Hi,
Thanks a lot for this amazing package and documentation. I am trying to account for departure from normality and variance homogeneity when fitting a Weibull Type II model on several dose response curves by using a boxcox transformation as shown in the Ritz,. 2012 paper.
The function is able to run and seems to be effective based on diagnostic plots before/after transformation. However, a warning message is sent :
Error in optim(startVec, opfct, hessian = TRUE, method = "L-BFGS-B", lower = lowerLimits, : L-BFGS-B needs finite values of 'fn'
Lambda optimal value seems to be always λ=2 independently of the data, which I suspect comes from this error.
I must precise that I don't have true replicates for any of the concentration and a relatively low number of points (12) per curve. However I have a large number of curves, each of them representing a single individual (60).
Here is my code :
Chicken.W2.3<-drm(mFvFm~meanTemp_hold.adj, data=df_filt_info %>% subset(Site.name=="Chicken"), curveid = Genotype, fct=W2.3(names = c('hill', 'max', 'ed50')),upperl=c(NA,0.9,40),lowerl=c(NA,0.1,30)) Chicken.W2.3.bc=boxcox(Chicken.W2.3,lambda = seq(-2,4,by=0.25))
summary(Chicken.W2.3)
Model fitted: Weibull (type 2) with lower limit at 0 (3 parms)
Non-normality/heterogeneity adjustment through optimal Box-Cox transformation
Estimated lambda: 2
Confidence interval for lambda: [1.79,2.06]`
Running a quick anova test shows that the correction doesn't significantly affect the results :
`Chicken.W2.3.sum=broom::tidy(Chicken.W2.3) %>% mutate(Model="W2.3")
aov=aov(estimate~Model,data=Boxcox.comp %>% subset(term=="ed50"))
summary(aov)
Df Sum Sq Mean Sq F value Pr(>F)
Model 1 0.05 0.05203 0.167 0.683
Residuals 118 36.68 0.31087
TukeyHSD(aov)
Tukey multiple comparisons of means
95% family-wise confidence level
However I am neither sure if I can trust those results, nor where the issue may come from. Any clarification or guideline would be greatly appreciated.
Hi, Thanks a lot for this amazing package and documentation. I am trying to account for departure from normality and variance homogeneity when fitting a Weibull Type II model on several dose response curves by using a boxcox transformation as shown in the Ritz,. 2012 paper.
The function is able to run and seems to be effective based on diagnostic plots before/after transformation. However, a warning message is sent :
Error in optim(startVec, opfct, hessian = TRUE, method = "L-BFGS-B", lower = lowerLimits, : L-BFGS-B needs finite values of 'fn'Lambda optimal value seems to be always λ=2 independently of the data, which I suspect comes from this error.
I must precise that I don't have true replicates for any of the concentration and a relatively low number of points (12) per curve. However I have a large number of curves, each of them representing a single individual (60).
Here is my code :
Chicken.W2.3<-drm(mFvFm~meanTemp_hold.adj, data=df_filt_info %>% subset(Site.name=="Chicken"), curveid = Genotype, fct=W2.3(names = c('hill', 'max', 'ed50')),upperl=c(NA,0.9,40),lowerl=c(NA,0.1,30)) Chicken.W2.3.bc=boxcox(Chicken.W2.3,lambda = seq(-2,4,by=0.25))Model fitted: Weibull (type 2) with lower limit at 0 (3 parms)
Parameter estimates:
hill:1523 -17.564453 8.518489 -2.0619 0.0397087 *
hill:1512 -14.731103 3.958415 -3.7215 0.0002195 hill:798 -23.705428 6.701404 -3.5374 0.0004401 hill:1528 -8.971746 3.034226 -2.9568 0.0032484 hill:799 -23.073299 6.929115 -3.3299 0.0009300 ** hill:1515 -15.931781 3.649070 -4.3660 1.526e-05 hill:1514 -17.277075 4.086176 -4.2282 2.781e-05 hill:1522 -23.529915 5.373922 -4.3785 1.444e-05 hill:1519 -20.088171 4.075313 -4.9292 1.110e-06 hill:1518 -22.169927 4.768053 -4.6497 4.215e-06 hill:797 -18.115977 5.179171 -3.4979 0.0005090 hill:806 -17.210004 4.745791 -3.6264 0.0003156 hill:1501 -30.566419 19.925163 -1.5341 0.1256193
hill:1504 -21.126029 6.659423 -3.1724 0.0016009 hill:1507 -10.237252 2.392683 -4.2786 2.237e-05 ** hill:1516 -20.369981 4.453983 -4.5734 5.997e-06 hill:999 -59.517291 35.048855 -1.6981 0.0900792 .
hill:1534 -15.499087 4.086902 -3.7924 0.0001666 hill:997 -21.419227 5.390718 -3.9734 8.082e-05 hill:800 -27.293125 8.801219 -3.1011 0.0020323 hill:993 -20.543757 4.791741 -4.2873 2.154e-05 hill:802 -17.355629 5.669505 -3.0612 0.0023176 hill:1533 -20.677138 4.952235 -4.1753 3.486e-05 hill:1513 -20.923588 4.677666 -4.4731 9.465e-06 hill:991 -15.354623 4.614543 -3.3274 0.0009381 hill:995 -19.330011 5.762926 -3.3542 0.0008537 hill:1524 -30.936977 24.182125 -1.2793 0.2013473
hill:1526 -48.825133 41.920247 -1.1647 0.2446654
hill:1535 -15.310950 4.250495 -3.6022 0.0003457 hill:998 -17.804519 5.723226 -3.1109 0.0019668 hill:1532 -12.065920 3.886250 -3.1048 0.0020074 hill:956 -20.427318 8.558752 -2.3867 0.0173540 *
hill:1517 -51.248455 42.147868 -1.2159 0.2245638
hill:1531 -12.346202 3.412301 -3.6181 0.0003255 hill:1505 -15.052659 3.908384 -3.8514 0.0001320 hill:803 -20.653777 6.034773 -3.4225 0.0006692 hill:990 -57.583740 37.909734 -1.5190 0.1293744
hill:805 -17.536826 4.016871 -4.3658 1.527e-05 hill:804 -15.950507 3.851578 -4.1413 4.026e-05 hill:987 -18.463429 5.053799 -3.6534 0.0002849 hill:1502 -35.946270 41.788151 -0.8602 0.3900716
hill:1527 -9.863548 3.534892 -2.7903 0.0054575 hill:1503 -22.521281 4.943469 -4.5558 6.502e-06 ** hill:1510 -17.918111 5.264542 -3.4035 0.0007162 hill:1511 -12.527183 4.284945 -2.9235 0.0036108 hill:1530 -20.367959 5.365450 -3.7961 0.0001642 ** hill:992 -20.823543 4.648621 -4.4795 9.194e-06 hill:1529 -12.732994 3.307765 -3.8494 0.0001330 hill:988 -61.138689 35.448117 -1.7247 0.0851656 .
hill:994 -20.086773 5.574165 -3.6035 0.0003439 hill:1520 -30.072543 11.842742 -2.5393 0.0113952 *
hill:1525 -16.548085 4.625996 -3.5772 0.0003796 hill:1000 -21.578482 4.307600 -5.0094 7.480e-07 hill:1508 -8.527051 2.031115 -4.1982 3.162e-05 hill:996 -86.616349 33.939286 -2.5521 0.0109908
hill:989 -19.892267 5.844699 -3.4035 0.0007164 hill:1521 -31.475207 10.293496 -3.0578 0.0023440 hill:801 -22.219104 5.236290 -4.2433 2.606e-05 hill:1509 -19.056915 3.935031 -4.8429 1.688e-06 hill:1506 -14.260804 3.322402 -4.2923 2.107e-05 max:1523 0.499006 0.024754 20.1584 < 2.2e-16 max:1512 0.561418 0.026245 21.3912 < 2.2e-16 max:798 0.605336 0.021523 28.1256 < 2.2e-16 max:1528 0.576645 0.038080 15.1431 < 2.2e-16 max:799 0.639159 0.022560 28.3315 < 2.2e-16 max:1515 0.592453 0.025855 22.9145 < 2.2e-16 max:1514 0.609307 0.025090 24.2846 < 2.2e-16 max:1522 0.581084 0.025574 22.7212 < 2.2e-16 max:1519 0.587608 0.025903 22.6853 < 2.2e-16 max:1518 0.535929 0.025752 20.8113 < 2.2e-16 max:797 0.614098 0.024953 24.6099 < 2.2e-16 max:806 0.611887 0.026259 23.3018 < 2.2e-16 max:1501 0.589201 0.026793 21.9909 < 2.2e-16 max:1504 0.555444 0.023178 23.9646 < 2.2e-16 max:1507 0.572616 0.031457 18.2030 < 2.2e-16 max:1516 0.586710 0.025607 22.9123 < 2.2e-16 max:999 0.618902 0.020989 29.4864 < 2.2e-16 max:1534 0.612380 0.025003 24.4919 < 2.2e-16 max:997 0.573723 0.026441 21.6980 < 2.2e-16 max:800 0.590666 0.021815 27.0767 < 2.2e-16 max:993 0.583774 0.024503 23.8248 < 2.2e-16 max:802 0.592841 0.026209 22.6197 < 2.2e-16 max:1533 0.559007 0.025346 22.0549 < 2.2e-16 max:1513 0.561435 0.025413 22.0921 < 2.2e-16 max:991 0.603136 0.029318 20.5720 < 2.2e-16 max:995 0.618217 0.023429 26.3874 < 2.2e-16 max:1524 0.607571 0.021017 28.9082 < 2.2e-16 max:1526 0.519936 0.020997 24.7621 < 2.2e-16 max:1535 0.569511 0.025464 22.3657 < 2.2e-16 max:998 0.600423 0.026971 22.2619 < 2.2e-16 max:1532 0.588578 0.025544 23.0413 < 2.2e-16 max:956 0.599484 0.024232 24.7396 < 2.2e-16 max:1517 0.556664 0.020991 26.5196 < 2.2e-16 max:1531 0.584390 0.028500 20.5051 < 2.2e-16 max:1505 0.561308 0.025656 21.8781 < 2.2e-16 max:803 0.580360 0.025076 23.1440 < 2.2e-16 max:990 0.602285 0.022267 27.0485 < 2.2e-16 max:805 0.633366 0.025993 24.3671 < 2.2e-16 max:804 0.636555 0.026074 24.4132 < 2.2e-16 max:987 0.638252 0.026476 24.1065 < 2.2e-16 max:1502 0.577257 0.021008 27.4777 < 2.2e-16 max:1527 0.502192 0.034127 14.7155 < 2.2e-16 max:1503 0.634007 0.025702 24.6675 < 2.2e-16 max:1510 0.580406 0.025521 22.7427 < 2.2e-16 max:1511 0.512531 0.026752 19.1584 < 2.2e-16 max:1530 0.574783 0.023204 24.7707 < 2.2e-16 max:992 0.573273 0.025904 22.1307 < 2.2e-16 max:1529 0.598324 0.028260 21.1721 < 2.2e-16 max:988 0.594199 0.022237 26.7208 < 2.2e-16 max:994 0.617232 0.024698 24.9911 < 2.2e-16 max:1520 0.566154 0.022246 25.4492 < 2.2e-16 max:1525 0.560179 0.026400 21.2190 < 2.2e-16 max:1000 0.625044 0.025416 24.5926 < 2.2e-16 max:1508 0.638145 0.052030 12.2651 < 2.2e-16 max:996 0.618035 0.020953 29.4968 < 2.2e-16 max:989 0.600721 0.027952 21.4909 < 2.2e-16 max:1521 0.510128 0.027251 18.7197 < 2.2e-16 max:801 0.548662 0.025623 21.4125 < 2.2e-16 max:1509 0.593231 0.025698 23.0846 < 2.2e-16 max:1506 0.571380 0.026728 21.3775 < 2.2e-16 ed50:1523 36.767942 0.578049 63.6070 < 2.2e-16 ed50:1512 35.741915 0.445046 80.3106 < 2.2e-16 ed50:798 36.580575 0.306711 119.2671 < 2.2e-16 ed50:1528 35.521479 0.748981 47.4264 < 2.2e-16 ed50:799 36.418127 0.341971 106.4947 < 2.2e-16 ed50:1515 35.530633 0.388217 91.5227 < 2.2e-16 ed50:1514 36.061960 0.380698 94.7259 < 2.2e-16 ed50:1522 35.565832 0.379659 93.6783 < 2.2e-16 ed50:1519 35.336718 0.349982 100.9673 < 2.2e-16 ed50:1518 35.193287 0.351285 100.1845 < 2.2e-16 ed50:797 36.260647 0.408771 88.7066 < 2.2e-16 ed50:806 36.216726 0.427410 84.7353 < 2.2e-16 ed50:1501 35.984544 0.896987 40.1171 < 2.2e-16 ed50:1504 36.398551 0.392581 92.7161 < 2.2e-16 ed50:1507 35.081923 0.579652 60.5224 < 2.2e-16 ed50:1516 35.706923 0.374473 95.3524 < 2.2e-16 ed50:999 36.881811 0.254116 145.1376 < 2.2e-16 ed50:1534 36.237764 0.451425 80.2741 < 2.2e-16 ed50:997 35.846452 0.436778 82.0702 < 2.2e-16 ed50:800 36.259472 0.370515 97.8624 < 2.2e-16 ed50:993 35.990069 0.370290 97.1942 < 2.2e-16 ed50:802 36.157616 0.480121 75.3094 < 2.2e-16 ed50:1533 35.760774 0.387403 92.3089 < 2.2e-16 ed50:1513 35.554802 0.364493 97.5459 < 2.2e-16 ed50:991 36.359135 0.525088 69.2439 < 2.2e-16 ed50:995 36.576517 0.364259 100.4136 < 2.2e-16 ed50:1524 36.862703 0.415135 88.7968 < 2.2e-16 ed50:1526 36.720234 0.539683 68.0404 < 2.2e-16 ed50:1535 36.088317 0.484100 74.5473 < 2.2e-16 ed50:998 36.300174 0.465232 78.0260 < 2.2e-16 ed50:1532 36.926388 0.536166 68.8712 < 2.2e-16 ed50:956 36.549492 0.435006 84.0207 < 2.2e-16 ed50:1517 36.750788 0.449626 81.7364 < 2.2e-16 ed50:1531 35.595219 0.497707 71.5184 < 2.2e-16 ed50:1505 36.065605 0.450026 80.1411 < 2.2e-16 ed50:803 36.034373 0.432715 83.2752 < 2.2e-16 ed50:990 37.083351 0.260990 142.0870 < 2.2e-16 ed50:805 35.979165 0.388678 92.5680 < 2.2e-16 ed50:804 36.130976 0.405553 89.0907 < 2.2e-16 ed50:987 36.183924 0.416575 86.8604 < 2.2e-16 ed50:1502 36.542293 0.896664 40.7536 < 2.2e-16 ed50:1527 35.655551 0.766186 46.5364 < 2.2e-16 ed50:1503 35.809500 0.369642 96.8761 < 2.2e-16 ed50:1510 36.293631 0.432788 83.8600 < 2.2e-16 ed50:1511 36.369731 0.576979 63.0347 < 2.2e-16 ed50:1530 36.339981 0.363399 100.0002 < 2.2e-16 ed50:992 35.684707 0.391926 91.0496 < 2.2e-16 ed50:1529 35.466865 0.469257 75.5809 < 2.2e-16 ed50:988 37.224365 0.182922 203.4982 < 2.2e-16 ed50:994 36.276992 0.391385 92.6888 < 2.2e-16 ed50:1520 36.086934 0.491977 73.3508 < 2.2e-16 ed50:1525 36.088353 0.464797 77.6433 < 2.2e-16 ed50:1000 35.687905 0.343948 103.7596 < 2.2e-16 ed50:1508 33.629502 0.821242 40.9496 < 2.2e-16 ed50:996 37.102049 0.100176 370.3690 < 2.2e-16 ed50:989 36.194736 0.434644 83.2745 < 2.2e-16 ed50:1521 35.580635 0.597806 59.5187 < 2.2e-16 ed50:801 35.669867 0.396216 90.0264 < 2.2e-16 ed50:1509 35.515465 0.360510 98.5144 < 2.2e-16 ed50:1506 35.373542 0.434845 81.3474 < 2.2e-16
Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error:
0.06299986 (523 degrees of freedom)
Model fitted: Weibull (type 2) with lower limit at 0 (3 parms)
Parameter estimates:
hill:1523 -18.494658 9.573649 -1.9318 0.0539204 .
hill:1512 -14.991701 5.040968 -2.9740 0.0030754 hill:798 -28.962301 11.237091 -2.5774 0.0102279
hill:1528 -7.213380 2.411312 -2.9915 0.0029073 hill:799 -26.253296 8.605156 -3.0509 0.0023975 hill:1515 -15.872921 4.775848 -3.3236 0.0009509 hill:1514 -18.521633 4.864064 -3.8079 0.0001568 hill:1522 -23.537381 8.381299 -2.8083 0.0051662 hill:1519 -18.410739 5.943897 -3.0974 0.0020570 hill:1518 -20.587388 9.110372 -2.2598 0.0242458
hill:797 -18.595834 4.909306 -3.7879 0.0001696 ** hill:806 -16.232345 3.855452 -4.2102 3.003e-05 hill:1501 -28.384744 8.868406 -3.2007 0.0014544 hill:1504 -27.819525 14.485461 -1.9205 0.0553365 .
hill:1507 -11.175580 3.184672 -3.5092 0.0004883 ** hill:1516 -20.696748 6.096816 -3.3947 0.0007393 hill:999 -21.194708 5.053712 -4.1939 3.221e-05 hill:1534 -15.736570 4.536297 -3.4690 0.0005654 hill:997 -19.108879 5.152688 -3.7085 0.0002307 hill:800 -30.210881 12.205186 -2.4752 0.0136301
hill:993 -21.971730 6.162031 -3.5657 0.0003962 ** hill:802 -16.925194 4.855336 -3.4859 0.0005317 hill:1533 -21.052212 6.973166 -3.0190 0.0026596 hill:1513 -19.889528 6.635458 -2.9975 0.0028518 hill:991 -11.984285 3.489891 -3.4340 0.0006419 hill:995 -21.713689 6.485075 -3.3483 0.0008718 hill:1524 -28.409658 21.389421 -1.3282 0.1846878
hill:1526 -39.674781 77.355447 -0.5129 0.6082453
hill:1535 -15.600412 5.211872 -2.9932 0.0028908 hill:998 -15.847823 3.914788 -4.0482 5.942e-05 ** hill:1532 -12.970556 3.676416 -3.5280 0.0004555 hill:956 -22.415973 10.018491 -2.2375 0.0256767
hill:1517 -25.412931 17.670968 -1.4381 0.1509988
hill:1531 -13.130155 4.564014 -2.8769 0.0041802 hill:1505 -14.610927 4.172053 -3.5021 0.0005011 hill:803 -22.248500 7.303797 -3.0462 0.0024348 hill:990 -48.688431 56.912994 -0.8555 0.3926723
hill:805 -16.564232 3.664197 -4.5206 7.635e-06 ** hill:804 -14.713957 3.216787 -4.5741 5.978e-06 hill:987 -17.119427 3.711727 -4.6123 5.014e-06 * hill:1502 -23.667429 7.816101 -3.0280 0.0025830 hill:1527 -8.843092 3.458341 -2.5570 0.0108380
hill:1503 -21.909283 5.218006 -4.1988 3.154e-05 hill:1510 -16.915806 4.564498 -3.7060 0.0002330 * hill:1511 -12.850610 4.435684 -2.8971 0.0039241 hill:1530 -23.608821 7.964521 -2.9642 0.0031726 hill:992 -19.491061 5.615323 -3.4710 0.0005613 hill:1529 -12.566362 4.154752 -3.0246 0.0026122 hill:988 -16.708487 5.475299 -3.0516 0.0023918 hill:994 -20.641451 5.043753 -4.0925 4.942e-05 hill:1520 -30.521082 14.077254 -2.1681 0.0306003
hill:1525 -14.689278 4.516849 -3.2521 0.0012194 hill:1000 -21.848811 5.763489 -3.7909 0.0001676 hill:1508 -8.008997 2.279820 -3.5130 0.0004815 hill:996 -16.561849 3.714740 -4.4584 1.011e-05 hill:989 -18.842583 4.911378 -3.8365 0.0001400 * hill:1521 -27.776907 15.827611 -1.7550 0.0798508 .
hill:801 -22.426992 7.921978 -2.8310 0.0048191 * hill:1509 -17.354832 5.070621 -3.4226 0.0006688 hill:1506 -13.810033 4.100712 -3.3677 0.0008138 max:1523 0.501389 0.019610 25.5676 < 2.2e-16 max:1512 0.562599 0.018139 31.0158 < 2.2e-16 max:798 0.605590 0.013543 44.7163 < 2.2e-16 max:1528 0.588131 0.032682 17.9956 < 2.2e-16 max:799 0.637916 0.013254 48.1291 < 2.2e-16 max:1515 0.591148 0.017090 34.5908 < 2.2e-16 max:1514 0.606699 0.016510 36.7474 < 2.2e-16 max:1522 0.582713 0.017201 33.8758 < 2.2e-16 max:1519 0.589213 0.017118 34.4205 < 2.2e-16 max:1518 0.536954 0.018716 28.6892 < 2.2e-16 max:797 0.613395 0.015968 38.4145 < 2.2e-16 max:806 0.613446 0.016470 37.2456 < 2.2e-16 max:1501 0.592354 0.016112 36.7655 < 2.2e-16 max:1504 0.553082 0.014970 36.9460 < 2.2e-16 max:1507 0.569727 0.020835 27.3447 < 2.2e-16 max:1516 0.586915 0.017092 34.3388 < 2.2e-16 max:999 0.627707 0.015854 39.5927 < 2.2e-16 max:1534 0.609845 0.016203 37.6386 < 2.2e-16 max:997 0.576728 0.017303 33.3303 < 2.2e-16 max:800 0.591251 0.014001 42.2307 < 2.2e-16 max:993 0.582012 0.016648 34.9602 < 2.2e-16 max:802 0.594135 0.017106 34.7320 < 2.2e-16 max:1533 0.557907 0.018173 30.7001 < 2.2e-16 max:1513 0.560375 0.017929 31.2551 < 2.2e-16 max:991 0.606456 0.019033 31.8639 < 2.2e-16 max:995 0.617313 0.014703 41.9844 < 2.2e-16 max:1524 0.608660 0.013477 45.1641 < 2.2e-16 max:1526 0.521960 0.015670 33.3098 < 2.2e-16 max:1535 0.569236 0.017600 32.3426 < 2.2e-16 max:998 0.603522 0.016769 35.9901 < 2.2e-16 max:1532 0.587966 0.016946 34.6957 < 2.2e-16 max:956 0.598522 0.015826 37.8188 < 2.2e-16 max:1517 0.559652 0.019152 29.2213 < 2.2e-16 max:1531 0.583878 0.018542 31.4903 < 2.2e-16 max:1505 0.561526 0.017900 31.3697 < 2.2e-16 max:803 0.579339 0.017213 33.6565 < 2.2e-16 max:990 0.602732 0.014393 41.8758 < 2.2e-16 max:805 0.634311 0.015877 39.9505 < 2.2e-16 max:804 0.637910 0.015854 40.2354 < 2.2e-16 max:987 0.639869 0.015760 40.6000 < 2.2e-16 max:1502 0.582619 0.017153 33.9670 < 2.2e-16 max:1527 0.506445 0.028056 18.0515 < 2.2e-16 max:1503 0.634498 0.015790 40.1824 < 2.2e-16 max:1510 0.582672 0.017196 33.8835 < 2.2e-16 max:1511 0.529575 0.019490 27.1723 < 2.2e-16 max:1530 0.572419 0.015340 37.3160 < 2.2e-16 max:992 0.576131 0.017363 33.1823 < 2.2e-16 max:1529 0.597476 0.018917 31.5846 < 2.2e-16 max:988 0.603518 0.018890 31.9493 < 2.2e-16 max:994 0.617397 0.015708 39.3045 < 2.2e-16 max:1520 0.566657 0.015140 37.4267 < 2.2e-16 max:1525 0.564128 0.018264 30.8883 < 2.2e-16 max:1000 0.624753 0.015944 39.1851 < 2.2e-16 max:1508 0.636625 0.038387 16.5846 < 2.2e-16 max:996 0.630276 0.015953 39.5093 < 2.2e-16 max:989 0.601404 0.018223 33.0029 < 2.2e-16 max:1521 0.517496 0.018854 27.4480 < 2.2e-16 max:801 0.548864 0.018331 29.9426 < 2.2e-16 max:1509 0.593670 0.016990 34.9415 < 2.2e-16 max:1506 0.571556 0.018355 31.1385 < 2.2e-16 ed50:1523 36.748487 0.717807 51.1955 < 2.2e-16 ed50:1512 35.946444 0.520394 69.0755 < 2.2e-16 ed50:798 36.679679 0.333772 109.8943 < 2.2e-16 ed50:1528 35.705693 0.799854 44.6403 < 2.2e-16 ed50:799 36.520430 0.338175 107.9926 < 2.2e-16 ed50:1515 35.767079 0.462190 77.3861 < 2.2e-16 ed50:1514 36.190496 0.418054 86.5689 < 2.2e-16 ed50:1522 35.590977 0.489113 72.7663 < 2.2e-16 ed50:1519 35.409818 0.423957 83.5221 < 2.2e-16 ed50:1518 35.265316 0.511916 68.8889 < 2.2e-16 ed50:797 36.382088 0.415372 87.5892 < 2.2e-16 ed50:806 36.245268 0.415945 87.1395 < 2.2e-16 ed50:1501 35.912649 0.520790 68.9580 < 2.2e-16 ed50:1504 36.600929 0.445503 82.1565 < 2.2e-16 ed50:1507 35.207447 0.585967 60.0844 < 2.2e-16 ed50:1516 35.713417 0.449623 79.4298 < 2.2e-16 ed50:999 36.228822 0.393456 92.0785 < 2.2e-16 ed50:1534 36.455163 0.536000 68.0134 < 2.2e-16 ed50:997 35.888149 0.461509 77.7626 < 2.2e-16 ed50:800 36.392402 0.422412 86.1539 < 2.2e-16 ed50:993 36.074888 0.463092 77.9001 < 2.2e-16 ed50:802 36.182938 0.458110 78.9831 < 2.2e-16 ed50:1533 35.891020 0.496147 72.3395 < 2.2e-16 ed50:1513 35.757802 0.497946 71.8107 < 2.2e-16 ed50:991 36.751491 0.606881 60.5580 < 2.2e-16 ed50:995 36.603430 0.370344 98.8362 < 2.2e-16 ed50:1524 36.890783 0.383714 96.1414 < 2.2e-16 ed50:1526 36.626312 1.256890 29.1404 < 2.2e-16 ed50:1535 36.168749 0.603443 59.9373 < 2.2e-16 ed50:998 36.347488 0.433592 83.8288 < 2.2e-16 ed50:1532 36.936606 0.528360 69.9080 < 2.2e-16 ed50:956 36.588978 0.443689 82.4654 < 2.2e-16 ed50:1517 36.386592 0.770471 47.2264 < 2.2e-16 ed50:1531 35.711954 0.504104 70.8424 < 2.2e-16 ed50:1505 36.177612 0.525912 68.7903 < 2.2e-16 ed50:803 36.082103 0.487126 74.0715 < 2.2e-16 ed50:990 37.053672 0.414481 89.3978 < 2.2e-16 ed50:805 36.029475 0.386039 93.3311 < 2.2e-16 ed50:804 36.209416 0.400065 90.5089 < 2.2e-16 ed50:987 36.221990 0.376936 96.0958 < 2.2e-16 ed50:1502 36.209192 0.487137 74.3307 < 2.2e-16 ed50:1527 35.766941 0.871940 41.0200 < 2.2e-16 ed50:1503 35.843233 0.386383 92.7660 < 2.2e-16 ed50:1510 36.420139 0.456369 79.8041 < 2.2e-16 ed50:1511 36.511329 0.633342 57.6487 < 2.2e-16 ed50:1530 36.480517 0.424555 85.9265 < 2.2e-16 ed50:992 35.750797 0.463211 77.1804 < 2.2e-16 ed50:1529 35.609439 0.480867 74.0525 < 2.2e-16 ed50:988 36.767628 0.517683 71.0234 < 2.2e-16 ed50:994 36.312660 0.397860 91.2698 < 2.2e-16 ed50:1520 36.216215 0.572890 63.2167 < 2.2e-16 ed50:1525 36.161879 0.507860 71.2044 < 2.2e-16 ed50:1000 35.708059 0.408998 87.3061 < 2.2e-16 ed50:1508 33.866112 0.658763 51.4087 < 2.2e-16 ed50:996 36.483989 0.392156 93.0345 < 2.2e-16 ed50:989 36.304037 0.426166 85.1875 < 2.2e-16 ed50:1521 35.545009 0.832777 42.6825 < 2.2e-16 ed50:801 35.695354 0.523239 68.2200 < 2.2e-16 ed50:1509 35.658258 0.435157 81.9434 < 2.2e-16 ed50:1506 35.499497 0.497644 71.3352 < 2.2e-16 ***
Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error:
0.02453777 (523 degrees of freedom)
Non-normality/heterogeneity adjustment through optimal Box-Cox transformation
Estimated lambda: 2 Confidence interval for lambda: [1.79,2.06]`
Running a quick anova test shows that the correction doesn't significantly affect the results : `Chicken.W2.3.sum=broom::tidy(Chicken.W2.3) %>% mutate(Model="W2.3")
Fit: aov(formula = estimate ~ Model, data = Boxcox.comp %>% subset(term == "ed50"))
$Model diff lwr upr p adj W2.3.bc-W2.3 0.04164715 -0.1599361 0.2432304 0.6831898`
However I am neither sure if I can trust those results, nor where the issue may come from. Any clarification or guideline would be greatly appreciated.
Thanks,
Hugo