The statistical significance of differences between groups has been evaluated using one-way ANOVA, followed by Dunnett’s test to compare treatment groups to the control and Tukey’s test to compare groups to each other. The analysis has been performed using the Multcomp package for R.
| Group | Ligand | Time | Treatment |
|---|---|---|---|
| A | Saline | 1 hr pre | 100 umol / kg |
| B | HOPO | 24 hr pre | 100 umol / kg |
| C | HOPO | 1 hr pre | 100 umol / kg |
| D | DTPA | 1 hr pre | 100 umol / kg |
| E | HOPO | 1 hr post | 100 umol / kg |
| F | DTPA | 1 hr post | 100 umol / kg |
| G | HOPO | 24 hr post | 100 umol / kg |
| H | HOPO | 48 hr post | 100 umol / kg |
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: aov(formula = Body ~ Group, data = reten)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.50673 0.01041 -48.67 <1e-10 ***
## C - A == 0 -0.55900 0.01041 -53.69 <1e-10 ***
## D - A == 0 -0.47516 0.01041 -45.64 <1e-10 ***
## E - A == 0 -0.39713 0.01041 -38.14 <1e-10 ***
## F - A == 0 -0.14984 0.01041 -14.39 <1e-10 ***
## G - A == 0 -0.23232 0.01041 -22.31 <1e-10 ***
## H - A == 0 -0.22376 0.01041 -21.49 <1e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = Body ~ Group, data = reten)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.50673 0.01041 -48.668 <0.001 ***
## C - A == 0 -0.55900 0.01041 -53.688 <0.001 ***
## D - A == 0 -0.47516 0.01041 -45.636 <0.001 ***
## E - A == 0 -0.39713 0.01041 -38.142 <0.001 ***
## F - A == 0 -0.14984 0.01041 -14.391 <0.001 ***
## G - A == 0 -0.23232 0.01041 -22.313 <0.001 ***
## H - A == 0 -0.22376 0.01041 -21.491 <0.001 ***
## C - B == 0 -0.05227 0.01041 -5.020 <0.001 ***
## D - B == 0 0.03157 0.01041 3.032 0.0899 .
## E - B == 0 0.10960 0.01041 10.526 <0.001 ***
## F - B == 0 0.35689 0.01041 34.277 <0.001 ***
## G - B == 0 0.27441 0.01041 26.356 <0.001 ***
## H - B == 0 0.28297 0.01041 27.178 <0.001 ***
## D - C == 0 0.08384 0.01041 8.052 <0.001 ***
## E - C == 0 0.16187 0.01041 15.546 <0.001 ***
## F - C == 0 0.40916 0.01041 39.297 <0.001 ***
## G - C == 0 0.32668 0.01041 31.376 <0.001 ***
## H - C == 0 0.33524 0.01041 32.198 <0.001 ***
## E - D == 0 0.07803 0.01041 7.494 <0.001 ***
## F - D == 0 0.32532 0.01041 31.245 <0.001 ***
## G - D == 0 0.24284 0.01041 23.324 <0.001 ***
## H - D == 0 0.25140 0.01041 24.146 <0.001 ***
## F - E == 0 0.24729 0.01041 23.751 <0.001 ***
## G - E == 0 0.16481 0.01041 15.829 <0.001 ***
## H - E == 0 0.17337 0.01041 16.651 <0.001 ***
## G - F == 0 -0.08248 0.01041 -7.922 <0.001 ***
## H - F == 0 -0.07392 0.01041 -7.100 <0.001 ***
## H - G == 0 0.00856 0.01041 0.822 0.9900
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Skeleton"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -36.6262 0.8569 -42.74 <1e-09 ***
## C - A == 0 -40.4684 0.8569 -47.23 <1e-09 ***
## D - A == 0 -32.9109 0.8569 -38.41 <1e-09 ***
## E - A == 0 -26.8994 0.8569 -31.39 <1e-09 ***
## F - A == 0 -9.0900 0.8569 -10.61 <1e-09 ***
## G - A == 0 -11.3471 0.8569 -13.24 <1e-09 ***
## H - A == 0 -12.1295 0.8569 -14.15 <1e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Skeleton"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -36.6262 0.8569 -42.743 < 0.001 ***
## C - A == 0 -40.4684 0.8569 -47.227 < 0.001 ***
## D - A == 0 -32.9109 0.8569 -38.407 < 0.001 ***
## E - A == 0 -26.8994 0.8569 -31.391 < 0.001 ***
## F - A == 0 -9.0900 0.8569 -10.608 < 0.001 ***
## G - A == 0 -11.3471 0.8569 -13.242 < 0.001 ***
## H - A == 0 -12.1295 0.8569 -14.155 < 0.001 ***
## C - B == 0 -3.8422 0.8569 -4.484 0.00340 **
## D - B == 0 3.7153 0.8569 4.336 0.00461 **
## E - B == 0 9.7269 0.8569 11.351 < 0.001 ***
## F - B == 0 27.5362 0.8569 32.135 < 0.001 ***
## G - B == 0 25.2791 0.8569 29.501 < 0.001 ***
## H - B == 0 24.4967 0.8569 28.588 < 0.001 ***
## D - C == 0 7.5575 0.8569 8.820 < 0.001 ***
## E - C == 0 13.5691 0.8569 15.835 < 0.001 ***
## F - C == 0 31.3784 0.8569 36.618 < 0.001 ***
## G - C == 0 29.1213 0.8569 33.984 < 0.001 ***
## H - C == 0 28.3389 0.8569 33.071 < 0.001 ***
## E - D == 0 6.0116 0.8569 7.015 < 0.001 ***
## F - D == 0 23.8209 0.8569 27.799 < 0.001 ***
## G - D == 0 21.5638 0.8569 25.165 < 0.001 ***
## H - D == 0 20.7814 0.8569 24.252 < 0.001 ***
## F - E == 0 17.8093 0.8569 20.783 < 0.001 ***
## G - E == 0 15.5522 0.8569 18.149 < 0.001 ***
## H - E == 0 14.7699 0.8569 17.236 < 0.001 ***
## G - F == 0 -2.2571 0.8569 -2.634 0.19194
## H - F == 0 -3.0395 0.8569 -3.547 0.02949 *
## H - G == 0 -0.7824 0.8569 -0.913 0.98176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Liver"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -9.9321 0.5572 -17.83 <1e-06 ***
## C - A == 0 -10.3855 0.5572 -18.64 <1e-06 ***
## D - A == 0 -9.7268 0.5572 -17.46 <1e-06 ***
## E - A == 0 -9.9519 0.5572 -17.86 <1e-06 ***
## F - A == 0 -4.1845 0.5572 -7.51 <1e-06 ***
## G - A == 0 -9.6982 0.5572 -17.41 <1e-06 ***
## H - A == 0 -8.8268 0.5572 -15.84 <1e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Liver"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -9.93210 0.55716 -17.826 <0.001 ***
## C - A == 0 -10.38546 0.55716 -18.640 <0.001 ***
## D - A == 0 -9.72676 0.55716 -17.458 <0.001 ***
## E - A == 0 -9.95192 0.55716 -17.862 <0.001 ***
## F - A == 0 -4.18445 0.55716 -7.510 <0.001 ***
## G - A == 0 -9.69824 0.55716 -17.407 <0.001 ***
## H - A == 0 -8.82680 0.55716 -15.842 <0.001 ***
## C - B == 0 -0.45336 0.55716 -0.814 0.991
## D - B == 0 0.20533 0.55716 0.369 1.000
## E - B == 0 -0.01982 0.55716 -0.036 1.000
## F - B == 0 5.74764 0.55716 10.316 <0.001 ***
## G - B == 0 0.23386 0.55716 0.420 1.000
## H - B == 0 1.10530 0.55716 1.984 0.512
## D - C == 0 0.65870 0.55716 1.182 0.929
## E - C == 0 0.43354 0.55716 0.778 0.993
## F - C == 0 6.20100 0.55716 11.130 <0.001 ***
## G - C == 0 0.68722 0.55716 1.233 0.914
## H - C == 0 1.55866 0.55716 2.797 0.142
## E - D == 0 -0.22516 0.55716 -0.404 1.000
## F - D == 0 5.54231 0.55716 9.947 <0.001 ***
## G - D == 0 0.02852 0.55716 0.051 1.000
## H - D == 0 0.89996 0.55716 1.615 0.737
## F - E == 0 5.76747 0.55716 10.352 <0.001 ***
## G - E == 0 0.25368 0.55716 0.455 1.000
## H - E == 0 1.12512 0.55716 2.019 0.490
## G - F == 0 -5.51379 0.55716 -9.896 <0.001 ***
## H - F == 0 -4.64235 0.55716 -8.332 <0.001 ***
## H - G == 0 0.87144 0.55716 1.564 0.766
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Soft"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -2.7816 0.1688 -16.480 <1e-04 ***
## C - A == 0 -3.1287 0.1688 -18.536 <1e-04 ***
## D - A == 0 -3.0822 0.1688 -18.260 <1e-04 ***
## E - A == 0 -1.8295 0.1688 -10.839 <1e-04 ***
## F - A == 0 -1.1208 0.1688 -6.640 <1e-04 ***
## G - A == 0 -1.2886 0.1688 -7.634 <1e-04 ***
## H - A == 0 -1.1020 0.1688 -6.529 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Soft"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -2.78163 0.16879 -16.480 < 0.001 ***
## C - A == 0 -3.12872 0.16879 -18.536 < 0.001 ***
## D - A == 0 -3.08219 0.16879 -18.260 < 0.001 ***
## E - A == 0 -1.82949 0.16879 -10.839 < 0.001 ***
## F - A == 0 -1.12082 0.16879 -6.640 < 0.001 ***
## G - A == 0 -1.28858 0.16879 -7.634 < 0.001 ***
## H - A == 0 -1.10204 0.16879 -6.529 < 0.001 ***
## C - B == 0 -0.34709 0.16879 -2.056 0.46809
## D - B == 0 -0.30056 0.16879 -1.781 0.63795
## E - B == 0 0.95214 0.16879 5.641 < 0.001 ***
## F - B == 0 1.66081 0.16879 9.839 < 0.001 ***
## G - B == 0 1.49305 0.16879 8.846 < 0.001 ***
## H - B == 0 1.67959 0.16879 9.951 < 0.001 ***
## D - C == 0 0.04653 0.16879 0.276 0.99999
## E - C == 0 1.29924 0.16879 7.697 < 0.001 ***
## F - C == 0 2.00791 0.16879 11.896 < 0.001 ***
## G - C == 0 1.84015 0.16879 10.902 < 0.001 ***
## H - C == 0 2.02668 0.16879 12.007 < 0.001 ***
## E - D == 0 1.25270 0.16879 7.422 < 0.001 ***
## F - D == 0 1.96137 0.16879 11.620 < 0.001 ***
## G - D == 0 1.79362 0.16879 10.626 < 0.001 ***
## H - D == 0 1.98015 0.16879 11.731 < 0.001 ***
## F - E == 0 0.70867 0.16879 4.199 0.00652 **
## G - E == 0 0.54091 0.16879 3.205 0.06267 .
## H - E == 0 0.72744 0.16879 4.310 0.00508 **
## G - F == 0 -0.16776 0.16879 -0.994 0.97097
## H - F == 0 0.01878 0.16879 0.111 1.00000
## H - G == 0 0.18653 0.16879 1.105 0.94944
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "ART"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.70203 0.07987 -8.790 < 0.001 ***
## C - A == 0 -0.91150 0.07987 -11.412 < 0.001 ***
## D - A == 0 -0.83144 0.07987 -10.410 < 0.001 ***
## E - A == 0 -0.54448 0.07987 -6.817 < 0.001 ***
## F - A == 0 -0.11737 0.07987 -1.469 0.54288
## G - A == 0 -0.34302 0.07987 -4.295 0.00151 **
## H - A == 0 0.04727 0.07987 0.592 0.98731
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "ART"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.70203 0.07987 -8.790 <0.01 ***
## C - A == 0 -0.91150 0.07987 -11.412 <0.01 ***
## D - A == 0 -0.83144 0.07987 -10.410 <0.01 ***
## E - A == 0 -0.54448 0.07987 -6.817 <0.01 ***
## F - A == 0 -0.11737 0.07987 -1.469 0.8155
## G - A == 0 -0.34302 0.07987 -4.295 <0.01 **
## H - A == 0 0.04727 0.07987 0.592 0.9987
## C - B == 0 -0.20947 0.07987 -2.623 0.1963
## D - B == 0 -0.12941 0.07987 -1.620 0.7341
## E - B == 0 0.15755 0.07987 1.973 0.5191
## F - B == 0 0.58466 0.07987 7.320 <0.01 ***
## G - B == 0 0.35901 0.07987 4.495 <0.01 **
## H - B == 0 0.74930 0.07987 9.382 <0.01 ***
## D - C == 0 0.08006 0.07987 1.002 0.9695
## E - C == 0 0.36702 0.07987 4.595 <0.01 **
## F - C == 0 0.79414 0.07987 9.943 <0.01 ***
## G - C == 0 0.56848 0.07987 7.118 <0.01 ***
## H - C == 0 0.95877 0.07987 12.004 <0.01 ***
## E - D == 0 0.28696 0.07987 3.593 0.0269 *
## F - D == 0 0.71407 0.07987 8.941 <0.01 ***
## G - D == 0 0.48842 0.07987 6.115 <0.01 ***
## H - D == 0 0.87871 0.07987 11.002 <0.01 ***
## F - E == 0 0.42711 0.07987 5.348 <0.01 ***
## G - E == 0 0.20146 0.07987 2.522 0.2337
## H - E == 0 0.59175 0.07987 7.409 <0.01 ***
## G - F == 0 -0.22565 0.07987 -2.825 0.1350
## H - F == 0 0.16464 0.07987 2.061 0.4654
## H - G == 0 0.39029 0.07987 4.887 <0.01 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Kidneys"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.25286 0.07672 -3.296 0.0170 *
## C - A == 0 -0.56633 0.07672 -7.382 <0.001 ***
## D - A == 0 -0.54548 0.07672 -7.110 <0.001 ***
## E - A == 0 -0.47864 0.07672 -6.239 <0.001 ***
## F - A == 0 -0.36935 0.07672 -4.814 <0.001 ***
## G - A == 0 -0.44282 0.07672 -5.772 <0.001 ***
## H - A == 0 -0.23009 0.07672 -2.999 0.0332 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Kidneys"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.25286 0.07672 -3.296 0.05167 .
## C - A == 0 -0.56633 0.07672 -7.382 < 0.001 ***
## D - A == 0 -0.54548 0.07672 -7.110 < 0.001 ***
## E - A == 0 -0.47864 0.07672 -6.239 < 0.001 ***
## F - A == 0 -0.36935 0.07672 -4.814 0.00148 **
## G - A == 0 -0.44282 0.07672 -5.772 < 0.001 ***
## H - A == 0 -0.23009 0.07672 -2.999 0.09580 .
## C - B == 0 -0.31347 0.07672 -4.086 0.00848 **
## D - B == 0 -0.29262 0.07672 -3.814 0.01621 *
## E - B == 0 -0.22578 0.07672 -2.943 0.10736
## F - B == 0 -0.11649 0.07672 -1.518 0.79046
## G - B == 0 -0.18996 0.07672 -2.476 0.25245
## H - B == 0 0.02277 0.07672 0.297 0.99999
## D - C == 0 0.02085 0.07672 0.272 0.99999
## E - C == 0 0.08769 0.07672 1.143 0.94013
## F - C == 0 0.19698 0.07672 2.568 0.21558
## G - C == 0 0.12351 0.07672 1.610 0.74012
## H - C == 0 0.33624 0.07672 4.383 0.00423 **
## E - D == 0 0.06684 0.07672 0.871 0.98604
## F - D == 0 0.17613 0.07672 2.296 0.33585
## G - D == 0 0.10266 0.07672 1.338 0.87506
## H - D == 0 0.31539 0.07672 4.111 0.00807 **
## F - E == 0 0.10929 0.07672 1.425 0.83714
## G - E == 0 0.03582 0.07672 0.467 0.99971
## H - E == 0 0.24855 0.07672 3.240 0.05837 .
## G - F == 0 -0.07347 0.07672 -0.958 0.97627
## H - F == 0 0.13926 0.07672 1.815 0.61653
## H - G == 0 0.21273 0.07672 2.773 0.14896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Heart"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.07904 0.02185 -3.617 0.00807 **
## C - A == 0 -0.09172 0.02185 -4.198 0.00194 **
## D - A == 0 -0.08635 0.02185 -3.952 0.00346 **
## E - A == 0 0.25310 0.02185 11.584 < 0.001 ***
## F - A == 0 0.10313 0.02185 4.720 < 0.001 ***
## G - A == 0 0.12090 0.02185 5.533 < 0.001 ***
## H - A == 0 0.06052 0.02185 2.770 0.05494 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Heart"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.079038 0.021850 -3.617 0.02534 *
## C - A == 0 -0.091720 0.021850 -4.198 0.00634 **
## D - A == 0 -0.086345 0.021850 -3.952 0.01184 *
## E - A == 0 0.253104 0.021850 11.584 < 0.001 ***
## F - A == 0 0.103129 0.021850 4.720 0.00187 **
## G - A == 0 0.120898 0.021850 5.533 < 0.001 ***
## H - A == 0 0.060521 0.021850 2.770 0.15038
## C - B == 0 -0.012681 0.021850 -0.580 0.99882
## D - B == 0 -0.007307 0.021850 -0.334 0.99997
## E - B == 0 0.332142 0.021850 15.201 < 0.001 ***
## F - B == 0 0.182168 0.021850 8.337 < 0.001 ***
## G - B == 0 0.199936 0.021850 9.150 < 0.001 ***
## H - B == 0 0.139559 0.021850 6.387 < 0.001 ***
## D - C == 0 0.005374 0.021850 0.246 1.00000
## E - C == 0 0.344823 0.021850 15.781 < 0.001 ***
## F - C == 0 0.194849 0.021850 8.917 < 0.001 ***
## G - C == 0 0.212617 0.021850 9.731 < 0.001 ***
## H - C == 0 0.152240 0.021850 6.967 < 0.001 ***
## E - D == 0 0.339449 0.021850 15.535 < 0.001 ***
## F - D == 0 0.189475 0.021850 8.672 < 0.001 ***
## G - D == 0 0.207243 0.021850 9.485 < 0.001 ***
## H - D == 0 0.146866 0.021850 6.722 < 0.001 ***
## F - E == 0 -0.149975 0.021850 -6.864 < 0.001 ***
## G - E == 0 -0.132206 0.021850 -6.051 < 0.001 ***
## H - E == 0 -0.192583 0.021850 -8.814 < 0.001 ***
## G - F == 0 0.017768 0.021850 0.813 0.99063
## H - F == 0 -0.042608 0.021850 -1.950 0.53280
## H - G == 0 -0.060377 0.021850 -2.763 0.15178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Lungs"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.13676 0.01553 -8.807 < 1e-04 ***
## C - A == 0 -0.16687 0.01553 -10.745 < 1e-04 ***
## D - A == 0 -0.14544 0.01553 -9.366 < 1e-04 ***
## E - A == 0 -0.12816 0.01553 -8.253 < 1e-04 ***
## F - A == 0 -0.09223 0.01553 -5.939 < 1e-04 ***
## G - A == 0 -0.08481 0.01553 -5.461 < 1e-04 ***
## H - A == 0 -0.07176 0.01553 -4.621 0.000659 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Lungs"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.136763 0.015529 -8.807 <0.01 ***
## C - A == 0 -0.166865 0.015529 -10.745 <0.01 ***
## D - A == 0 -0.145439 0.015529 -9.366 <0.01 ***
## E - A == 0 -0.128161 0.015529 -8.253 <0.01 ***
## F - A == 0 -0.092234 0.015529 -5.939 <0.01 ***
## G - A == 0 -0.084809 0.015529 -5.461 <0.01 ***
## H - A == 0 -0.071755 0.015529 -4.621 <0.01 **
## C - B == 0 -0.030102 0.015529 -1.938 0.5402
## D - B == 0 -0.008676 0.015529 -0.559 0.9991
## E - B == 0 0.008602 0.015529 0.554 0.9991
## F - B == 0 0.044530 0.015529 2.868 0.1248
## G - B == 0 0.051954 0.015529 3.346 0.0462 *
## H - B == 0 0.065008 0.015529 4.186 <0.01 **
## D - C == 0 0.021426 0.015529 1.380 0.8573
## E - C == 0 0.038704 0.015529 2.492 0.2449
## F - C == 0 0.074632 0.015529 4.806 <0.01 **
## G - C == 0 0.082056 0.015529 5.284 <0.01 ***
## H - C == 0 0.095110 0.015529 6.125 <0.01 ***
## E - D == 0 0.017278 0.015529 1.113 0.9476
## F - D == 0 0.053205 0.015529 3.426 0.0392 *
## G - D == 0 0.060630 0.015529 3.904 0.0132 *
## H - D == 0 0.073684 0.015529 4.745 <0.01 **
## F - E == 0 0.035928 0.015529 2.314 0.3264
## G - E == 0 0.043352 0.015529 2.792 0.1443
## H - E == 0 0.056406 0.015529 3.632 0.0244 *
## G - F == 0 0.007424 0.015529 0.478 0.9997
## H - F == 0 0.020478 0.015529 1.319 0.8828
## H - G == 0 0.013054 0.015529 0.841 0.9886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Spleen"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.06413 0.01024 -6.262 <0.001 ***
## C - A == 0 -0.07321 0.01024 -7.149 <0.001 ***
## D - A == 0 -0.07480 0.01024 -7.304 <0.001 ***
## E - A == 0 -0.03332 0.01024 -3.254 0.0186 *
## F - A == 0 -0.02152 0.01024 -2.101 0.2054
## G - A == 0 -0.02696 0.01024 -2.632 0.0734 .
## H - A == 0 -0.02846 0.01024 -2.779 0.0539 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Spleen"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.064132 0.010241 -6.262 < 0.001 ***
## C - A == 0 -0.073213 0.010241 -7.149 < 0.001 ***
## D - A == 0 -0.074802 0.010241 -7.304 < 0.001 ***
## E - A == 0 -0.033323 0.010241 -3.254 0.05688 .
## F - A == 0 -0.021519 0.010241 -2.101 0.44159
## G - A == 0 -0.026959 0.010241 -2.632 0.19257
## H - A == 0 -0.028459 0.010241 -2.779 0.14739
## C - B == 0 -0.009081 0.010241 -0.887 0.98455
## D - B == 0 -0.010669 0.010241 -1.042 0.96266
## E - B == 0 0.030809 0.010241 3.008 0.09493 .
## F - B == 0 0.042614 0.010241 4.161 0.00724 **
## G - B == 0 0.037173 0.010241 3.630 0.02465 *
## H - B == 0 0.035673 0.010241 3.483 0.03435 *
## D - C == 0 -0.001588 0.010241 -0.155 1.00000
## E - C == 0 0.039890 0.010241 3.895 0.01338 *
## F - C == 0 0.051695 0.010241 5.048 < 0.001 ***
## G - C == 0 0.046254 0.010241 4.517 0.00307 **
## H - C == 0 0.044754 0.010241 4.370 0.00424 **
## E - D == 0 0.041478 0.010241 4.050 0.00934 **
## F - D == 0 0.053283 0.010241 5.203 < 0.001 ***
## G - D == 0 0.047842 0.010241 4.672 0.00213 **
## H - D == 0 0.046342 0.010241 4.525 0.00307 **
## F - E == 0 0.011805 0.010241 1.153 0.93763
## G - E == 0 0.006364 0.010241 0.621 0.99819
## H - E == 0 0.004864 0.010241 0.475 0.99968
## G - F == 0 -0.005441 0.010241 -0.531 0.99934
## H - F == 0 -0.006941 0.010241 -0.678 0.99688
## H - G == 0 -0.001500 0.010241 -0.146 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Brain"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.06630 0.02855 -2.323 0.1364
## C - A == 0 -0.08013 0.02855 -2.807 0.0509 .
## D - A == 0 -0.08809 0.02855 -3.086 0.0275 *
## E - A == 0 -0.08468 0.02855 -2.966 0.0356 *
## F - A == 0 -0.07671 0.02855 -2.687 0.0655 .
## G - A == 0 -0.09205 0.02855 -3.224 0.0201 *
## H - A == 0 -0.08987 0.02855 -3.148 0.0238 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Brain"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.066303 0.028547 -2.323 0.3221
## C - A == 0 -0.080134 0.028547 -2.807 0.1397
## D - A == 0 -0.088093 0.028547 -3.086 0.0807 .
## E - A == 0 -0.084683 0.028547 -2.966 0.1027
## F - A == 0 -0.076708 0.028547 -2.687 0.1746
## G - A == 0 -0.092047 0.028547 -3.224 0.0599 .
## H - A == 0 -0.089869 0.028547 -3.148 0.0704 .
## C - B == 0 -0.013832 0.028547 -0.485 0.9996
## D - B == 0 -0.021790 0.028547 -0.763 0.9936
## E - B == 0 -0.018380 0.028547 -0.644 0.9977
## F - B == 0 -0.010405 0.028547 -0.364 0.9999
## G - B == 0 -0.025745 0.028547 -0.902 0.9830
## H - B == 0 -0.023566 0.028547 -0.826 0.9898
## D - C == 0 -0.007959 0.028547 -0.279 1.0000
## E - C == 0 -0.004548 0.028547 -0.159 1.0000
## F - C == 0 0.003427 0.028547 0.120 1.0000
## G - C == 0 -0.011913 0.028547 -0.417 0.9999
## H - C == 0 -0.009734 0.028547 -0.341 1.0000
## E - D == 0 0.003410 0.028547 0.119 1.0000
## F - D == 0 0.011385 0.028547 0.399 0.9999
## G - D == 0 -0.003954 0.028547 -0.139 1.0000
## H - D == 0 -0.001776 0.028547 -0.062 1.0000
## F - E == 0 0.007975 0.028547 0.279 1.0000
## G - E == 0 -0.007364 0.028547 -0.258 1.0000
## H - E == 0 -0.005186 0.028547 -0.182 1.0000
## G - F == 0 -0.015339 0.028547 -0.537 0.9993
## H - F == 0 -0.013161 0.028547 -0.461 0.9997
## H - G == 0 0.002178 0.028547 0.076 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Dunnett Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Thymus"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.031838 0.008268 -3.851 0.00452 **
## C - A == 0 -0.027335 0.008268 -3.306 0.01655 *
## D - A == 0 -0.024683 0.008268 -2.985 0.03451 *
## E - A == 0 -0.016000 0.008268 -1.935 0.27391
## F - A == 0 -0.014417 0.008268 -1.744 0.37137
## G - A == 0 -0.029110 0.008268 -3.521 0.00999 **
## H - A == 0 -0.005103 0.008268 -0.617 0.98409
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: aov(formula = value ~ Group, data = mdata[which(mdata$variable ==
## "Thymus"), ])
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## B - A == 0 -0.031838 0.008268 -3.851 0.0149 *
## C - A == 0 -0.027335 0.008268 -3.306 0.0503 .
## D - A == 0 -0.024683 0.008268 -2.985 0.0988 .
## E - A == 0 -0.016000 0.008268 -1.935 0.5419
## F - A == 0 -0.014417 0.008268 -1.744 0.6610
## G - A == 0 -0.029110 0.008268 -3.521 0.0318 *
## H - A == 0 -0.005103 0.008268 -0.617 0.9983
## C - B == 0 0.004503 0.008268 0.545 0.9992
## D - B == 0 0.007155 0.008268 0.865 0.9865
## E - B == 0 0.015839 0.008268 1.916 0.5536
## F - B == 0 0.017422 0.008268 2.107 0.4386
## G - B == 0 0.002729 0.008268 0.330 1.0000
## H - B == 0 0.026735 0.008268 3.233 0.0594 .
## D - C == 0 0.002652 0.008268 0.321 1.0000
## E - C == 0 0.011335 0.008268 1.371 0.8612
## F - C == 0 0.012918 0.008268 1.562 0.7667
## G - C == 0 -0.001775 0.008268 -0.215 1.0000
## H - C == 0 0.022232 0.008268 2.689 0.1744
## E - D == 0 0.008684 0.008268 1.050 0.9611
## F - D == 0 0.010267 0.008268 1.242 0.9108
## G - D == 0 -0.004427 0.008268 -0.535 0.9993
## H - D == 0 0.019580 0.008268 2.368 0.3003
## F - E == 0 0.001583 0.008268 0.191 1.0000
## G - E == 0 -0.013110 0.008268 -1.586 0.7539
## H - E == 0 0.010897 0.008268 1.318 0.8831
## G - F == 0 -0.014693 0.008268 -1.777 0.6402
## H - F == 0 0.009314 0.008268 1.126 0.9443
## H - G == 0 0.024007 0.008268 2.903 0.1161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)