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SUDAAN FAQ
How can I limit the number of observations used by SUDAAN?

If you are working with a very large data set and you find that running procedures takes a while, you can use the maxobs = option on the proc statement of all analysis procedures to limit the number of observations that are read in.  This can be very useful when you are debugging a program.  Just remember to delete that option when you have the programming working correctly.  Compare the results of the two proc reg calls below.
proc regress data=temp1 filetype=sas design = jackknife maxobs = 1000;
weight rakedw0;
jackwgts rakedw1--rakedw80 / adjjack=1;
model ae13 = ae14;
run;
Number of observations read       :   1000    Weighted count:   431947
Observations used in the analysis :    591    Weighted count:   242364
Denominator degrees of freedom    :     80

Maximum number of estimable parameters for the model is  2
Weighted mean response is 2.262239

Multiple R-Square for the dependent variable AE13: 0.216196
Variance Estimation Method: Replicate Weight Jackknife
Working Correlations: Independent
Link Function: Identity
Response variable AE13: AE13

----------------------------------------------------------------------
Independent                                                   P-value
  Variables and        Beta                                   T-Test
  Effects              Coeff.          SE Beta   T-Test B=0   B=0
----------------------------------------------------------------------
Intercept                    1.96         0.11        17.83     0.0000
AE14                         0.32         0.08         3.78     0.0003
----------------------------------------------------------------------

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

Contrast               Degrees
                       of                      P-value
                       Freedom        Wald F   Wald F
-------------------------------------------------------
OVERALL MODEL                 2       197.90     0.0000
MODEL MINUS
  INTERCEPT                   1        14.29     0.0003
INTERCEPT                     1       317.85     0.0000
AE14                          1        14.29     0.0003
-------------------------------------------------------
proc regress data=temp1 filetype=sas design = jackknife;
weight rakedw0;
jackwgts rakedw1--rakedw80 / adjjack=1;
model ae13 = ae14;
run;
Number of observations read       :  55428    Weighted count: 23847415
Observations used in the analysis :  32538    Weighted count: 13783845
Denominator degrees of freedom    :     80

Maximum number of estimable parameters for the model is  2
Weighted mean response is 2.188590

Multiple R-Square for the dependent variable AE13: 0.241897
Variance Estimation Method: Replicate Weight Jackknife
Working Correlations: Independent
Link Function: Identity
Response variable AE13: AE13

----------------------------------------------------------------------
Independent                                                   P-value
  Variables and        Beta                                   T-Test
  Effects              Coeff.          SE Beta   T-Test B=0   B=0
----------------------------------------------------------------------
Intercept                    1.88         0.01       152.15     0.0000
AE14                         0.34         0.01        25.47     0.0000
----------------------------------------------------------------------
-------------------------------------------------------

Contrast               Degrees
                       of                      P-value
                       Freedom        Wald F   Wald F
-------------------------------------------------------
OVERALL MODEL                 2     12818.28     0.0000
MODEL MINUS
  INTERCEPT                   1       648.71     0.0000
INTERCEPT                     1     23150.59     0.0000
AE14                          1       648.71     0.0000
-------------------------------------------------------

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