-------------------------------------------------------------------------------------------------------------- name: log: c:\vhm812-data\l11a_intro_cluster.txt log type: text opened on: 19 Mar 2015, 11:01:19 . . *Continuous data herd level predictor . use "simcont_clustherd.dta", clear . *ignoring clustering . reg milk X Source | SS df MS Number of obs = 11626 -------------+------------------------------ F( 1, 11624) = 317.72 Model | 36598.5078 1 36598.5078 Prob > F = 0.0000 Residual | 1338999 11624 115.192618 R-squared = 0.0266 -------------+------------------------------ Adj R-squared = 0.0265 Total | 1375597.51 11625 118.330968 Root MSE = 10.733 ------------------------------------------------------------------------------ milk | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 3.55661 .199534 17.82 0.000 3.16549 3.94773 _cons | 30.0215 .1457715 205.95 0.000 29.73576 30.30723 ------------------------------------------------------------------------------ . * accounting for clustering . mixed milk X || herd: , reml stddev Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -40902.479 Iteration 1: log restricted-likelihood = -40902.479 Computing standard errors: Mixed-effects REML regression Number of obs = 11626 Group variable: herd Number of groups = 100 Obs per group: min = 20 avg = 116.3 max = 311 Wald chi2(1) = 6.44 Log restricted-likelihood = -40902.479 Prob > chi2 = 0.0112 ------------------------------------------------------------------------------ milk | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 3.796004 1.495943 2.54 0.011 .864009 6.727999 _cons | 31.13696 1.058717 29.41 0.000 29.06191 33.21201 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ herd: Identity | sd(_cons) | 7.410465 .5396842 6.424728 8.547442 -----------------------------+------------------------------------------------ sd(Residual) | 8.012545 .0527739 7.909774 8.11665 ------------------------------------------------------------------------------ LR test vs. linear regression: chibar2(01) = 6374.40 Prob >= chibar2 = 0.0000 . . *herd average . collapse (mean) milk X, by(herd) . reg milk X Source | SS df MS Number of obs = 100 -------------+------------------------------ F( 1, 98) = 6.37 Model | 356.97798 1 356.97798 Prob > F = 0.0132 Residual | 5493.56229 98 56.056758 R-squared = 0.0610 -------------+------------------------------ Adj R-squared = 0.0514 Total | 5850.54027 99 59.0963664 Root MSE = 7.4871 ------------------------------------------------------------------------------ milk | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 3.778772 1.497421 2.52 0.013 .8071885 6.750356 _cons | 31.16586 1.058837 29.43 0.000 29.06463 33.26708 ------------------------------------------------------------------------------ . . *Continuous data cow level predictor . use "simcont_clustcow.dta", clear . * ignoring clustering . reg milk X Source | SS df MS Number of obs = 11626 -------------+------------------------------ F( 1, 11624) = 624.90 Model | 72138.7619 1 72138.7619 Prob > F = 0.0000 Residual | 1341880.62 11624 115.440522 R-squared = 0.0510 -------------+------------------------------ Adj R-squared = 0.0509 Total | 1414019.39 11625 121.636076 Root MSE = 10.744 ------------------------------------------------------------------------------ milk | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 4.982006 .1992962 25.00 0.000 4.591352 5.37266 _cons | 29.25664 .1412627 207.11 0.000 28.97974 29.53354 ------------------------------------------------------------------------------ . * accounting for clustering . mixed milk X || herd:, reml stddev Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = -40947.175 Iteration 1: log restricted-likelihood = -40947.175 Computing standard errors: Mixed-effects REML regression Number of obs = 11626 Group variable: herd Number of groups = 100 Obs per group: min = 20 avg = 116.3 max = 311 Wald chi2(1) = 1108.56 Log restricted-likelihood = -40947.175 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ milk | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 4.968194 .1492174 33.30 0.000 4.675733 5.260655 _cons | 30.64647 .7281276 42.09 0.000 29.21936 32.07357 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ herd: Identity | sd(_cons) | 7.170209 .5201795 6.219843 8.265787 -----------------------------+------------------------------------------------ sd(Residual) | 8.044296 .0529852 7.941114 8.148818 ------------------------------------------------------------------------------ LR test vs. linear regression: chibar2(01) = 6310.00 Prob >= chibar2 = 0.0000 . . *Discrete data herd level predictor . use "simbin_clustherd.dta", clear . * ignoring clustering . logit Y X Iteration 0: log likelihood = -6894.3552 Iteration 1: log likelihood = -6815.0583 Iteration 2: log likelihood = -6814.7785 Iteration 3: log likelihood = -6814.7785 Logistic regression Number of obs = 11626 LR chi2(1) = 159.15 Prob > chi2 = 0.0000 Log likelihood = -6814.7785 Pseudo R2 = 0.0115 ------------------------------------------------------------------------------ Y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | .5287317 .0423191 12.49 0.000 .4457877 .6116757 _cons | -1.241768 .0325699 -38.13 0.000 -1.305604 -1.177932 ------------------------------------------------------------------------------ . * accounting for clustering . meqrlogit Y X || herd: Refining starting values: Iteration 0: log likelihood = -6065.2694 Iteration 1: log likelihood = -6065.1778 Iteration 2: log likelihood = -6065.0871 Performing gradient-based optimization: Iteration 0: log likelihood = -6065.0871 Iteration 1: log likelihood = -6065.0867 Mixed-effects logistic regression Number of obs = 11626 Group variable: herd Number of groups = 100 Obs per group: min = 20 avg = 116.3 max = 311 Integration points = 7 Wald chi2(1) = 9.25 Log likelihood = -6065.0867 Prob > chi2 = 0.0024 ------------------------------------------------------------------------------ Y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | .619974 .2038516 3.04 0.002 .2204322 1.019516 _cons | -1.305417 .1455518 -8.97 0.000 -1.590693 -1.020141 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ herd: Identity | var(_cons) | .9416184 .1492971 .6901028 1.284802 ------------------------------------------------------------------------------ LR test vs. logistic regression: chibar2(01) = 1499.38 Prob>=chibar2 = 0.0000 . . *Discrete data cow level predictor . use "simbin_clustcow.dta", clear . * ignoring clustering . logit Y X Iteration 0: log likelihood = -6910.3442 Iteration 1: log likelihood = -6811.48 Iteration 2: log likelihood = -6811.0741 Iteration 3: log likelihood = -6811.0741 Logistic regression Number of obs = 11626 LR chi2(1) = 198.54 Prob > chi2 = 0.0000 Log likelihood = -6811.0741 Pseudo R2 = 0.0144 ------------------------------------------------------------------------------ Y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | .5863084 .0419748 13.97 0.000 .5040393 .6685775 _cons | -1.25032 .0316033 -39.56 0.000 -1.312261 -1.188379 ------------------------------------------------------------------------------ . * accounting for clustering . meqrlogit Y X || herd: Refining starting values: Iteration 0: log likelihood = -5999.0538 Iteration 1: log likelihood = -5995.9727 Iteration 2: log likelihood = -5995.9698 Performing gradient-based optimization: Iteration 0: log likelihood = -5995.9698 Iteration 1: log likelihood = -5995.9698 Mixed-effects logistic regression Number of obs = 11626 Group variable: herd Number of groups = 100 Obs per group: min = 20 avg = 116.3 max = 311 Integration points = 7 Wald chi2(1) = 229.28 Log likelihood = -5995.9698 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ Y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | .697479 .046063 15.14 0.000 .6071972 .7877609 _cons | -1.361173 .1112103 -12.24 0.000 -1.579141 -1.143204 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ herd: Identity | var(_cons) | 1.068172 .168255 .7844458 1.45452 ------------------------------------------------------------------------------ LR test vs. logistic regression: chibar2(01) = 1630.21 Prob>=chibar2 = 0.0000 . end of do-file . h reg . mixed milk X || herd: , stddev variable milk not found r(111); . do "C:\Users\JAVIER~1\AppData\Local\Temp\STD02000000.tmp" . use "simcont_clustherd.dta", clear . end of do-file . mixed milk X || herd: , stddev Performing EM optimization: Performing gradient-based optimization: Iteration 0: log likelihood = -40904.419 Iteration 1: log likelihood = -40904.419 Computing standard errors: Mixed-effects ML regression Number of obs = 11626 Group variable: herd Number of groups = 100 Obs per group: min = 20 avg = 116.3 max = 311 Wald chi2(1) = 6.57 Log likelihood = -40904.419 Prob > chi2 = 0.0104 ------------------------------------------------------------------------------ milk | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 3.796341 1.480879 2.56 0.010 .8938719 6.69881 _cons | 31.13638 1.048074 29.71 0.000 29.08219 33.19057 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ herd: Identity | sd(_cons) | 7.334446 .5289867 6.367597 8.4481 -----------------------------+------------------------------------------------ sd(Residual) | 8.012546 .0527739 7.909776 8.116651 ------------------------------------------------------------------------------ LR test vs. linear regression: chibar2(01) = 6366.36 Prob >= chibar2 = 0.0000 . log close name: log: c:\vhm812-data\l11a_intro_cluster.txt log type: text closed on: 19 Mar 2015, 15:29:36 --------------------------------------------------------------------------------------------------------------