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