. * do-file for lecture 2a of VHM 812/802, Winter 2025 . version 18 /* works mostly with versions 14-17, as indicated below */ . set more off . set scheme stcolor_alt . cd "r:\" r:\ . . use daisy2red, clear . * recoding a continuous predictor (VER 14.4) . regress milk120 parity Source | SS df MS Number of obs = 1,536 -------------+---------------------------------- F(1, 1534) = 262.27 Model | 109234227 1 109234227 Prob > F = 0.0000 Residual | 638905966 1,534 416496.718 R-squared = 0.1460 -------------+---------------------------------- Adj R-squared = 0.1455 Total | 748140192 1,535 487387.748 Root MSE = 645.37 ------------------------------------------------------------------------------ milk120 | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- parity | 178.347 11.01266 16.19 0.000 156.7455 199.9484 _cons | 2727.08 34.33991 79.41 0.000 2659.722 2794.438 ------------------------------------------------------------------------------ . generate parity1=parity-1 . regress milk120 parity1 // intercept now for parity=1 Source | SS df MS Number of obs = 1,536 -------------+---------------------------------- F(1, 1534) = 262.27 Model | 109234227 1 109234227 Prob > F = 0.0000 Residual | 638905966 1,534 416496.718 R-squared = 0.1460 -------------+---------------------------------- Adj R-squared = 0.1455 Total | 748140192 1,535 487387.748 Root MSE = 645.37 ------------------------------------------------------------------------------ milk120 | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- parity1 | 178.347 11.01266 16.19 0.000 156.7455 199.9484 _cons | 2905.427 25.23474 115.14 0.000 2855.928 2954.925 ------------------------------------------------------------------------------ . . * switch now to wpc outcome . summarize wpc, d Interval from wait period to conception ------------------------------------------------------------- Percentiles Smallest 1% 11 1 5% 15 1 10% 20 3 Obs 1,574 25% 30 4 Sum of wgt. 1,574 50% 53 Mean 68.79924 Largest Std. dev. 51.59928 75% 94 254 90% 141 255 Variance 2662.486 95% 178 284 Skewness 1.356612 99% 232 298 Kurtosis 4.551503 . * scaling of continuous predictor . regress wpc herd_size Source | SS df MS Number of obs = 1,574 -------------+---------------------------------- F(1, 1572) = 70.66 Model | 180151.608 1 180151.608 Prob > F = 0.0000 Residual | 4007938.95 1,572 2549.57949 R-squared = 0.0430 -------------+---------------------------------- Adj R-squared = 0.0424 Total | 4188090.56 1,573 2662.48605 Root MSE = 50.493 ------------------------------------------------------------------------------ wpc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- herd_size | .1725618 .0205286 8.41 0.000 .1322954 .2128281 _cons | 25.48492 5.30769 4.80 0.000 15.07403 35.89582 ------------------------------------------------------------------------------ . generate hs100=herd_size/100 . regress wpc hs100 // slope corresponds to change in herd size of 100 Source | SS df MS Number of obs = 1,574 -------------+---------------------------------- F(1, 1572) = 70.66 Model | 180151.593 1 180151.593 Prob > F = 0.0000 Residual | 4007938.97 1,572 2549.5795 R-squared = 0.0430 -------------+---------------------------------- Adj R-squared = 0.0424 Total | 4188090.56 1,573 2662.48605 Root MSE = 50.493 ------------------------------------------------------------------------------ wpc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- hs100 | 17.25617 2.052862 8.41 0.000 13.22954 21.28281 _cons | 25.48492 5.30769 4.80 0.000 15.07403 35.89582 ------------------------------------------------------------------------------ . gen hs100_sq=hs100^2 . regress wpc hs100 hs100_sq // VER 14.8 Source | SS df MS Number of obs = 1,574 -------------+---------------------------------- F(2, 1571) = 40.45 Model | 205120.743 2 102560.371 Prob > F = 0.0000 Residual | 3982969.82 1,571 2535.3086 R-squared = 0.0490 -------------+---------------------------------- Adj R-squared = 0.0478 Total | 4188090.56 1,573 2662.48605 Root MSE = 50.352 ------------------------------------------------------------------------------ wpc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- hs100 | -29.51616 15.04392 -1.96 0.050 -59.02444 -.0078794 hs100_sq | 9.744227 3.104995 3.14 0.002 3.653856 15.8346 _cons | 77.74837 17.47457 4.45 0.000 43.47243 112.0243 ------------------------------------------------------------------------------ . regress wpc c.hs100##c.hs100 // same with Stata factor notation, see 2aL-14 Source | SS df MS Number of obs = 1,574 -------------+---------------------------------- F(2, 1571) = 40.45 Model | 205120.743 2 102560.372 Prob > F = 0.0000 Residual | 3982969.82 1,571 2535.3086 R-squared = 0.0490 -------------+---------------------------------- Adj R-squared = 0.0478 Total | 4188090.56 1,573 2662.48605 Root MSE = 50.352 --------------------------------------------------------------------------------- wpc | Coefficient Std. err. t P>|t| [95% conf. interval] ----------------+---------------------------------------------------------------- hs100 | -29.51616 15.04392 -1.96 0.050 -59.02444 -.0078797 | c.hs100#c.hs100 | 9.744227 3.104995 3.14 0.002 3.653856 15.8346 | _cons | 77.74837 17.47457 4.45 0.000 43.47243 112.0243 --------------------------------------------------------------------------------- . * no exploration of centering hs100 because not really needed . . * parametrizing categorical predictors, back to milk120 again... . regress milk120 i.parity // VER 14.7 Source | SS df MS Number of obs = 1,536 -------------+---------------------------------- F(6, 1529) = 83.50 Model | 184637128 6 30772854.7 Prob > F = 0.0000 Residual | 563503064 1,529 368543.534 R-squared = 0.2468 -------------+---------------------------------- Adj R-squared = 0.2438 Total | 748140192 1,535 487387.748 Root MSE = 607.08 ------------------------------------------------------------------------------ milk120 | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- parity | 2 | 708.2134 43.74086 16.19 0.000 622.4149 794.0118 3 | 789.8435 45.94638 17.19 0.000 699.7189 879.9681 4 | 848.5137 50.96418 16.65 0.000 748.5467 948.4808 5 | 787.6091 56.22915 14.01 0.000 677.3147 897.9035 6 | 878.1606 79.09305 11.10 0.000 723.0183 1033.303 7 | 925.9548 305.0416 3.04 0.002 327.6106 1524.299 | _cons | 2639.645 30.2407 87.29 0.000 2580.328 2698.963 ------------------------------------------------------------------------------ . tabstat milk120, by(parity) Summary for variables: milk120 Group variable: parity (Lactation number) parity | Mean ---------+---------- 1 | 2639.645 2 | 3347.859 3 | 3429.489 4 | 3488.159 5 | 3427.254 6 | 3517.806 7 | 3565.6 ---------+---------- Total | 3215.096 -------------------- . testparm i.parity // overall test for parity ( 1) 2.parity = 0 ( 2) 3.parity = 0 ( 3) 4.parity = 0 ( 4) 5.parity = 0 ( 5) 6.parity = 0 ( 6) 7.parity = 0 F( 6, 1529) = 83.50 Prob > F = 0.0000 . regress milk120 ib7.parity // parametrized with last category (7) as reference Source | SS df MS Number of obs = 1,536 -------------+---------------------------------- F(6, 1529) = 83.50 Model | 184637128 6 30772854.7 Prob > F = 0.0000 Residual | 563503064 1,529 368543.534 R-squared = 0.2468 -------------+---------------------------------- Adj R-squared = 0.2438 Total | 748140192 1,535 487387.748 Root MSE = 607.08 ------------------------------------------------------------------------------ milk120 | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- parity | 1 | -925.9548 305.0416 -3.04 0.002 -1524.299 -327.6106 2 | -217.7414 305.1797 -0.71 0.476 -816.3565 380.8736 3 | -136.1113 305.5036 -0.45 0.656 -735.3618 463.1391 4 | -77.44108 306.2984 -0.25 0.800 -678.2506 523.3684 5 | -138.3457 307.2183 -0.45 0.653 -740.9596 464.2682 6 | -47.79418 312.2132 -0.15 0.878 -660.2056 564.6173 | _cons | 3565.6 303.5389 11.75 0.000 2970.203 4160.997 ------------------------------------------------------------------------------ . testparm i.parity // the same, of course ( 1) 1.parity = 0 ( 2) 2.parity = 0 ( 3) 3.parity = 0 ( 4) 4.parity = 0 ( 5) 5.parity = 0 ( 6) 6.parity = 0 F( 6, 1529) = 83.50 Prob > F = 0.0000 . . * exploring additivity and interaction . * two categorical predictors (VER 14.9) . regress wpc i.rp i.vag_disch // additive model Source | SS df MS Number of obs = 1,574 -------------+---------------------------------- F(2, 1571) = 4.65 Model | 24663.1482 2 12331.5741 Prob > F = 0.0097 Residual | 4163427.41 1,571 2650.17658 R-squared = 0.0059 -------------+---------------------------------- Adj R-squared = 0.0046 Total | 4188090.56 1,573 2662.48605 Root MSE = 51.48 ------------------------------------------------------------------------------ wpc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- rp | yes | 10.2397 4.540774 2.26 0.024 1.333087 19.14632 | vag_disch | yes | 9.066942 5.9819 1.52 0.130 -2.666406 20.80029 _cons | 67.35756 1.381095 48.77 0.000 64.64857 70.06654 ------------------------------------------------------------------------------ . regress wpc i.rp i.vag_disch i.rp#i.vag_disch // interaction added Source | SS df MS Number of obs = 1,574 -------------+---------------------------------- F(3, 1570) = 4.53 Model | 35915.9774 3 11971.9925 Prob > F = 0.0036 Residual | 4152174.58 1,570 2644.69719 R-squared = 0.0086 -------------+---------------------------------- Adj R-squared = 0.0067 Total | 4188090.56 1,573 2662.48605 Root MSE = 51.427 ------------------------------------------------------------------------------ wpc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- rp | yes | 6.339794 4.914322 1.29 0.197 -3.299531 15.97912 | vag_disch | yes | .5429296 7.265382 0.07 0.940 -13.70794 14.7938 | rp#vag_disch | yes#yes | 26.34867 12.77367 2.06 0.039 1.293414 51.40392 | _cons | 67.66861 1.387883 48.76 0.000 64.94631 70.39091 ------------------------------------------------------------------------------ . table vag_disch rp, statistic(mean wpc) nototal ------------------------------------------------------------ | Retained placenta at calving | no yes ---------------------------+-------------------------------- Vaginal discharge observed | no | 67.66861 74.0084 yes | 68.21154 100.9 ------------------------------------------------------------ . * table vag_disch rp, c(mean wpc) // version 16 or before . testparm i.rp#i.vag_disch // same as t-test in coefficient table ( 1) 1.rp#1.vag_disch = 0 F( 1, 1570) = 4.25 Prob > F = 0.0393 . regress wpc i.rp##i.vag_disch // same with less typing Source | SS df MS Number of obs = 1,574 -------------+---------------------------------- F(3, 1570) = 4.53 Model | 35915.9774 3 11971.9925 Prob > F = 0.0036 Residual | 4152174.58 1,570 2644.69719 R-squared = 0.0086 -------------+---------------------------------- Adj R-squared = 0.0067 Total | 4188090.56 1,573 2662.48605 Root MSE = 51.427 ------------------------------------------------------------------------------ wpc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- rp | yes | 6.339794 4.914322 1.29 0.197 -3.299531 15.97912 | vag_disch | yes | .5429296 7.265382 0.07 0.940 -13.70794 14.7938 | rp#vag_disch | yes#yes | 26.34867 12.77367 2.06 0.039 1.293414 51.40392 | _cons | 67.66861 1.387883 48.76 0.000 64.94631 70.39091 ------------------------------------------------------------------------------ . regress wpc rp##vag_disch // i. factor notation may be omitted, but maybe keep it Source | SS df MS Number of obs = 1,574 -------------+---------------------------------- F(3, 1570) = 4.53 Model | 35915.9774 3 11971.9925 Prob > F = 0.0036 Residual | 4152174.58 1,570 2644.69719 R-squared = 0.0086 -------------+---------------------------------- Adj R-squared = 0.0067 Total | 4188090.56 1,573 2662.48605 Root MSE = 51.427 ------------------------------------------------------------------------------ wpc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- rp | yes | 6.339794 4.914322 1.29 0.197 -3.299531 15.97912 | vag_disch | yes | .5429296 7.265382 0.07 0.940 -13.70794 14.7938 | rp#vag_disch | yes#yes | 26.34867 12.77367 2.06 0.039 1.293414 51.40392 | _cons | 67.66861 1.387883 48.76 0.000 64.94631 70.39091 ------------------------------------------------------------------------------ . margins i.rp#i.vag_disch Adjusted predictions Number of obs = 1,574 Model VCE: OLS Expression: Linear prediction, predict() ------------------------------------------------------------------------------ | Delta-method | Margin std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- rp#vag_disch | no#no | 67.66861 1.387883 48.76 0.000 64.94631 70.39091 no#yes | 68.21154 7.131589 9.56 0.000 54.2231 82.19998 yes#no | 74.0084 4.71427 15.70 0.000 64.76147 83.25533 yes#yes | 100.9 9.389173 10.75 0.000 82.48336 119.3166 ------------------------------------------------------------------------------ . marginsplot, noci /* interaction plot in Stata, more details in Lecture 3a */ Variables that uniquely identify margins: rp vag_disch . . * one continuous, one categorical predictor (VER 14.10) . regress wpc i.dyst milk120 Source | SS df MS Number of obs = 1,536 -------------+---------------------------------- F(2, 1533) = 1.01 Model | 5380.7883 2 2690.39415 Prob > F = 0.3658 Residual | 4098983.87 1,533 2673.83162 R-squared = 0.0013 -------------+---------------------------------- Adj R-squared = 0.0000 Total | 4104364.66 1,535 2673.8532 Root MSE = 51.709 ------------------------------------------------------------------------------ wpc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- dyst | yes | 4.004176 5.568429 0.72 0.472 -6.918368 14.92672 milk120 | -.0022375 .0018933 -1.18 0.237 -.0059512 .0014763 _cons | 75.93882 6.255198 12.14 0.000 63.66917 88.20847 ------------------------------------------------------------------------------ . generate milk120k= milk120/1000 // rescale predictor (38 missing values generated) . regress wpc i.dyst milk120k // additive model Source | SS df MS Number of obs = 1,536 -------------+---------------------------------- F(2, 1533) = 1.01 Model | 5380.78796 2 2690.39398 Prob > F = 0.3658 Residual | 4098983.87 1,533 2673.83162 R-squared = 0.0013 -------------+---------------------------------- Adj R-squared = 0.0000 Total | 4104364.66 1,535 2673.8532 Root MSE = 51.709 ------------------------------------------------------------------------------ wpc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- dyst | yes | 4.004176 5.568429 0.72 0.472 -6.918368 14.92672 milk120k | -2.237454 1.893316 -1.18 0.237 -5.951217 1.476309 _cons | 75.93882 6.255198 12.14 0.000 63.66917 88.20847 ------------------------------------------------------------------------------ . regress wpc i.dyst milk120k i.dyst#c.milk120k // interaction added, see 2aL for factor notation Source | SS df MS Number of obs = 1,536 -------------+---------------------------------- F(3, 1532) = 3.83 Model | 30572.875 3 10190.9583 Prob > F = 0.0095 Residual | 4073791.78 1,532 2659.13302 R-squared = 0.0074 -------------+---------------------------------- Adj R-squared = 0.0055 Total | 4104364.66 1,535 2673.8532 Root MSE = 51.567 --------------------------------------------------------------------------------- wpc | Coefficient Std. err. t P>|t| [95% conf. interval] ----------------+---------------------------------------------------------------- dyst | yes | -85.48838 29.60089 -2.89 0.004 -143.5509 -27.42583 milk120k | -3.446531 1.928535 -1.79 0.074 -7.229379 .3363161 | dyst#c.milk120k | yes | 29.14238 9.468101 3.08 0.002 10.57056 47.71419 | _cons | 79.83774 6.365297 12.54 0.000 67.35213 92.32336 --------------------------------------------------------------------------------- . regress wpc i.dyst##c.milk120k Source | SS df MS Number of obs = 1,536 -------------+---------------------------------- F(3, 1532) = 3.83 Model | 30572.875 3 10190.9583 Prob > F = 0.0095 Residual | 4073791.78 1,532 2659.13302 R-squared = 0.0074 -------------+---------------------------------- Adj R-squared = 0.0055 Total | 4104364.66 1,535 2673.8532 Root MSE = 51.567 --------------------------------------------------------------------------------- wpc | Coefficient Std. err. t P>|t| [95% conf. interval] ----------------+---------------------------------------------------------------- dyst | yes | -85.48838 29.60089 -2.89 0.004 -143.5509 -27.42583 milk120k | -3.446531 1.928535 -1.79 0.074 -7.229379 .3363161 | dyst#c.milk120k | yes | 29.14238 9.468101 3.08 0.002 10.57056 47.71419 | _cons | 79.83774 6.365297 12.54 0.000 67.35213 92.32336 --------------------------------------------------------------------------------- . margins i.dyst, at(milk120k=(1(1)6)) Adjusted predictions Number of obs = 1,536 Model VCE: OLS Expression: Linear prediction, predict() 1._at: milk120k = 1 2._at: milk120k = 2 3._at: milk120k = 3 4._at: milk120k = 4 5._at: milk120k = 5 6._at: milk120k = 6 ------------------------------------------------------------------------------ | Delta-method | Margin std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- _at#dyst | 1#no | 76.39121 4.499919 16.98 0.000 67.56456 85.21787 1#yes | 20.04521 19.8754 1.01 0.313 -18.94067 59.03108 2#no | 72.94468 2.723976 26.78 0.000 67.60156 78.2878 2#yes | 45.74105 11.23473 4.07 0.000 23.70398 67.77812 3#no | 69.49815 1.424536 48.79 0.000 66.7039 72.2924 3#yes | 71.43689 5.409061 13.21 0.000 60.82695 82.04684 4#no | 66.05162 2.019173 32.71 0.000 62.09098 70.01225 4#yes | 97.13274 10.2053 9.52 0.000 77.1149 117.1506 5#no | 62.60509 3.682839 17.00 0.000 55.38115 69.82903 5#yes | 122.8286 18.73205 6.56 0.000 86.0854 159.5718 6#no | 59.15855 5.521598 10.71 0.000 48.32786 69.98925 6#yes | 148.5244 27.73955 5.35 0.000 94.11291 202.9359 ------------------------------------------------------------------------------ . marginsplot, noci /* interaction plot in Stata, more details in Lecture 3a */ Variables that uniquely identify margins: milk120k dyst . . * two continuous predictors (VER 14.11) . regress wpc c.parity##c.milk120k Source | SS df MS Number of obs = 1,536 -------------+---------------------------------- F(3, 1532) = 2.26 Model | 18084.1905 3 6028.06351 Prob > F = 0.0797 Residual | 4086280.47 1,532 2667.2849 R-squared = 0.0044 -------------+---------------------------------- Adj R-squared = 0.0025 Total | 4104364.66 1,535 2673.8532 Root MSE = 51.646 ------------------------------------------------------------------------------------- wpc | Coefficient Std. err. t P>|t| [95% conf. interval] --------------------+---------------------------------------------------------------- parity | 4.889989 4.437817 1.10 0.271 -3.814849 13.59483 milk120k | -1.666398 4.212531 -0.40 0.692 -9.929335 6.596539 | c.parity#c.milk120k | -.8764359 1.363504 -0.64 0.520 -3.550968 1.798096 | _cons | 69.02201 12.77659 5.40 0.000 43.96056 94.08347 ------------------------------------------------------------------------------------- . * interaction plot in Lecture 3a . . * illustration of confounding (not a VER example) . regress wpc i.vag_disch // crude model: vag_disch adds ~12 days, P=0.04 Source | SS df MS Number of obs = 1,574 -------------+---------------------------------- F(1, 1572) = 4.21 Model | 11186.2576 1 11186.2576 Prob > F = 0.0404 Residual | 4176904.3 1,572 2657.0638 R-squared = 0.0027 -------------+---------------------------------- Adj R-squared = 0.0020 Total | 4188090.56 1,573 2662.48605 Root MSE = 51.547 ------------------------------------------------------------------------------ wpc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- vag_disch | yes | 11.99647 5.846716 2.05 0.040 .5282858 23.46465 _cons | 68.17426 1.334494 51.09 0.000 65.55669 70.79184 ------------------------------------------------------------------------------ . regress wpc i.herd if vag_disch==0 // confounder and outcome in unexposed individuals Source | SS df MS Number of obs = 1,492 -------------+---------------------------------- F(6, 1485) = 14.92 Model | 223706.812 6 37284.4686 Prob > F = 0.0000 Residual | 3711781.88 1,485 2499.51642 R-squared = 0.0568 -------------+---------------------------------- Adj R-squared = 0.0530 Total | 3935488.69 1,491 2639.4961 Root MSE = 49.995 ------------------------------------------------------------------------------ wpc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- herd | 2 | -7.455598 4.572715 -1.63 0.103 -16.42527 1.51407 3 | 12.30396 4.184586 2.94 0.003 4.095636 20.51229 4 | -20.0733 4.70619 -4.27 0.000 -29.30479 -10.84181 5 | -21.78125 5.489853 -3.97 0.000 -32.54994 -11.01256 106 | -15.40129 4.618509 -3.33 0.001 -24.46079 -6.341796 119 | -17.26021 5.05679 -3.41 0.001 -27.17942 -7.341 | _cons | 75.1583 3.106548 24.19 0.000 69.06461 81.25199 ------------------------------------------------------------------------------ . testparm i.herd // significant herd effects ( 1) 2.herd = 0 ( 2) 3.herd = 0 ( 3) 4.herd = 0 ( 4) 5.herd = 0 ( 5) 106.herd = 0 ( 6) 119.herd = 0 F( 6, 1485) = 14.92 Prob > F = 0.0000 . tabulate herd vag_disch, chi2 row // vag_disch varies across significantly herds +----------------+ | Key | |----------------| | frequency | | row percentage | +----------------+ | Vaginal discharge Herd | observed Number | no yes | Total -----------+----------------------+---------- 1 | 259 13 | 272 | 95.22 4.78 | 100.00 -----------+----------------------+---------- 2 | 222 5 | 227 | 97.80 2.20 | 100.00 -----------+----------------------+---------- 3 | 318 4 | 322 | 98.76 1.24 | 100.00 -----------+----------------------+---------- 4 | 200 3 | 203 | 98.52 1.48 | 100.00 -----------+----------------------+---------- 5 | 122 7 | 129 | 94.57 5.43 | 100.00 -----------+----------------------+---------- 106 | 214 39 | 253 | 84.58 15.42 | 100.00 -----------+----------------------+---------- 119 | 157 11 | 168 | 93.45 6.55 | 100.00 -----------+----------------------+---------- Total | 1,492 82 | 1,574 | 94.79 5.21 | 100.00 Pearson chi2(6) = 74.2267 Pr = 0.000 . regress wpc i.vag_disch i.herd // adjusted model Source | SS df MS Number of obs = 1,574 -------------+---------------------------------- F(7, 1566) = 14.35 Model | 252509.59 7 36072.7985 Prob > F = 0.0000 Residual | 3935580.97 1,566 2513.14238 R-squared = 0.0603 -------------+---------------------------------- Adj R-squared = 0.0561 Total | 4188090.56 1,573 2662.48605 Root MSE = 50.131 ------------------------------------------------------------------------------ wpc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- vag_disch | yes | 17.81936 5.825177 3.06 0.002 6.393393 29.24533 | herd | 2 | -8.178615 4.509228 -1.81 0.070 -17.02338 .6661455 3 | 11.71303 4.133611 2.83 0.005 3.605039 19.82103 4 | -20.74627 4.653654 -4.46 0.000 -29.87432 -11.61823 5 | -22.15954 5.359351 -4.13 0.000 -32.6718 -11.64728 106 | -18.18881 4.422295 -4.11 0.000 -26.86305 -9.514562 119 | -17.85657 4.920293 -3.63 0.000 -27.50763 -8.205518 | _cons | 75.97555 3.052377 24.89 0.000 69.98837 81.96272 ------------------------------------------------------------------------------ . display "relative change =" abs(12.00-17.82)/12.00 relative change =.485 . . * VER 14.12 multivariable causal model . use daisy2red, clear . generate calv_mth=month(calv_dt) . tabulate calv_mth, summ(wpc) | Summary of Interval from wait | period to conception calv_mth | Mean Std. dev. Freq. ------------+------------------------------------ 1 | 77.00885 50.850533 113 2 | 60.009615 45.765017 104 3 | 69.402516 54.240687 159 4 | 60.385827 53.046258 127 5 | 63 49.882911 106 6 | 64.350365 51.665154 137 7 | 63.581818 45.047453 110 8 | 69.514019 54.277478 107 9 | 74.664773 52.827873 176 10 | 72.477011 49.332856 174 11 | 72.026846 54.231479 149 12 | 73.508929 53.222801 112 ------------+------------------------------------ Total | 68.799238 51.599283 1,574 . generate aut_calv=(calv_mth>=2 & calv_mth<=7) if !missing(calv_mth) . tabulate aut_calv, summ(wpc) | Summary of Interval from wait | period to conception aut_calv | Mean Std. dev. Freq. ------------+------------------------------------ 0 | 73.233454 52.236588 831 1 | 63.839838 50.451973 743 ------------+------------------------------------ Total | 68.799238 51.599283 1,574 . generate hs100=herd_size/100 . generate parity1=parity-1 . regress wpc c.hs100##c.hs100 parity1 i.aut_calv i.twin i.dyst i.rp##i.vag_disch Source | SS df MS Number of obs = 1,574 -------------+---------------------------------- F(9, 1564) = 13.22 Model | 296062.681 9 32895.8535 Prob > F = 0.0000 Residual | 3892027.88 1,564 2488.50887 R-squared = 0.0707 -------------+---------------------------------- Adj R-squared = 0.0653 Total | 4188090.56 1,573 2662.48605 Root MSE = 49.885 --------------------------------------------------------------------------------- wpc | Coefficient Std. err. t P>|t| [95% conf. interval] ----------------+---------------------------------------------------------------- hs100 | -36.05705 15.05032 -2.40 0.017 -65.57798 -6.53612 | c.hs100#c.hs100 | 11.13827 3.111145 3.58 0.000 5.035818 17.24073 | parity1 | 1.13721 .8583103 1.32 0.185 -.54635 2.82077 1.aut_calv | -8.263839 2.537751 -3.26 0.001 -13.24159 -3.286086 | twin | yes | 20.68314 9.845165 2.10 0.036 1.37203 39.99425 | dyst | yes | 11.70041 5.462576 2.14 0.032 .9856659 22.41516 | rp | yes | 5.98687 4.811976 1.24 0.214 -3.451734 15.42547 | vag_disch | yes | 1.228195 7.161395 0.17 0.864 -12.81875 15.27514 | rp#vag_disch | yes#yes | 22.85194 12.51605 1.83 0.068 -1.698056 47.40194 | _cons | 84.66125 17.61671 4.81 0.000 50.10639 119.2161 --------------------------------------------------------------------------------- . * same results as in VER results, except for intercept . * to center herdsize at 251 is pretty meaningless... . summarize herd_size, d // this is not the actual distribution of herd_size! Herd size ------------------------------------------------------------- Percentiles Smallest 1% 125 125 5% 125 125 10% 185 125 Obs 1,574 25% 201 125 Sum of wgt. 1,574 50% 263 Mean 251.0076 Largest Std. dev. 62.01692 75% 294 333 90% 333 333 Variance 3846.098 95% 333 333 Skewness -.3550929 99% 333 333 Kurtosis 2.256969 . tabulate herd_size Herd size | Freq. Percent Cum. ------------+----------------------------------- 125 | 129 8.20 8.20 185 | 168 10.67 18.87 201 | 203 12.90 31.77 235 | 227 14.42 46.19 263 | 253 16.07 62.26 294 | 272 17.28 79.54 333 | 322 20.46 100.00 ------------+----------------------------------- Total | 1,574 100.00 . bysort herd: gen within=_n // generates counter 1,2,... of cows in each herd . summarize herd_size if within==1,d Herd size ------------------------------------------------------------- Percentiles Smallest 1% 125 125 5% 125 185 10% 125 201 Obs 7 25% 185 235 Sum of wgt. 7 50% 235 Mean 233.7143 Largest Std. dev. 70.25362 75% 294 235 90% 333 263 Variance 4935.571 95% 333 294 Skewness -.1103475 99% 333 333 Kurtosis 2.056033 . end of do-file . exit, clear