?? stepwise.sas
字號:
data stepwise;
input hemogl Ca Mg Fe Mn Cu;
cards;
13.50 54.89 30.86 448.70 0.012 1.010
13.00 72.49 42.61 467.30 0.008 1.640
13.75 53.81 52.86 425.61 0.004 1.220
14.00 64.74 39.18 469.80 0.005 1.220
14.25 58.80 37.67 456.55 0.012 1.010
12.75 43.67 26.18 395.78 0.001 0.594
12.50 54.89 30.86 448.70 0.012 1.010
12.25 86.12 43.79 440.13 0.017 1.770
12.00 60.35 38.20 394.40 0.001 1.140
11.75 54.04 34.23 405.60 0.008 1.300
11.50 61.23 37.35 446.00 0.022 1.380
11.25 60.17 33.67 383.20 0.001 0.914
11.00 69.69 40.01 416.70 0.012 1.350
10.75 72.28 40.12 430.80 0.000 1.200
10.50 55.13 33.02 445.80 0.012 0.918
10.25 70.08 36.81 409.80 0.012 1.190
10.00 63.05 35.07 384.10 0.000 0.853
9.75 48.75 30.53 342.90 0.018 0.924
9.50 52.28 27.14 326.29 0.004 0.817
9.25 52.21 36.18 388.54 0.024 1.020
9.00 49.71 25.43 331.10 0.012 0.897
8.75 61.02 29.27 258.94 0.016 1.190
8.50 53.68 28.79 292.80 0.048 1.320
8.25 50.22 29.17 292.60 0.006 1.040
8.00 65.34 29.99 312.80 0.006 1.030
7.80 56.39 29.29 283.00 0.016 1.350
7.50 66.12 31.93 344.20 0.000 0.689
7.25 73.89 32.94 312.50 0.064 1.150
7.00 47.31 28.55 294.70 0.005 0.838
;
proc reg;
title'1. Forward Selection';
model hemogl=ca mg fe mn cu/selection=forward;
run;
title'2. Backward Elimination';
model hemogl=ca mg fe mn cu/selection=backward;
run;
title'3. Stepwise Regression';
model hemogl=ca mg fe mn cu/selection=stepwise;
run;
title'4. Maximum R-square Improvement';
model hemogl=ca mg fe mn cu/selection=maxr;
run;
title'5. Minimum R-square Improvement';
model hemogl=ca mg fe mn cu/selection=minr;
run;
title'6. Rsquare method';
model hemogl=ca mg fe mn cu/selection=rsquare;
run;
title'7.Adjusted R-square method';
model hemogl=ca mg fe mn cu/selection=adjrsq;
run;
title'8. Cp method';
model hemogl=ca mg fe mn cu/selection=cp;
run;
title'9. Multivariate Regression';
model hemogl fe=ca mg mn cu/selection=none;
run;
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