Last updated on 2026-07-23 02:51:02 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.1 | 8.58 | 86.34 | 94.92 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.1 | 7.56 | 59.69 | 67.25 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.0.1 | 16.00 | 137.29 | 153.29 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.1 | 69.37 | OK | |||
| r-devel-windows-x86_64 | 1.0.1 | 13.00 | 112.00 | 125.00 | OK | |
| r-patched-linux-x86_64 | 1.0.1 | 11.56 | 84.81 | 96.37 | OK | |
| r-release-linux-x86_64 | 1.0.1 | 8.94 | 86.17 | 95.11 | OK | |
| r-release-macos-arm64 | 1.0.1 | 3.00 | 48.00 | 51.00 | OK | |
| r-release-macos-x86_64 | 1.0.1 | 7.00 | 146.00 | 153.00 | OK | |
| r-release-windows-x86_64 | 1.0.1 | 13.00 | 128.00 | 141.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.1 | OK | ||||
| r-oldrel-macos-x86_64 | 1.0.1 | 7.00 | 153.00 | 160.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.1 | 17.00 | 140.00 | 157.00 | OK |
Version: 1.0.1
Check: tests
Result: ERROR
Running ‘Test.R’ [1s/2s]
Running the tests in ‘tests/Test.R’ failed.
Complete output:
> 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
0.0677085653 0.0972940300 -0.0295854647 -0.0589400053 -0.0008563609
> 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
0.06770624 0.09729930 0.69585539 0.48873173 0.98979282
> 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
0.07235566 0.10843574 0.67410200 0.45950894 0.98888123
> 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, 40, 0, 40)
p1 p2 RD lower upper
0.00000000 0.00000000 0.00000000 -0.08863178 0.08863178
> RDmn1(40, 40, 40, 40)
p1 p2 RD lower upper
1.00000000 1.00000000 0.00000000 -0.08863178 0.08863178
> 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) :*** buffer overflow detected ***: terminated
Aborted
Flavor: r-devel-linux-x86_64-debian-gcc