 0.190960 2.347804 WHITE -0.184855 0.041252 -0.266949 -0.102761 -4.481135 OTH SINGL/MULTI RACE 0.000000 0.000000 0.000000 0.000000 . -------------------------------------------------------------------------------------------------------------------- Independent P-value Variables and T-Test Effects B=0 ------------------------------- Intercept 0.000000 SRSEX MALE 0.000000 FEMALE . RACEHPRA LATINO 0.000000 PACIFIC ISLANDER 0.996215 AIAN 0.002869 ASIAN 0.894440 AFRICAN AMERICAN 0.021355 WHITE 0.000024 OTH SINGL/MULTI RACE . ------------------------------------------------------------------------------------- Contrast Degrees of P-value Freedom Wald F Wald F ------------------------------------------------------- OVERALL MODEL 8.000000 ********** 0.000000 MODEL MINUS INTERCEPT 7.000000 178.070752 0.000000 INTERCEPT . . . SRSEX 1.000000 46.466848 0.000000 RACEHPRA 6.000000 183.206577 0.000000 -------------------------------------------------------
To change the reference category, you can use the reflevel statement. In this example, we have changed the reference category for both variables. Also, we have used only the first four categories of the race variable, racehpra. If you want to use only some of the categories, you can recode the variable such that the categories that you want to use are the first ones (e.g., coded 1, 2, 3, etc.) and then just give the number of desired categories on the levels statement.
proc regress data=chis filetype=sas design = jackknife;
weight rakedw0;
jackwgts rakedw1--rakedw80 / adjjack=1;
reflevel racehpra = 2 srsex = 1 ;
model ab1 = srsex racehpra;
subgroup srsex racehpra;
levels 2 4;
run;
----------------------------------------------------------------------
Independent P-value
Variables and Beta T-Test
Effects Coeff. SE Beta T-Test B=0 B=0
----------------------------------------------------------------------
Intercept 2.46 0.11 22.88 0.0000
SRSEX
MALE 0.00 0.00 . .
FEMALE 0.11 0.03 4.51 0.0000
RACEHPRA
LATINO 0.35 0.11 3.27 0.0016
PACIFIC ISLANDER 0.00 0.00 . .
AIAN 0.22 0.12 1.91 0.0598
ASIAN -0.01 0.11 -0.06 0.9562
----------------------------------------------------------------------
-------------------------------------------------------
Contrast Degrees
of P-value
Freedom Wald F Wald F
-------------------------------------------------------
OVERALL MODEL 5 12285.21 0.0000
MODEL MINUS
INTERCEPT 4 56.79 0.0000
INTERCEPT . . .
SRSEX 1 20.36 0.0000
RACEHPRA 3 71.68 0.0000
-------------------------------------------------------
proc descript data=chis filetype=sas design = jackknife; weight rakedw0; jackwgts rakedw1--rakedw80 / adjjack=1; var srsex racehpra racehpra racehpra; catlevel 1 1 3 5; setenv colwidth=12; print nsum wsum total setotal; run;----------------------------------------------------- | | | | Variable | | One | | | 1 | ----------------------------------------------------- | | | | | SRSEX: MALE | Sample Size | 55428 | | | Weighted Size | 23847415.32 | | | Total | 11631728.37 | | | SE Total | 515.26 | ----------------------------------------------------- | | | | | RACEHPRA: | Sample Size | 55428 | | LATINO | Weighted Size | 23847415.32 | | | Total | 5643945.79 | | | SE Total | 28469.00 | ----------------------------------------------------- | | | | | RACEHPRA: AIAN | Sample Size | 55428 | | | Weighted Size | 23847415.32 | | | Total | 85146.30 | | | SE Total | 4008.83 | ----------------------------------------------------- | | | | | RACEHPRA: | Sample Size | 55428 | | AFRICAN | Weighted Size | 23847415.32 | | AMERICAN | Total | 1387993.65 | | | SE Total | 11564.25 | -----------------------------------------------------
The numerical values used for the codes of a categorical variable are very important. Values of variables listed on the subgroup statement must be positive or else they are considered as missing. This means that you cannot list a 0/1 dummy variable on the subgroup statement. In most regression analyses, this is not a problem; you just include the variable in the model and not on the subgroup statement (just as you would not include a dummy variable on a class statement in SAS). However, in other procedures, such as proc descript, you may want to include a dummy variable on the subgroup statement. In this case, you would want to recode the variable either using the recode statement or in a SAS data step.
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