R Under development (unstable) (2024-10-01 r87205 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(sasLM) Loading required package: mvtnorm > > f1 = yield ~ block + N*P*K > GLM(f1, npk) $ANOVA Response : yield Df Sum Sq Mean Sq F value Pr(>F) MODEL 11 691.08 62.825 4.0688 0.01156 * RESIDUALS 12 185.29 15.441 CORRECTED TOTAL 23 876.37 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $Fitness Root MSE yield Mean Coef Var R-square Adj R-sq 3.929447 54.875 7.160724 0.7885736 0.5947661 $`Type I` Df Sum Sq Mean Sq F value Pr(>F) block 5 343.29 68.659 4.4467 0.015939 * N 1 189.28 189.282 12.2587 0.004372 ** P 1 8.40 8.402 0.5441 0.474904 N:P 1 21.28 21.282 1.3783 0.263165 K 1 95.20 95.202 6.1657 0.028795 * N:K 1 33.14 33.135 2.1460 0.168648 P:K 1 0.48 0.482 0.0312 0.862752 N:P:K 0 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $`Type II` Df Sum Sq Mean Sq F value Pr(>F) block 4 306.293 76.573 4.9592 0.013587 * N 1 189.282 189.282 12.2587 0.004372 ** P 1 8.402 8.402 0.5441 0.474904 N:P 1 21.282 21.282 1.3783 0.263165 K 1 95.202 95.202 6.1657 0.028795 * N:K 1 33.135 33.135 2.1460 0.168648 P:K 1 0.482 0.482 0.0312 0.862752 N:P:K 0 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $`Type III` CAUTION: Singularity Exists ! Df Sum Sq Mean Sq F value Pr(>F) block 4 306.293 76.573 4.9592 0.013587 * N 1 189.282 189.282 12.2587 0.004372 ** P 1 8.402 8.402 0.5441 0.474904 N:P 1 21.282 21.282 1.3783 0.263165 K 1 95.202 95.202 6.1657 0.028795 * N:K 1 33.135 33.135 2.1460 0.168648 P:K 1 0.482 0.482 0.0312 0.862752 N:P:K 0 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > REG(f1, npk) $ANOVA Response : yield Df Sum Sq Mean Sq F value Pr(>F) MODEL 11 691.08 62.825 4.0688 0.01156 * RESIDUALS 12 185.29 15.441 CORRECTED TOTAL 23 876.37 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $Fitness Root MSE yield Mean Coef Var R-square Adj R-sq PRESS R2pred 3.929447 54.875 7.160724 0.7885736 0.5947661 741.1467 0.1542945 $Coefficients Estimate Std. Error Df Lower CL Upper CL t value Pr(>|t|) (Intercept) 54.600 2.7785 12 48.546 60.654 19.6506 1.713e-10 *** block1 -2.325 2.7785 12 -8.379 3.729 -0.8368 0.41907 block2 1.100 2.7785 12 -4.954 7.154 0.3959 0.69913 block3 4.425 2.7785 12 -1.629 10.479 1.5926 0.13724 block4 -6.225 2.7785 12 -12.279 -0.171 -2.2404 0.04477 * block5 -5.825 2.7785 12 -11.879 0.229 -2.0964 0.05791 . block6 0.000 0.0000 12 0.000 0.000 N0 -1.383 2.7785 12 -7.437 4.671 -0.4979 0.62758 N1 0.000 0.0000 12 0.000 0.000 P0 2.783 2.7785 12 -3.271 8.837 1.0017 0.33625 P1 0.000 0.0000 12 0.000 0.000 N0:P0 -3.767 3.2084 12 -10.757 3.224 -1.1740 0.26317 N0:P1 0.000 0.0000 12 0.000 0.000 N1:P0 0.000 0.0000 12 0.000 0.000 N1:P1 0.000 0.0000 12 0.000 0.000 K0 6.050 2.7785 12 -0.004 12.104 2.1774 0.05013 . K1 0.000 0.0000 12 0.000 0.000 N0:K0 -4.700 3.2084 12 -11.690 2.290 -1.4649 0.16865 N0:K1 0.000 0.0000 12 0.000 0.000 N1:K0 0.000 0.0000 12 0.000 0.000 N1:K1 0.000 0.0000 12 0.000 0.000 P0:K0 0.567 3.2084 12 -6.424 7.557 0.1766 0.86275 P0:K1 0.000 0.0000 12 0.000 0.000 P1:K0 0.000 0.0000 12 0.000 0.000 P1:K1 0.000 0.0000 12 0.000 0.000 N0:P0:K0 0.000 0.0000 12 0.000 0.000 N0:P0:K1 0.000 0.0000 12 0.000 0.000 N0:P1:K0 0.000 0.0000 12 0.000 0.000 N0:P1:K1 0.000 0.0000 12 0.000 0.000 N1:P0:K0 0.000 0.0000 12 0.000 0.000 N1:P0:K1 0.000 0.0000 12 0.000 0.000 N1:P1:K0 0.000 0.0000 12 0.000 0.000 N1:P1:K1 0.000 0.0000 12 0.000 0.000 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Warning message: In REG(f1, npk) : Complete aliased variable(s) exist(s)! > EMS(f1, npk) block N P K N:P N:K P:K N:P:K block 4 0 0 0 0 0 0 0 N 0 12 0 0 6 6 0 3 P 0 0 12 0 6 0 6 3 K 0 0 0 12 0 6 6 3 N:P 0 0 0 0 6 0 0 3 N:K 0 0 0 0 0 6 0 3 P:K 0 0 0 0 0 0 6 3 N:P:K 0 0 0 0 0 0 0 0 > lr(f1, npk) Call: lr(Formula = f1, Data = npk) Residuals: Min 1Q Median 3Q Max -5.3000 -1.6833 0.1583 1.9979 4.4750 Coefficients: (21 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) 54.6000 2.7785 19.651 1.71e-10 *** block1 -2.3250 2.7785 -0.837 0.4191 block2 1.1000 2.7785 0.396 0.6991 block3 4.4250 2.7785 1.593 0.1372 block4 -6.2250 2.7785 -2.240 0.0448 * block5 -5.8250 2.7785 -2.096 0.0579 . N0 -1.3833 2.7785 -0.498 0.6276 P0 2.7833 2.7785 1.002 0.3362 K0 6.0500 2.7785 2.177 0.0501 . N0:P0 -3.7667 3.2084 -1.174 0.2632 N0:K0 -4.7000 3.2084 -1.465 0.1686 P0:K0 0.5667 3.2084 0.177 0.8628 N0:P0:K0 NA NA NA NA --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.929 on 12 degrees of freedom Multiple R-squared: 0.7886, Adjusted R-squared: 0.5948 F-statistic: 4.069 on 11 and 12 DF, p-value: 0.01156 Warning message: In lr(f1, npk) : Complete aliased variable(s) exist(s)! > lr0(f1, npk) Intercept SE(Intercept) Slope SE(Slope) Rsq Pr(>F) block 54.0250 2.7210 3.4250 3.8481 0.3917 0.38518 N 52.0667 1.6133 5.6167 2.2815 0.2160 0.02213 * P 55.4667 1.8132 -1.1833 2.5643 0.0096 0.64899 K 56.8667 1.7202 -3.9833 2.4327 0.1086 0.11577 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > aov1(f1, npk) Response : yield Df Sum Sq Mean Sq F value Pr(>F) MODEL 11 691.08 62.825 4.0688 0.011565 * block 5 343.29 68.659 4.4467 0.015939 * N 1 189.28 189.282 12.2587 0.004372 ** P 1 8.40 8.402 0.5441 0.474904 N:P 1 21.28 21.282 1.3783 0.263165 K 1 95.20 95.202 6.1657 0.028795 * N:K 1 33.14 33.135 2.1460 0.168648 P:K 1 0.48 0.482 0.0312 0.862752 N:P:K 0 RESIDUALS 12 185.29 15.441 CORRECTED TOTAL 23 876.36 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > aov2(f1, npk) Response : yield Df Sum Sq Mean Sq F value Pr(>F) MODEL 11 691.08 62.825 4.0688 0.011565 * block 4 306.29 76.573 4.9592 0.013587 * N 1 189.28 189.282 12.2587 0.004372 ** P 1 8.40 8.402 0.5441 0.474904 K 1 95.20 95.202 6.1657 0.028795 * N:P 1 21.28 21.282 1.3783 0.263165 N:K 1 33.14 33.135 2.1460 0.168648 P:K 1 0.48 0.482 0.0312 0.862752 N:P:K 0 RESIDUALS 12 185.29 15.441 CORRECTED TOTAL 23 876.36 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > aov3(f1, npk) Response : yield Df Sum Sq Mean Sq F value Pr(>F) MODEL 11 691.08 62.825 4.0688 0.011565 * block 4 306.29 76.573 4.9592 0.013587 * N 1 189.28 189.282 12.2587 0.004372 ** P 1 8.40 8.402 0.5441 0.474904 K 1 95.20 95.202 6.1657 0.028795 * N:P 1 21.28 21.282 1.3783 0.263165 N:K 1 33.14 33.135 2.1460 0.168648 P:K 1 0.48 0.482 0.0312 0.862752 N:P:K 0 RESIDUALS 12 185.29 15.441 CORRECTED TOTAL 23 876.36 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > f1b = yield ~ block + N*P*K - 1 > GLM(f1b, npk[-1, ]) $ANOVA Response : yield Df Sum Sq Mean Sq F value Pr(>F) MODEL 12 70515 5876.3 356.31 1.92e-12 *** RESIDUALS 11 181 16.5 UNCORRECTED TOTAL 23 70696 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $Fitness Root MSE yield Mean Coef Var R-square Adj R-sq 4.061048 55.1087 7.36916 0.9974339 0.9946345 $`Type I` Df Sum Sq Mean Sq F value Pr(>F) block 6 70191 11698.5 709.3364 1.403e-13 *** N 1 166 166.1 10.0740 0.008856 ** P 1 6 6.1 0.3723 0.554159 N:P 1 26 26.2 1.5897 0.233460 K 1 88 88.4 5.3588 0.040944 * N:K 1 36 36.0 2.1835 0.167551 P:K 1 1 1.5 0.0892 0.770806 N:P:K 0 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $`Type II` Df Sum Sq Mean Sq F value Pr(>F) block 4 308.667 77.167 4.6790 0.018877 * N 1 183.238 183.238 11.1107 0.006673 ** P 1 7.269 7.269 0.4407 0.520440 N:P 1 24.781 24.781 1.5026 0.245866 K 1 88.378 88.378 5.3588 0.040944 * N:K 1 36.922 36.922 2.2388 0.162719 P:K 1 1.471 1.471 0.0892 0.770806 N:P:K 0 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $`Type III` CAUTION: Singularity Exists ! Df Sum Sq Mean Sq F value Pr(>F) block 4 308.667 77.167 4.6790 0.018877 * N 1 160.589 160.589 9.7373 0.009742 ** P 1 5.013 5.013 0.3040 0.592427 N:P 1 24.781 24.781 1.5026 0.245866 K 1 77.942 77.942 4.7260 0.052416 . N:K 1 36.922 36.922 2.2388 0.162719 P:K 1 1.471 1.471 0.0892 0.770806 N:P:K 0 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > REG(f1b, npk[-1, ]) $ANOVA Response : yield Df Sum Sq Mean Sq F value Pr(>F) MODEL 12 70515 5876.3 356.31 1.92e-12 *** RESIDUALS 11 181 16.5 UNCORRECTED TOTAL 23 70696 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $Fitness Root MSE yield Mean Coef Var R-square Adj R-sq PRESS R2pred 4.061048 55.1087 7.36916 0.9974339 0.9946345 786.4989 0.988875 $Coefficients Estimate Std. Error Df Lower CL Upper CL t value Pr(>|t|) block1 52.971 3.2105 11 45.904 60.037 16.4990 4.165e-09 *** block2 55.700 2.8716 11 49.380 62.020 19.3969 7.421e-10 *** block3 59.025 2.8716 11 52.705 65.345 20.5548 3.984e-10 *** block4 48.375 2.8716 11 42.055 54.695 16.8460 3.340e-09 *** block5 48.775 2.8716 11 42.455 55.095 16.9853 3.060e-09 *** block6 54.600 2.8716 11 48.280 60.920 19.0138 9.187e-10 *** N0 -0.687 3.2105 11 -7.754 6.379 -0.2141 0.83436 N1 0.000 0.0000 11 0.000 0.000 P0 2.551 2.9112 11 -3.856 8.959 0.8764 0.39955 P1 0.000 0.0000 11 0.000 0.000 N0:P0 -4.231 3.4512 11 -11.827 3.366 -1.2258 0.24587 N0:P1 0.000 0.0000 11 0.000 0.000 N1:P0 0.000 0.0000 11 0.000 0.000 N1:P1 0.000 0.0000 11 0.000 0.000 K0 5.818 2.9112 11 -0.589 12.226 1.9985 0.07099 . K1 0.000 0.0000 11 0.000 0.000 N0:K0 -5.164 3.4512 11 -12.760 2.432 -1.4962 0.16272 N0:K1 0.000 0.0000 11 0.000 0.000 N1:K0 0.000 0.0000 11 0.000 0.000 N1:K1 0.000 0.0000 11 0.000 0.000 P0:K0 1.031 3.4512 11 -6.566 8.627 0.2986 0.77081 P0:K1 0.000 0.0000 11 0.000 0.000 P1:K0 0.000 0.0000 11 0.000 0.000 P1:K1 0.000 0.0000 11 0.000 0.000 N0:P0:K0 0.000 0.0000 11 0.000 0.000 N0:P0:K1 0.000 0.0000 11 0.000 0.000 N0:P1:K0 0.000 0.0000 11 0.000 0.000 N0:P1:K1 0.000 0.0000 11 0.000 0.000 N1:P0:K0 0.000 0.0000 11 0.000 0.000 N1:P0:K1 0.000 0.0000 11 0.000 0.000 N1:P1:K0 0.000 0.0000 11 0.000 0.000 N1:P1:K1 0.000 0.0000 11 0.000 0.000 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Warning message: In REG(f1b, npk[-1, ]) : Complete aliased variable(s) exist(s)! > EMS(f1b, npk[-1, ]) block N P K N:P N:K P:K N:P:K block 3.75 0.00000 0.00000 0.00000 0.000000 0.000000 0.000000 0.000000 N 0.00 11.07692 0.00000 0.00000 5.538462 5.538462 0.000000 2.769231 P 0.00 0.00000 11.07692 0.00000 5.538462 0.000000 5.538462 2.769231 K 0.00 0.00000 0.00000 11.07692 0.000000 5.538462 5.538462 2.769231 N:P 0.00 0.00000 0.00000 0.00000 5.538462 0.000000 0.000000 2.769231 N:K 0.00 0.00000 0.00000 0.00000 0.000000 5.538462 0.000000 2.769231 P:K 0.00 0.00000 0.00000 0.00000 0.000000 0.000000 5.538462 2.769231 N:P:K 0.00 0.00000 0.00000 0.00000 0.000000 0.000000 0.000000 0.000000 > lr(f1b, npk[-1, ]) Call: lr(Formula = f1b, Data = npk[-1, ]) Residuals: Min 1Q Median 3Q Max -5.4889 -1.5924 0.3333 2.1243 4.2250 Coefficients: (20 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) block1 52.9708 3.2105 16.499 4.17e-09 *** block2 55.7000 2.8716 19.397 7.42e-10 *** block3 59.0250 2.8716 20.555 3.98e-10 *** block4 48.3750 2.8716 16.846 3.34e-09 *** block5 48.7750 2.8716 16.985 3.06e-09 *** block6 54.6000 2.8716 19.014 9.19e-10 *** N0 -0.6875 3.2105 -0.214 0.834 P0 2.5514 2.9112 0.876 0.400 K0 5.8181 2.9112 1.999 0.071 . N0:P0 -4.2306 3.4512 -1.226 0.246 N0:K0 -5.1639 3.4512 -1.496 0.163 P0:K0 1.0306 3.4512 0.299 0.771 N0:P0:K0 NA NA NA NA --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.061 on 11 degrees of freedom Multiple R-squared: 0.9974, Adjusted R-squared: 0.9946 F-statistic: 356.3 on 12 and 11 DF, p-value: 1.92e-12 Warning message: In lr(f1b, npk[-1, ]) : Complete aliased variable(s) exist(s)! > lr0(f1b, npk[-1, ]) Intercept SE(Intercept) Slope SE(Slope) Rsq Pr(>F) block 55.53333 3.14913 1.91667 4.16591 0.4023 0.6513 N 52.30000 1.71560 5.38333 2.37514 0.1965 0.0341 * P 55.46667 1.82900 -0.74848 2.64473 0.0038 0.7799 K 56.86667 1.74651 -3.67576 2.52545 0.0916 0.1603 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > aov1(f1b, npk[-1, ]) Response : yield Df Sum Sq Mean Sq F value Pr(>F) MODEL 12 70515 5876.3 356.3072 1.920e-12 *** block 6 70191 11698.5 709.3364 1.403e-13 *** N 1 166 166.1 10.0740 0.008856 ** P 1 6 6.1 0.3723 0.554159 N:P 1 26 26.2 1.5897 0.233460 K 1 88 88.4 5.3588 0.040944 * N:K 1 36 36.0 2.1835 0.167551 P:K 1 1 1.5 0.0892 0.770806 N:P:K 0 RESIDUALS 11 181 16.5 UNCORRECTED TOTAL 23 70696 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > aov2(f1b, npk[-1, ]) Response : yield Df Sum Sq Mean Sq F value Pr(>F) MODEL 12 70515 5876.3 356.3072 1.92e-12 *** block 4 309 77.2 4.6790 0.018877 * N 1 183 183.2 11.1107 0.006673 ** P 1 7 7.3 0.4407 0.520440 K 1 88 88.4 5.3588 0.040944 * N:P 1 25 24.8 1.5026 0.245866 N:K 1 37 36.9 2.2388 0.162719 P:K 1 1 1.5 0.0892 0.770806 N:P:K 0 RESIDUALS 11 181 16.5 UNCORRECTED TOTAL 23 70696 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > aov3(f1b, npk[-1, ]) Response : yield Df Sum Sq Mean Sq F value Pr(>F) MODEL 12 70515 5876.3 356.3072 1.92e-12 *** block 4 309 77.2 4.6790 0.018877 * N 1 161 160.6 9.7373 0.009742 ** P 1 5 5.0 0.3040 0.592427 K 1 78 77.9 4.7260 0.052416 . N:P 1 25 24.8 1.5026 0.245866 N:K 1 37 36.9 2.2388 0.162719 P:K 1 1 1.5 0.0892 0.770806 N:P:K 0 RESIDUALS 11 181 16.5 UNCORRECTED TOTAL 23 70696 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > f2 = weight ~ feed > GLM(f2, chickwts) $ANOVA Response : weight Df Sum Sq Mean Sq F value Pr(>F) MODEL 5 231129 46226 15.365 5.936e-10 *** RESIDUALS 65 195556 3009 CORRECTED TOTAL 70 426685 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $Fitness Root MSE weight Mean Coef Var R-square Adj R-sq 54.85029 261.3099 20.99052 0.5416855 0.5064305 $`Type I` Df Sum Sq Mean Sq F value Pr(>F) feed 5 231129 46226 15.365 5.936e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $`Type II` Df Sum Sq Mean Sq F value Pr(>F) feed 5 231129 46226 15.365 5.936e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $`Type III` Df Sum Sq Mean Sq F value Pr(>F) feed 5 231129 46226 15.365 5.936e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > REG(f2, chickwts) $ANOVA Response : weight Df Sum Sq Mean Sq F value Pr(>F) MODEL 5 231129 46226 15.365 5.936e-10 *** RESIDUALS 65 195556 3009 CORRECTED TOTAL 70 426685 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $Fitness Root MSE weight Mean Coef Var R-square Adj R-sq PRESS R2pred 54.85029 261.3099 20.99052 0.5416855 0.5064305 233009.2 0.4539083 $Coefficients Estimate Std. Error Df Lower CL Upper CL t value Pr(>|t|) (Intercept) 328.92 15.834 65 297.294 360.54 20.7729 < 2.2e-16 *** feedcasein -5.33 22.392 65 -50.054 39.39 -0.2382 0.812495 feedhorsebean -168.72 23.485 65 -215.620 -121.81 -7.1839 8.204e-10 *** feedlinseed -110.17 22.392 65 -154.888 -65.45 -4.9198 6.212e-06 *** feedmeatmeal -52.01 22.896 65 -97.734 -6.28 -2.2715 0.026435 * feedsoybean -82.49 21.578 65 -125.582 -39.39 -3.8228 0.000298 *** feedsunflower 0.00 0.000 65 0.000 0.00 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > EMS(f2, chickwts) feed feed 11.80845 > lr(f2, chickwts) Call: lr(Formula = f2, Data = chickwts) Residuals: Min 1Q Median 3Q Max -123.909 -34.413 1.571 38.170 103.091 Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) 328.917 15.834 20.773 < 2e-16 *** feedcasein -5.333 22.393 -0.238 0.812495 feedhorsebean -168.717 23.485 -7.184 8.20e-10 *** feedlinseed -110.167 22.393 -4.920 6.21e-06 *** feedmeatmeal -52.008 22.896 -2.271 0.026435 * feedsoybean -82.488 21.578 -3.823 0.000298 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 54.85 on 65 degrees of freedom Multiple R-squared: 0.5417, Adjusted R-squared: 0.5064 F-statistic: 15.36 on 5 and 65 DF, p-value: 5.936e-10 > lr0(f2, chickwts) Intercept SE(Intercept) Slope SE(Slope) Rsq Pr(>F) feed 323.583 15.834 -163.383 23.485 0.5417 2.068e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > aov1(f2, chickwts) Response : weight Df Sum Sq Mean Sq F value Pr(>F) MODEL 5 231129 46226 15.365 5.936e-10 *** feed 5 231129 46226 15.365 5.936e-10 *** RESIDUALS 65 195556 3009 CORRECTED TOTAL 70 426685 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > aov2(f2, chickwts) Response : weight Df Sum Sq Mean Sq F value Pr(>F) MODEL 5 231129 46226 15.365 5.936e-10 *** feed 5 231129 46226 15.365 5.936e-10 *** RESIDUALS 65 195556 3009 CORRECTED TOTAL 70 426685 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > aov3(f2, chickwts) Response : weight Df Sum Sq Mean Sq F value Pr(>F) MODEL 5 231129 46226 15.365 5.936e-10 *** feed 5 231129 46226 15.365 5.936e-10 *** RESIDUALS 65 195556 3009 CORRECTED TOTAL 70 426685 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > f3 = uptake ~ conc - 1 > GLM(f3, CO2) $ANOVA Response : uptake Df Sum Sq Mean Sq F value Pr(>F) MODEL 1 54468 54468 259.15 < 2.2e-16 *** RESIDUALS 83 17445 210 UNCORRECTED TOTAL 84 71913 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $Fitness Root MSE uptake Mean Coef Var R-square Adj R-sq 14.49762 27.2131 53.27444 0.7574161 0.7544934 $`Type I` Df Sum Sq Mean Sq F value Pr(>F) conc 1 54468 54468 259.15 < 2.2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $`Type II` Df Sum Sq Mean Sq F value Pr(>F) conc 1 54468 54468 259.15 < 2.2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $`Type III` Df Sum Sq Mean Sq F value Pr(>F) conc 1 54468 54468 259.15 < 2.2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > REG(f3, CO2) $ANOVA Response : uptake Df Sum Sq Mean Sq F value Pr(>F) MODEL 1 54468 54468 259.15 < 2.2e-16 *** RESIDUALS 83 17445 210 UNCORRECTED TOTAL 84 71913 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 $Fitness Root MSE uptake Mean Coef Var R-square Adj R-sq PRESS R2pred 14.49762 27.2131 53.27444 0.7574161 0.7544934 17941.83 0.7505078 $Coefficients Estimate Std. Error Df Lower CL Upper CL t value Pr(>|t|) conc 0.048492 0.0030123 83 0.042501 0.054484 16.098 < 2.2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > EMS(f3, CO2) conc conc 23163300 > lr(f3, CO2) Call: lr(Formula = f3, Data = CO2) Residuals: Min 1Q Median 3Q Max -34.092 -0.657 7.774 14.697 28.177 Coefficients: Estimate Std. Error t value Pr(>|t|) conc 0.048492 0.003012 16.1 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 14.5 on 83 degrees of freedom Multiple R-squared: 0.7574, Adjusted R-squared: 0.7545 F-statistic: 259.1 on 1 and 83 DF, p-value: < 2.2e-16 > lr0(f3, CO2) Intercept SE(Intercept) Slope SE(Slope) Rsq Pr(>F) conc 19.5002898 1.8530800 0.0177306 0.0035289 0.2354 2.906e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > aov1(f3, CO2) Response : uptake Df Sum Sq Mean Sq F value Pr(>F) MODEL 1 54468 54468 259.15 < 2.2e-16 *** conc 1 54468 54468 259.15 < 2.2e-16 *** RESIDUALS 83 17445 210 UNCORRECTED TOTAL 84 71913 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > aov2(f3, CO2) Response : uptake Df Sum Sq Mean Sq F value Pr(>F) MODEL 1 54468 54468 259.15 < 2.2e-16 *** conc 1 54468 54468 259.15 < 2.2e-16 *** RESIDUALS 83 17445 210 UNCORRECTED TOTAL 84 71913 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > aov3(f3, CO2) Response : uptake Df Sum Sq Mean Sq F value Pr(>F) MODEL 1 54468 54468 259.15 < 2.2e-16 *** conc 1 54468 54468 259.15 < 2.2e-16 *** RESIDUALS 83 17445 210 UNCORRECTED TOTAL 84 71913 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > Coll(mpg ~ disp + hp + drat + wt + qsec, mtcars) $`Tolerance and VIF` Tol VIF disp 0.1097590 9.110869 hp 0.1922399 5.201833 drat 0.4305997 2.322343 wt 0.1425987 7.012686 qsec 0.3132892 3.191939 $`Collinearity Diagnostics` Eigenvalue Cond. Index disp hp drat wt disp 3.33874601 1.000000 0.0091086322 0.01340997 0.02180314 0.009969706 hp 1.14334450 1.708847 0.0006978666 0.01965345 0.07907011 0.012669147 drat 0.32402239 3.209994 0.0115008746 0.02230435 0.78944241 0.096728964 wt 0.12542374 5.159431 0.1372631744 0.93217393 0.07187070 0.056070646 qsec 0.06846337 6.983330 0.8414294521 0.01245831 0.03781365 0.824561537 qsec disp 0.008214719 hp 0.154627714 drat 0.110814912 wt 0.411429590 qsec 0.314913064 > Coll(mpg ~ disp + hp + drat + wt + qsec - 1, mtcars) $`Tolerance and VIF` Tol VIF disp 0.026696364 37.45828 hp 0.045236036 22.10627 drat 0.015276133 65.46159 wt 0.012211789 81.88808 qsec 0.009462859 105.67631 $`Collinearity Diagnostics` Eigenvalue Cond. Index disp hp drat wt disp 3.33874601 1.000000 0.0091086322 0.01340997 0.02180314 0.009969706 hp 1.14334450 1.708847 0.0006978666 0.01965345 0.07907011 0.012669147 drat 0.32402239 3.209994 0.0115008746 0.02230435 0.78944241 0.096728964 wt 0.12542374 5.159431 0.1372631744 0.93217393 0.07187070 0.056070646 qsec 0.06846337 6.983330 0.8414294521 0.01245831 0.03781365 0.824561537 qsec disp 0.008214719 hp 0.154627714 drat 0.110814912 wt 0.411429590 qsec 0.314913064 > > RD(7, 10, 3, 10) p1 p2 RD SE lower upper 1 0.7 0.3 0.4 0.204939 -0.001673089 0.8016731 > RDmn1(7, 10, 3, 10) p1 p2 RD lower upper 0.70000000 0.30000000 0.40000000 -0.04910451 0.71457639 > RDmn(data.frame(y1=7, n1=10, y2=3, n2=10)) p1 p2 RD lower upper 0.70000000 0.30000000 0.40000000 -0.04910451 0.71457639 > RDinv(data.frame(y1=7, n1=10, y2=3, n2=10)) $RDs p1 p2 RD SE lower upper 1 0.7 0.3 0.4 0.204939 -0.001673089 0.8016731 $Heterogeneity Q prob 1 7.336876e-32 0 $tau2 [1] 0 $Fixed PE SE lower upper 1 0.4 0.204939 -0.001673089 0.8016731 $Random PE SE lower upper 1 0.4 0.204939 -0.001673089 0.8016731 > RDmn(data.frame(y1=c(25, 23), n1=c(339, 370), y2=c(28, 40), n2=c(335, 364))) $Strata p1 p2 RD lower upper 1 0.07374631 0.08358209 -0.009835777 -0.05173666 0.031557500 2 0.06216216 0.10989011 -0.047727948 -0.08979481 -0.007312602 $Common p1 p2 RD lower upper 0.067708578 0.097294000 -0.029585422 -0.059017775 -0.001249758 > RDinv(data.frame(y1=c(25, 23), n1=c(339, 370), y2=c(28, 40), n2=c(335, 364))) $RDs p1 p2 RD SE lower upper 1 0.07374631 0.08358209 -0.009835777 0.02073989 -0.05048522 0.030813662 2 0.06216216 0.10989011 -0.047727948 0.02064662 -0.08819457 -0.007261323 $Heterogeneity Q prob 1 1.676522 0.1953873 $tau2 [1] 0.0002896954 $Fixed PE SE lower upper 1 -0.02886726 0.01463223 -0.0575459 -0.0001886201 $Random PE SE lower upper 1 -0.0288328 0.01894602 -0.06596631 0.008300711 > > RR(7, 10, 3, 10) p1 p2 RR SElog lower upper 1 0.7 0.3 2.333333 0.5255383 0.8329862 6.536056 > RRmn1(7, 10, 3, 10) p1 p2 RR lower upper 0.7000000 0.3000000 2.3333333 0.9264279 6.8474376 > RRmn(data.frame(y1=7, n1=10, y2=3, n2=10)) p1 p2 RR lower upper 0.7000000 0.3000000 2.3333333 0.9264279 6.8474376 > RRinv(data.frame(y1=7, n1=10, y2=3, n2=10)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 0.7 0.3 2.333333 0.5255383 0.8329862 6.536056 100 100 $Heterogeneity Q prob 1 0 1 $tau2 [1] 0 $Fixed RR lower upper 1 2.333333 0.8329862 6.536056 $Random RR lower upper 1 2.333333 0.8329862 6.536056 > RRmn(data.frame(y1=c(25, 23), n1=c(339, 370), y2=c(28, 40), n2=c(335, 364))) $Strata p1 p2 RR lower upper 1 0.07374631 0.08358209 0.8823220 0.5284979 1.4721821 2 0.06216216 0.10989011 0.5656757 0.3470167 0.9195091 $Common p1 p2 RR lower upper 0.06770635 0.09729907 0.71722286 0.50158714 1.02477468 > RRinv(data.frame(y1=c(25, 23), n1=c(339, 370), y2=c(28, 40), n2=c(335, 364))) $RRs p1 p2 RR SElog lower upper pwi 1 0.07374631 0.08358209 0.8823220 0.2641578 0.5257455 1.4807394 52.02783 2 0.06216216 0.10989011 0.5656757 0.2510544 0.3458355 0.9252634 47.97217 pwsi 1 48.29184 2 51.70816 $Heterogeneity Q prob 1 1.487959 0.2225334 $tau2 [1] 0.03240235 $Fixed RR lower upper 1 0.6985385 0.4889789 0.9979083 $Random RR lower upper 1 0.7011316 0.4536439 1.083637 > > OR(7, 10, 3, 10) odd1 odd2 OR SElog lower upper 1 2.333333 0.4285714 5.444444 0.9759001 0.8040183 36.86729 > ORmn1(7, 10, 3, 10) odd1 odd2 OR lower upper 2.3333333 0.4285714 5.4444444 0.8215414 36.0809910 > ORmn(data.frame(y1=7, n1=10, y2=3, n2=10)) odd1 odd2 OR lower upper 2.3333333 0.4285714 5.4444444 0.8215414 36.0809910 > ORinv(data.frame(y1=7, n1=10, y2=3, n2=10)) $ORs odd1 odd2 OR SElog lower upper 1 2.333333 0.4285714 5.444444 0.9759001 0.8040183 36.86729 $Common OR SElog lower upper 1 5.444444 0.9759001 0.8040183 36.86729 > ORcmh(data.frame(y1=7, n1=10, y2=3, n2=10)) $ORs odd1 odd2 OR SElog lower upper 1 2.333333 0.4285714 5.444444 0.9759001 0.8040183 36.86729 $Common OR SElog lower upper 1 5.444444 0.9759001 0.8040183 36.86729 > ORmn(data.frame(y1=c(25, 23), n1=c(339, 370), y2=c(28, 40), n2=c(335, 364))) $Strata odd1 odd2 OR lower upper 1 0.07961783 0.09120521 0.8729527 0.5004020 1.522954 2 0.06628242 0.12345679 0.5368876 0.3160012 0.912411 $Common odd1 odd2 OR lower upper 0.07239721 0.10866823 0.69098604 0.45905900 0.98885667 > ORinv(data.frame(y1=c(25, 23), n1=c(339, 370), y2=c(28, 40), n2=c(335, 364))) $ORs odd1 odd2 OR SElog lower upper 1 0.07961783 0.09120521 0.8729527 0.2866293 0.4977502 1.5309816 2 0.06628242 0.12345679 0.5368876 0.2728489 0.3145096 0.9165007 $Common OR SElog lower upper 1 0.6764574 0.1976255 0.4592206 0.9964593 > ORcmh(data.frame(y1=c(25, 23), n1=c(339, 370), y2=c(28, 40), n2=c(335, 364))) $ORs odd1 odd2 OR SElog lower upper 1 0.07961783 0.09120521 0.8729527 0.2866293 0.4977502 1.5309816 2 0.06628242 0.12345679 0.5368876 0.2728489 0.3145096 0.9165007 $Common OR SElog lower upper 1 0.6740762 0.196439 0.4586694 0.9906452 > > ## Test of extreme input values > > ScoreCI(0, 1) PE Lower Upper 1 0 0 0.7934507 > ScoreCI(0, 2) PE Lower Upper 1 0 0 0.6576198 > ScoreCI(0, 10) PE Lower Upper 1 0 2.775558e-17 0.2775328 > ScoreCI(0, 1e308) PE Lower Upper 1 0 0 3.841459e-308 > ScoreCI(1, 1) PE Lower Upper 1 1 0.2065493 1 > ScoreCI(10, 10) PE Lower Upper 1 1 0.7224672 1 > ScoreCI(1e308, 1e308) PE Lower Upper 1 1 1 1 > > RD(0, 1, 0, 1) p1 p2 RD SE lower upper 1 0 0 0 0 0 0 Warning message: In RD(0, 1, 0, 1) : Note that standard error is too small! > RD(0, 1, 1, 1) p1 p2 RD SE lower upper 1 0 1 -1 0 -1 -1 Warning message: In RD(0, 1, 1, 1) : Note that standard error is too small! > RD(0, 10, 0, 10) p1 p2 RD SE lower upper 1 0 0 0 0 0 0 Warning message: In RD(0, 10, 0, 10) : Note that standard error is too small! > RD(0, 10, 10, 10) p1 p2 RD SE lower upper 1 0 1 -1 0 -1 -1 Warning message: In RD(0, 10, 10, 10) : Note that standard error is too small! > RD(0, 1e308, 0, 1e308) p1 p2 RD SE lower upper 1 0 0 0 0 0 0 Warning message: In RD(0, 1e+308, 0, 1e+308) : Note that standard error is too small! > RD(0, 1e308, 1e308, 1e308) p1 p2 RD SE lower upper 1 0 1 -1 0 -1 -1 Warning message: In RD(0, 1e+308, 1e+308, 1e+308) : Note that standard error is too small! > RD(1, 1, 0, 1) p1 p2 RD SE lower upper 1 1 0 1 0 1 1 Warning message: In RD(1, 1, 0, 1) : Note that standard error is too small! > RD(1, 1, 1, 1) p1 p2 RD SE lower upper 1 1 1 0 0 0 0 Warning message: In RD(1, 1, 1, 1) : Note that standard error is too small! > RD(10, 10, 0, 10) p1 p2 RD SE lower upper 1 1 0 1 0 1 1 Warning message: In RD(10, 10, 0, 10) : Note that standard error is too small! > RD(10, 10, 10, 10) p1 p2 RD SE lower upper 1 1 1 0 0 0 0 Warning message: In RD(10, 10, 10, 10) : Note that standard error is too small! > RD(1e308, 1e308, 0, 1e308) p1 p2 RD SE lower upper 1 1 0 1 0 1 1 Warning message: In RD(1e+308, 1e+308, 0, 1e+308) : Note that standard error is too small! > RD(1e308, 1e308, 1e308, 1e308) p1 p2 RD SE lower upper 1 1 1 0 0 0 0 Warning message: In RD(1e+308, 1e+308, 1e+308, 1e+308) : Note that standard error is too small! > > RDinv(data.frame(y1=0, n1=1, y2=0, n2=1)) $RDs p1 p2 RD SE lower upper 1 0 0 0 0 0 0 $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed PE SE lower upper 1 NaN 0 NaN NaN $Random PE SE lower upper 1 NaN NaN NaN NaN Warning messages: 1: In RD(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RDinv(data.frame(y1 = 0, n1 = 1, y2 = 0, n2 = 1)) : Note that too small standard error exisits! > RDinv(data.frame(y1=0, n1=1, y2=1, n2=1)) $RDs p1 p2 RD SE lower upper 1 0 1 -1 0 -1 -1 $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed PE SE lower upper 1 NaN 0 NaN NaN $Random PE SE lower upper 1 NaN NaN NaN NaN Warning messages: 1: In RD(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RDinv(data.frame(y1 = 0, n1 = 1, y2 = 1, n2 = 1)) : Note that too small standard error exisits! > RDinv(data.frame(y1=0, n1=10, y2=0, n2=10)) $RDs p1 p2 RD SE lower upper 1 0 0 0 0 0 0 $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed PE SE lower upper 1 NaN 0 NaN NaN $Random PE SE lower upper 1 NaN NaN NaN NaN Warning messages: 1: In RD(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RDinv(data.frame(y1 = 0, n1 = 10, y2 = 0, n2 = 10)) : Note that too small standard error exisits! > RDinv(data.frame(y1=0, n1=10, y2=10, n2=10)) $RDs p1 p2 RD SE lower upper 1 0 1 -1 0 -1 -1 $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed PE SE lower upper 1 NaN 0 NaN NaN $Random PE SE lower upper 1 NaN NaN NaN NaN Warning messages: 1: In RD(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RDinv(data.frame(y1 = 0, n1 = 10, y2 = 10, n2 = 10)) : Note that too small standard error exisits! > RDinv(data.frame(y1=1, n1=1, y2=0, n2=1)) $RDs p1 p2 RD SE lower upper 1 1 0 1 0 1 1 $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed PE SE lower upper 1 NaN 0 NaN NaN $Random PE SE lower upper 1 NaN NaN NaN NaN Warning messages: 1: In RD(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RDinv(data.frame(y1 = 1, n1 = 1, y2 = 0, n2 = 1)) : Note that too small standard error exisits! > RDinv(data.frame(y1=1, n1=1, y2=1, n2=1)) $RDs p1 p2 RD SE lower upper 1 1 1 0 0 0 0 $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed PE SE lower upper 1 NaN 0 NaN NaN $Random PE SE lower upper 1 NaN NaN NaN NaN Warning messages: 1: In RD(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RDinv(data.frame(y1 = 1, n1 = 1, y2 = 1, n2 = 1)) : Note that too small standard error exisits! > RDinv(data.frame(y1=10, n1=10, y2=0, n2=10)) $RDs p1 p2 RD SE lower upper 1 1 0 1 0 1 1 $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed PE SE lower upper 1 NaN 0 NaN NaN $Random PE SE lower upper 1 NaN NaN NaN NaN Warning messages: 1: In RD(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RDinv(data.frame(y1 = 10, n1 = 10, y2 = 0, n2 = 10)) : Note that too small standard error exisits! > RDinv(data.frame(y1=10, n1=10, y2=10, n2=10)) $RDs p1 p2 RD SE lower upper 1 1 1 0 0 0 0 $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed PE SE lower upper 1 NaN 0 NaN NaN $Random PE SE lower upper 1 NaN NaN NaN NaN Warning messages: 1: In RD(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RDinv(data.frame(y1 = 10, n1 = 10, y2 = 10, n2 = 10)) : Note that too small standard error exisits! > RDinv(data.frame(y1=0, n1=1e308, y2=0, n2=1e308)) $RDs p1 p2 RD SE lower upper 1 0 0 0 0 0 0 $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed PE SE lower upper 1 NaN 0 NaN NaN $Random PE SE lower upper 1 NaN NaN NaN NaN Warning messages: 1: In RD(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RDinv(data.frame(y1 = 0, n1 = 1e+308, y2 = 0, n2 = 1e+308)) : Note that too small standard error exisits! > RDinv(data.frame(y1=0, n1=1e308, y2=1e308, n2=1e308)) $RDs p1 p2 RD SE lower upper 1 0 1 -1 0 -1 -1 $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed PE SE lower upper 1 NaN 0 NaN NaN $Random PE SE lower upper 1 NaN NaN NaN NaN Warning messages: 1: In RD(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RDinv(data.frame(y1 = 0, n1 = 1e+308, y2 = 1e+308, n2 = 1e+308)) : Note that too small standard error exisits! > RDinv(data.frame(y1=1e308, n1=1e308, y2=0, n2=1e308)) $RDs p1 p2 RD SE lower upper 1 1 0 1 0 1 1 $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed PE SE lower upper 1 NaN 0 NaN NaN $Random PE SE lower upper 1 NaN NaN NaN NaN Warning messages: 1: In RD(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RDinv(data.frame(y1 = 1e+308, n1 = 1e+308, y2 = 0, n2 = 1e+308)) : Note that too small standard error exisits! > RDinv(data.frame(y1=1e308, n1=1e308, y2=1e308, n2=1e308)) $RDs p1 p2 RD SE lower upper 1 1 1 0 0 0 0 $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed PE SE lower upper 1 NaN 0 NaN NaN $Random PE SE lower upper 1 NaN NaN NaN NaN Warning messages: 1: In RD(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RDinv(data.frame(y1 = 1e+308, n1 = 1e+308, y2 = 1e+308, n2 = 1e+308)) : Note that too small standard error exisits! > > RDmn1(0, 1, 0, 1) p1 p2 RD lower upper 0.0000000 0.0000000 0.0000000 -0.8848397 0.8848397 > RDmn1(0, 1, 1, 1) p1 p2 RD lower upper 0.000000 1.000000 -1.000000 -1.000000 0.586901 > RDmn1(0, 10, 0, 10) p1 p2 RD lower upper 0.0000000 0.0000000 0.0000000 -0.2879159 0.2879159 > RDmn1(0, 10, 10, 10) p1 p2 RD lower upper 0.0000000 1.0000000 -1.0000000 -1.0000000 -0.6636451 > RDmn1(1, 1, 0, 1) p1 p2 RD lower upper 1.000000 0.000000 1.000000 -0.586901 1.000000 > RDmn1(1, 1, 1, 1) p1 p2 RD lower upper 1.0000000 1.0000000 0.0000000 -0.8848397 0.8848397 > RDmn1(10, 10, 0, 10) p1 p2 RD lower upper 1.0000000 0.0000000 1.0000000 0.6636451 1.0000000 > RDmn1(10, 10, 10, 10) p1 p2 RD lower upper 1.0000000 1.0000000 0.0000000 -0.2879159 0.2879159 > RDmn1(0, 1e8, 0, 1e8) p1 p2 RD lower upper 0 0 0 0 0 > RDmn1(0, 1e8, 1e8, 1e8) p1 p2 RD lower upper 0 1 -1 -1 -1 > RDmn1(1e8, 1e8, 0, 1e8) p1 p2 RD lower upper 1 0 1 1 1 > RDmn1(1e8, 1e8, 1e8, 1e8) p1 p2 RD lower upper 1 1 0 0 0 > > RDmn(data.frame(y1=0, n1=1, y2=0, n2=1)) p1 p2 RD lower upper 0.0000000 0.0000000 0.0000000 -0.8848397 0.8848397 > RDmn(data.frame(y1=0, n1=1, y2=1, n2=1)) p1 p2 RD lower upper 0.000000 1.000000 -1.000000 -1.000000 0.586901 > RDmn(data.frame(y1=0, n1=10, y2=0, n2=10)) p1 p2 RD lower upper 0.0000000 0.0000000 0.0000000 -0.2879159 0.2879159 > RDmn(data.frame(y1=0, n1=10, y2=10, n2=10)) p1 p2 RD lower upper 0.0000000 1.0000000 -1.0000000 -1.0000000 -0.6636451 > RDmn(data.frame(y1=1, n1=1, y2=0, n2=1)) p1 p2 RD lower upper 1.000000 0.000000 1.000000 -0.586901 1.000000 > RDmn(data.frame(y1=1, n1=1, y2=1, n2=1)) p1 p2 RD lower upper 1.0000000 1.0000000 0.0000000 -0.8848397 0.8848397 > RDmn(data.frame(y1=10, n1=10, y2=0, n2=10)) p1 p2 RD lower upper 1.0000000 0.0000000 1.0000000 0.6636451 1.0000000 > RDmn(data.frame(y1=10, n1=10, y2=10, n2=10)) p1 p2 RD lower upper 1.0000000 1.0000000 0.0000000 -0.2879159 0.2879159 > RDmn(data.frame(y1=0, n1=1e8, y2=0, n2=1e8)) p1 p2 RD lower upper 0 0 0 0 0 > RDmn(data.frame(y1=0, n1=1e8, y2=1e8, n2=1e8)) p1 p2 RD lower upper 0 1 -1 -1 -1 > RDmn(data.frame(y1=1e8, n1=1e8, y2=0, n2=1e8)) p1 p2 RD lower upper 1 0 1 1 1 > RDmn(data.frame(y1=1e8, n1=1e8, y2=1e8, n2=1e8)) p1 p2 RD lower upper 1 1 0 0 0 > > RR(0, 1, 0, 1) p1 p2 RR SElog lower upper 1 0 0 NaN Inf NaN NaN > RR(0, 1, 1, 1) p1 p2 RR SElog lower upper 1 0 1 0 Inf 0 NaN > RR(0, 10, 0, 10) p1 p2 RR SElog lower upper 1 0 0 NaN Inf NaN NaN > RR(0, 10, 10, 10) p1 p2 RR SElog lower upper 1 0 1 0 Inf 0 NaN > RR(0, 1e308, 0, 1e308) p1 p2 RR SElog lower upper 1 0 0 NaN Inf NaN NaN > RR(0, 1e308, 1e308, 1e308) p1 p2 RR SElog lower upper 1 0 1 0 Inf 0 NaN > RR(1, 1, 0, 1) p1 p2 RR SElog lower upper 1 1 0 Inf Inf NaN Inf > RR(1, 1, 1, 1) p1 p2 RR SElog lower upper 1 1 1 1 0 1 1 Warning message: In RR(1, 1, 1, 1) : Note that standard error is too small! > RR(10, 10, 0, 10) p1 p2 RR SElog lower upper 1 1 0 Inf Inf NaN Inf > RR(10, 10, 10, 10) p1 p2 RR SElog lower upper 1 1 1 1 0 1 1 Warning message: In RR(10, 10, 10, 10) : Note that standard error is too small! > RR(1e308, 1e308, 0, 1e308) p1 p2 RR SElog lower upper 1 1 0 Inf Inf NaN Inf > RR(1e308, 1e308, 1e308, 1e308) p1 p2 RR SElog lower upper 1 1 1 1 0 1 1 Warning message: In RR(1e+308, 1e+308, 1e+308, 1e+308) : Note that standard error is too small! > > RRinv(data.frame(y1=0, n1=1, y2=0, n2=1)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 0 0 NaN Inf NaN NaN NaN NaN $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed RR lower upper 1 NaN NaN NaN $Random RR lower upper 1 NaN NaN NaN > RRinv(data.frame(y1=0, n1=1, y2=1, n2=1)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 0 1 0 Inf 0 NaN NaN NaN $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed RR lower upper 1 NaN NaN NaN $Random RR lower upper 1 NaN NaN NaN > RRinv(data.frame(y1=0, n1=10, y2=0, n2=10)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 0 0 NaN Inf NaN NaN NaN NaN $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed RR lower upper 1 NaN NaN NaN $Random RR lower upper 1 NaN NaN NaN > RRinv(data.frame(y1=0, n1=10, y2=10, n2=10)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 0 1 0 Inf 0 NaN NaN NaN $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed RR lower upper 1 NaN NaN NaN $Random RR lower upper 1 NaN NaN NaN > RRinv(data.frame(y1=1, n1=1, y2=0, n2=1)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 1 0 Inf Inf NaN Inf 100 NaN $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed RR lower upper 1 NaN NaN NaN $Random RR lower upper 1 NaN NaN NaN > RRinv(data.frame(y1=1, n1=1, y2=1, n2=1)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 1 1 1 0 1 1 100 NaN $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed RR lower upper 1 NaN NaN NaN $Random RR lower upper 1 NaN NaN NaN Warning messages: 1: In RR(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RRinv(data.frame(y1 = 1, n1 = 1, y2 = 1, n2 = 1)) : Note that standard error is too small! > RRinv(data.frame(y1=10, n1=10, y2=0, n2=10)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 1 0 Inf Inf NaN Inf 100 NaN $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed RR lower upper 1 NaN NaN NaN $Random RR lower upper 1 NaN NaN NaN > RRinv(data.frame(y1=10, n1=10, y2=10, n2=10)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 1 1 1 0 1 1 100 NaN $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed RR lower upper 1 NaN NaN NaN $Random RR lower upper 1 NaN NaN NaN Warning messages: 1: In RR(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RRinv(data.frame(y1 = 10, n1 = 10, y2 = 10, n2 = 10)) : Note that standard error is too small! > RRinv(data.frame(y1=0, n1=1e308, y2=0, n2=1e308)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 0 0 NaN Inf NaN NaN NaN NaN $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed RR lower upper 1 NaN NaN NaN $Random RR lower upper 1 NaN NaN NaN > RRinv(data.frame(y1=0, n1=1e308, y2=1e308, n2=1e308)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 0 1 0 Inf 0 NaN NaN NaN $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed RR lower upper 1 NaN NaN NaN $Random RR lower upper 1 NaN NaN NaN > RRinv(data.frame(y1=1e308, n1=1e308, y2=0, n2=1e308)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 1 0 Inf Inf NaN Inf NaN NaN $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed RR lower upper 1 NaN NaN NaN $Random RR lower upper 1 NaN NaN NaN > RRinv(data.frame(y1=1e308, n1=1e308, y2=1e308, n2=1e308)) $RRs p1 p2 RR SElog lower upper pwi pwsi 1 1 1 1 0 1 1 NaN NaN $Heterogeneity Q prob 1 NaN NaN $tau2 [1] NaN $Fixed RR lower upper 1 NaN NaN NaN $Random RR lower upper 1 NaN NaN NaN Warning messages: 1: In RR(y1, n1, y2, n2, conf.level = conf.level) : Note that standard error is too small! 2: In RRinv(data.frame(y1 = 1e+308, n1 = 1e+308, y2 = 1e+308, n2 = 1e+308)) : Note that standard error is too small! > > RRmn1(0, 1, 0, 1) p1 p2 RR lower upper 0 0 NaN 0 Inf > RRmn1(0, 1, 1, 1) p1 p2 RR lower upper 0.000000 1.000000 0.000000 0.000000 1.920729 > RRmn1(1, 1, 0, 1) p1 p2 RR lower upper 1.0000000 0.0000000 Inf 0.5206355 Inf > RRmn1(1, 1, 1, 1) p1 p2 RR lower upper 1.0000000 1.0000000 1.0000000 0.2065493 4.8414588 > > RRmn1(0, 2, 0, 2) p1 p2 RR lower upper 0 0 NaN 0 Inf > RRmn1(0, 2, 1, 2) p1 p2 RR lower upper 0.000000 0.500000 0.000000 0.000000 2.675134 > RRmn1(0, 2, 2, 2) p1 p2 RR lower upper 0.0000000 1.0000000 0.0000000 0.0000000 0.9603647 > RRmn1(1, 2, 0, 2) p1 p2 RR lower upper 0.500000 0.000000 Inf 0.373815 Inf > RRmn1(1, 2, 1, 2) p1 p2 RR lower upper 0.5000000 0.5000000 1.0000000 0.1505733 6.6414748 > RRmn1(1, 2, 2, 2) p1 p2 RR lower upper 0.50000000 1.00000000 0.50000000 0.09451541 1.94054706 > RRmn1(2, 2, 0, 2) p1 p2 RR lower upper 1.000000 0.000000 Inf 1.041271 Inf > RRmn1(2, 2, 1, 2) p1 p2 RR lower upper 1.0000000 0.5000000 2.0000000 0.5153186 10.5802852 > RRmn1(2, 2, 2, 2) p1 p2 RR lower upper 1.0000000 1.0000000 1.0000000 0.3423802 2.9207294 > > RRmn1(0, 30, 0, 30) p1 p2 RR lower upper 0 0 NaN 0 Inf > RRmn1(0, 30, 15, 30) p1 p2 RR lower upper 0.0000000 0.5000000 0.0000000 0.0000000 0.2303211 > RRmn1(0, 30, 30, 30) p1 p2 RR lower upper 0.0000000 1.0000000 0.0000000 0.0000000 0.1135261 > RRmn1(15, 30, 0, 30) p1 p2 RR lower upper 0.500000 0.000000 Inf 4.341783 Inf > RRmn1(15, 30, 5, 30) p1 p2 RR lower upper 0.5000000 0.1666667 3.0000000 1.3301089 7.2373807 > RRmn1(15, 30, 30, 30) p1 p2 RR lower upper 0.5000000 1.0000000 0.5000000 0.3315157 0.6684737 > RRmn1(30, 30, 0, 30) p1 p2 RR lower upper 1.000000 0.000000 Inf 8.808551 Inf > RRmn1(30, 30, 15, 30) p1 p2 RR lower upper 1.000000 0.500000 2.000000 1.495945 3.016448 > RRmn1(30, 30, 30, 30) p1 p2 RR lower upper 1.0000000 1.0000000 1.0000000 0.8864866 1.1280486 > > RRmn(data.frame(y1=0, n1=1, y2=0, n2=1)) p1 p2 RR lower upper 0 0 NaN 0 Inf > RRmn(data.frame(y1=0, n1=1, y2=1, n2=1)) p1 p2 RR lower upper 0.000000 1.000000 0.000000 0.000000 1.920729 > RRmn(data.frame(y1=0, n1=10, y2=0, n2=10)) p1 p2 RR lower upper 0 0 NaN 0 Inf > RRmn(data.frame(y1=0, n1=10, y2=10, n2=10)) p1 p2 RR lower upper 0.0000000 1.0000000 0.0000000 0.0000000 0.2775082 > RRmn(data.frame(y1=1, n1=1, y2=0, n2=1)) p1 p2 RR lower upper 1.0000000 0.0000000 Inf 0.5206355 Inf > RRmn(data.frame(y1=1, n1=1, y2=1, n2=1)) p1 p2 RR lower upper 1.0000000 1.0000000 1.0000000 0.2065493 4.8414588 > RRmn(data.frame(y1=10, n1=10, y2=0, n2=10)) p1 p2 RR lower upper 1.000000 0.000000 Inf 3.603497 Inf > RRmn(data.frame(y1=10, n1=10, y2=10, n2=10)) p1 p2 RR lower upper 1.0000000 1.0000000 1.0000000 0.7224672 1.3841459 > RRmn(data.frame(y1=0, n1=1e8, y2=0, n2=1e8)) p1 p2 RR lower upper 0 0 NaN 0 Inf > RRmn(data.frame(y1=0, n1=1e8, y2=1e8, n2=1e8)) p1 p2 RR lower upper 0e+00 1e+00 0e+00 0e+00 1e-08 > RRmn(data.frame(y1=1e8, n1=1e8, y2=0, n2=1e8)) p1 p2 RR lower upper 1e+00 0e+00 Inf 1e+08 Inf > RRmn(data.frame(y1=1e8, n1=1e8, y2=1e8, n2=1e8)) p1 p2 RR lower upper 1 1 1 1 1 > > OR(0, 1, 0, 1) odd1 odd2 OR SElog lower upper 1 0.3333333 0.3333333 1 2.309401 0.01082017 92.41996 Warning message: In OR(0, 1, 0, 1) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > OR(0, 1, 1, 1) odd1 odd2 OR SElog lower upper 1 0.3333333 3 0.1111111 2.309401 0.001202242 10.26888 Warning message: In OR(0, 1, 1, 1) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > OR(0, 10, 0, 10) odd1 odd2 OR SElog lower upper 1 0.04761905 0.04761905 1 2.047065 0.01809401 55.2669 Warning message: In OR(0, 10, 0, 10) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > OR(0, 10, 10, 10) odd1 odd2 OR SElog lower upper 1 0.04761905 21 0.002267574 2.047065 4.102951e-05 0.1253218 Warning message: In OR(0, 10, 10, 10) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > OR(0, 1e308, 0, 1e308) odd1 odd2 OR SElog lower upper 1 5e-309 5e-309 1 2 0.01984252 50.39681 Warning message: In OR(0, 1e+308, 0, 1e+308) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > OR(0, 1e308, 1e308, 1e308) odd1 odd2 OR SElog lower upper 1 5e-309 Inf 0 Inf 0 NaN Warning message: In OR(0, 1e+308, 1e+308, 1e+308) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > OR(1, 1, 0, 1) odd1 odd2 OR SElog lower upper 1 3 0.3333333 9 2.309401 0.09738156 831.7796 Warning message: In OR(1, 1, 0, 1) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > OR(1, 1, 1, 1) odd1 odd2 OR SElog lower upper 1 3 3 1 2.309401 0.01082017 92.41996 Warning message: In OR(1, 1, 1, 1) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > OR(10, 10, 0, 10) odd1 odd2 OR SElog lower upper 1 21 0.04761905 441 2.047065 7.97946 24372.7 Warning message: In OR(10, 10, 0, 10) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > OR(10, 10, 10, 10) odd1 odd2 OR SElog lower upper 1 21 21 1 2.047065 0.01809401 55.2669 Warning message: In OR(10, 10, 10, 10) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > OR(1e308, 1e308, 0, 1e308) odd1 odd2 OR SElog lower upper 1 Inf 5e-309 Inf Inf NaN Inf Warning message: In OR(1e+308, 1e+308, 0, 1e+308) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > OR(1e308, 1e308, 1e308, 1e308) odd1 odd2 OR SElog lower upper 1 Inf Inf NaN Inf NaN NaN Warning message: In OR(1e+308, 1e+308, 1e+308, 1e+308) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > > ORcmh(data.frame(y1=0, n1=1, y2=0, n2=1)) $ORs odd1 odd2 OR SElog lower upper 1 0.3333333 0.3333333 1 2.309401 0.01082017 92.41996 $Common OR SElog lower upper 1 NaN NaN NaN NaN Warning message: In OR(y1, n1, y2, n2, conf.level = conf.level) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > ORcmh(data.frame(y1=0, n1=1, y2=1, n2=1)) $ORs odd1 odd2 OR SElog lower upper 1 0.3333333 3 0.1111111 2.309401 0.001202242 10.26888 $Common OR SElog lower upper 1 0 NaN NaN NaN Warning message: In OR(y1, n1, y2, n2, conf.level = conf.level) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > ORcmh(data.frame(y1=1, n1=1, y2=0, n2=1)) $ORs odd1 odd2 OR SElog lower upper 1 3 0.3333333 9 2.309401 0.09738156 831.7796 $Common OR SElog lower upper 1 Inf NaN NaN NaN Warning message: In OR(y1, n1, y2, n2, conf.level = conf.level) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > ORcmh(data.frame(y1=1, n1=1, y2=1, n2=1)) $ORs odd1 odd2 OR SElog lower upper 1 3 3 1 2.309401 0.01082017 92.41996 $Common OR SElog lower upper 1 NaN NaN NaN NaN Warning message: In OR(y1, n1, y2, n2, conf.level = conf.level) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > ORcmh(data.frame(y1=0, n1=10, y2=0, n2=10)) $ORs odd1 odd2 OR SElog lower upper 1 0.04761905 0.04761905 1 2.047065 0.01809401 55.2669 $Common OR SElog lower upper 1 NaN NaN NaN NaN Warning message: In OR(y1, n1, y2, n2, conf.level = conf.level) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > ORcmh(data.frame(y1=0, n1=10, y2=10, n2=10)) $ORs odd1 odd2 OR SElog lower upper 1 0.04761905 21 0.002267574 2.047065 4.102951e-05 0.1253218 $Common OR SElog lower upper 1 0 NaN NaN NaN Warning message: In OR(y1, n1, y2, n2, conf.level = conf.level) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > ORcmh(data.frame(y1=10, n1=10, y2=0, n2=10)) $ORs odd1 odd2 OR SElog lower upper 1 21 0.04761905 441 2.047065 7.97946 24372.7 $Common OR SElog lower upper 1 Inf NaN NaN NaN Warning message: In OR(y1, n1, y2, n2, conf.level = conf.level) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > ORcmh(data.frame(y1=10, n1=10, y2=10, n2=10)) $ORs odd1 odd2 OR SElog lower upper 1 21 21 1 2.047065 0.01809401 55.2669 $Common OR SElog lower upper 1 NaN NaN NaN NaN Warning message: In OR(y1, n1, y2, n2, conf.level = conf.level) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > ORcmh(data.frame(y1=0, n1=1e308, y2=0, n2=1e308)) $ORs odd1 odd2 OR SElog lower upper 1 5e-309 5e-309 1 2 0.01984252 50.39681 $Common OR SElog lower upper 1 NaN NaN NaN NaN Warning message: In OR(y1, n1, y2, n2, conf.level = conf.level) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > ORcmh(data.frame(y1=0, n1=1e308, y2=1e308, n2=1e308)) $ORs odd1 odd2 OR SElog lower upper 1 5e-309 Inf 0 Inf 0 NaN $Common OR SElog lower upper 1 NaN NaN NaN NaN Warning message: In OR(y1, n1, y2, n2, conf.level = conf.level) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > ORcmh(data.frame(y1=1e308, n1=1e308, y2=0, n2=1e308)) $ORs odd1 odd2 OR SElog lower upper 1 Inf 5e-309 Inf Inf NaN Inf $Common OR SElog lower upper 1 NaN NaN NaN NaN Warning message: In OR(y1, n1, y2, n2, conf.level = conf.level) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > ORcmh(data.frame(y1=1e308, n1=1e308, y2=1e308, n2=1e308)) $ORs odd1 odd2 OR SElog lower upper 1 Inf Inf NaN Inf NaN NaN $Common OR SElog lower upper 1 NaN NaN NaN NaN Warning message: In OR(y1, n1, y2, n2, conf.level = conf.level) : Values are added: 0.5s to the numberator, 1s to the denominator. Consider other ways too! > > ORmn1(0, 1, 0, 1) odd1 odd2 OR lower upper 0 0 NaN 0 Inf > ORmn1(0, 1, 1, 1) odd1 odd2 OR lower upper 0.00000 Inf 0.00000 0.00000 14.75681 > ORmn1(1, 1, 0, 1) odd1 odd2 OR lower upper Inf 0.00000000 Inf 0.06776254 Inf > ORmn1(1, 1, 1, 1) odd1 odd2 OR lower upper Inf Inf NaN 0 Inf > > ORmn1(0, 10, 0, 10) odd1 odd2 OR lower upper 0 0 NaN 0 Inf > ORmn1(0, 10, 5, 10) odd1 odd2 OR lower upper 0.0000000 1.0000000 0.0000000 0.0000000 0.5237654 > ORmn1(0, 10, 10, 10) odd1 odd2 OR lower upper 0.00000000 Inf 0.00000000 0.00000000 0.04087758 > ORmn1(5, 10, 0, 10) odd1 odd2 OR lower upper 1.000000 0.000000 Inf 1.909252 Inf > ORmn1(5, 10, 5, 10) odd1 odd2 OR lower upper 1.0000000 1.0000000 1.0000000 0.1750117 5.7139332 > ORmn1(5, 10, 10, 10) odd1 odd2 OR lower upper 1.0000000 Inf 0.0000000 0.0000000 0.5237654 > ORmn1(10, 10, 0, 10) odd1 odd2 OR lower upper Inf 0.00000 Inf 24.46329 Inf > ORmn1(10, 10, 5, 10) odd1 odd2 OR lower upper Inf 1.000000 Inf 1.909252 Inf > ORmn1(10, 10, 10, 10) odd1 odd2 OR lower upper Inf Inf NaN 0 Inf > > ORmn1(0, 1e8, 0, 1e8) odd1 odd2 OR lower upper 0 0 NaN 0 Inf > ORmn1(0, 1e4, 1e4, 1e4) odd1 odd2 OR lower upper 0e+00 Inf 0e+00 0e+00 1e-08 > ORmn1(1e4, 1e4, 0, 1e4) odd1 odd2 OR lower upper Inf 0 Inf 27103426 Inf > ORmn1(1e8, 1e8, 1e8, 1e8) odd1 odd2 OR lower upper Inf Inf NaN 0 Inf > > ORmn1(0, 1, 0, 1) odd1 odd2 OR lower upper 0 0 NaN 0 Inf > ORmn1(1e-2, 1, 1, 1) odd1 odd2 OR lower upper 0.01010101 Inf 0.00000000 0.00000000 15.12756431 > ORmn1(0, 10, 0, 10) odd1 odd2 OR lower upper 0 0 NaN 0 Inf > ORmn1(1e-2, 10, 10, 10) odd1 odd2 OR lower upper 0.001001001 Inf 0.000000000 0.000000000 0.041388621 > ORmn1(1, 1, 1e-2, 1) odd1 odd2 OR lower upper Inf 0.01010101 Inf 0.06610294 Inf > ORmn1(1, 1, 1, 1) odd1 odd2 OR lower upper Inf Inf NaN 0 Inf > ORmn1(10, 10, 1e-2, 10) odd1 odd2 OR lower upper Inf 0.001001001 Inf 24.175637470 Inf > ORmn1(10, 10, 10, 10) odd1 odd2 OR lower upper Inf Inf NaN 0 Inf > > ORmn(data.frame(y1=0, n1=1, y2=0, n2=1)) odd1 odd2 OR lower upper 0 0 NaN 0 Inf > ORmn(data.frame(y1=0, n1=1, y2=1, n2=1)) odd1 odd2 OR lower upper 0.00000 Inf 0.00000 0.00000 14.75681 > ORmn(data.frame(y1=1, n1=1, y2=0, n2=1)) odd1 odd2 OR lower upper Inf 0.00000000 Inf 0.06776254 Inf > ORmn(data.frame(y1=1, n1=1, y2=1, n2=1)) odd1 odd2 OR lower upper Inf Inf NaN 0 Inf > ORmn(data.frame(y1=0, n1=10, y2=0, n2=10)) odd1 odd2 OR lower upper 0 0 NaN 0 Inf > ORmn(data.frame(y1=0, n1=10, y2=10, n2=10)) odd1 odd2 OR lower upper 0.00000000 Inf 0.00000000 0.00000000 0.04087758 > ORmn(data.frame(y1=10, n1=10, y2=0, n2=10)) odd1 odd2 OR lower upper Inf 0.00000 Inf 24.46329 Inf > ORmn(data.frame(y1=10, n1=10, y2=10, n2=10)) odd1 odd2 OR lower upper Inf Inf NaN 0 Inf > ORmn(data.frame(y1=0, n1=1e308, y2=0, n2=1e308)) odd1 odd2 OR lower upper 0 0 NaN 0 Inf > ORmn(data.frame(y1=0, n1=1e4, y2=1e4, n2=1e4)) odd1 odd2 OR lower upper 0e+00 Inf 0e+00 0e+00 1e-08 > ORmn(data.frame(y1=1e4, n1=1e4, y2=0, n2=1e4)) odd1 odd2 OR lower upper Inf 0 Inf 27103426 Inf > ORmn(data.frame(y1=1e308, n1=1e308, y2=1e308, n2=1e308)) odd1 odd2 OR lower upper Inf Inf NaN 0 Inf > > proc.time() user system elapsed 1.48 0.15 1.56