Hello, I have some doubts on TukeyHSD application.
I want to investigate the effects of depth, latitude and month variation on the length of a fish. These are orthogonal and observational data. For this, I have made an aov model (L~month+lat+prof+month*lat), after applying drop1 and step functions. But when I applied TukeyHSD I had unexpected results. For instance, I have three levels for latitude and the mean and standard deviation of lengths are: > aggregate(LtMm,list(FLat=FLat),mean) FLat x 1 24.5 431.8745 2 25 415.9973 3 25.5 416.0420 > aggregate(LtMm,list(FLat=FLat),sd) FLat x 1 24.5 114.6516 2 25 108.9774 3 25.5 105.5219 So, it's expected to have 25 and 25.5 levels closer than 24.5, and we see this making a simple aov model: > aov.LtArL <-aov(LtMm~FLat) > TukeyHSD(aov.LtArL, ordered = TRUE) Tukey multiple comparisons of means 95% family-wise confidence level factor levels have been ordered Fit: aov(formula = LtMm ~ FLat) $FLat diff lwr upr p adj 25.5-25 0.04474535 -16.009079 16.09857 0.9999764 24.5-25 15.87715429 -5.371913 37.12622 0.1860347 24.5-25.5 15.83240894 -3.213078 34.87790 0.1251572 Nevertheless, the complete model indicates just the opposite: > aov.LtAr<-aov(LtMm~FMes+FLat+FProf+FMes*FLat) > TukeyHSD(aov.LtAr,"FLat",ordered=T) Tukey multiple comparisons of means 95% family-wise confidence level factor levels have been ordered Fit: aov(formula = LtMm ~ FMes + FLat + FProf + FMes * FLat) $FLat diff lwr upr p adj 24.5-25.5 6.46322 -11.706623 24.63306 0.6815646 25-25.5 19.72066 4.404934 35.03639 0.0072350 25-24.5 13.25744 -7.014670 33.52955 0.2751153 Which should be the right interpretation? Thanks in advance for any help! Best regards, Mariana L. L. A. Botelho MSc candidate São Paulo Fisheries Institute Brazil [[alternative HTML version deleted]]
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