Tukey after lme does not match
Hi: In addition to Dr. Robinson's comments, I would add the following: (i) Your model specification indicates that you want random slopes for trial by subject with correlated intercepts. Is that what you intended? (ii) I'm wondering why you're not treating angle as a continuous variable and looking for potential trends in the response as a function of angle. If you have a discernable trend, its form would be more useful than a collection of multiple comparisons. Did you plot the response by subject and angle (either a conditioning plot by subject or a 'spaghetti plot' of individual profiles of (angle, RMSy) pairs)? My 2c, Dennis
On Thu, Jan 5, 2012 at 4:22 PM, Alen Hajnal <Alen.Hajnal at usm.edu> wrote:
Dear R users: I have a simple lme model based on the following data: sub ? ? trial ? angle ? RMSy 1 ? ? ? 1 ? ? ? 30 ? ? ?3.745084 1 ? ? ? 2 ? ? ? 0 ? ? ? 7.520667 1 ? ? ? 3 ? ? ? 90 ? ? ?11.17038 1 ? ? ? 4 ? ? ? 15 ? ? ?7.581526 1 ? ? ? 5 ? ? ? 60 ? ? ?11.17822 1 ? ? ? 6 ? ? ? 75 ? ? ?8.440891 1 ? ? ? 7 ? ? ? 45 ? ? ?13.19024 1 ? ? ? 8 ? ? ? 15 ? ? ?9.822035 1 ? ? ? 9 ? ? ? 60 ? ? ?6.002665 1 ? ? ? 10 ? ? ?75 ? ? ?4.393961 1 ? ? ? 11 ? ? ?0 ? ? ? 7.436676 When I run the model the results show that 0vs45, 0vs60, 0vs75, and 0vs90 are all significant main effects:
m.base01<-lme(RMSy ~ angle+trial, data=data, ?random=~ trial|sub,method='ML') summary(m.base01)
Fixed effects: RMSy ~ angle + trial ? ? ? ? ? ? ? ? ? ? ? ?Value ? Std.Error ? ? ? DF ? ? t-value ? ? p-value (Intercept) ?5.236020 ? 0.6312637 ? ? ? 267 8.294505 ?0.0000 angle15 ? ? -0.687669 ? 0.4140277 ? ? ? 267 -1.660925 ?0.0979 angle30 ? ? -0.571092 ? 0.4129365 ? ? ? 267 -1.383001 ?0.1678 angle45 ? ? -0.984597 ? 0.4139330 ? ? ? 267 -2.378638 ?0.0181 angle60 ? ? -0.874718 ? 0.4135615 ? ? ? 267 -2.115085 ?0.0353 angle75 ? ? -1.389835 ? 0.4113411 ? ? ? 267 -3.378788 ?0.0008 angle90 ? ? -1.620493 ? 0.4133209 ? ? ? 267 -3.920666 ?0.0001 trial ? ? ? -0.009372 ? 0.0299782 ? ? ? 267 -0.312620 ?0.7548 However, when I try to run a Tukey posthoc test, I get different results (notice that the estimates are the same as the main effect values above): ONLY 0vs90 is significant at p<.05 :
summary(glht(m.base01, linfct=mcp(angle = "Tukey")))
? ? ? ? Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: lme.formula(fixed = RMSy ~ angle + trial, data = data, random = ~trial | ? ?sub, method = "ML") Linear Hypotheses: ? ? ? ? ? ? Estimate Std. Error z value Pr(>|z|) 15 - 0 == 0 ? -0.6877 ? ? 0.4082 ?-1.684 ?0.62652 30 - 0 == 0 ? -0.5711 ? ? 0.4072 ?-1.403 ?0.80077 45 - 0 == 0 ? -0.9846 ? ? 0.4081 ?-2.412 ?0.19329 60 - 0 == 0 ? -0.8747 ? ? 0.4078 ?-2.145 ?0.32592 75 - 0 == 0 ? -1.3898 ? ? 0.4056 ?-3.427 ?0.01070 * 90 - 0 == 0 ? -1.6205 ? ? 0.4075 ?-3.976 ?0.00138 ** 30 - 15 == 0 ? 0.1166 ? ? 0.4079 ? 0.286 ?0.99996 45 - 15 == 0 ?-0.2969 ? ? 0.4078 ?-0.728 ?0.99090 60 - 15 == 0 ?-0.1870 ? ? 0.4078 ?-0.459 ?0.99931 75 - 15 == 0 ?-0.7022 ? ? 0.4050 ?-1.734 ?0.59342 90 - 15 == 0 ?-0.9328 ? ? 0.4073 ?-2.290 ?0.24842 45 - 30 == 0 ?-0.4135 ? ? 0.4082 ?-1.013 ?0.95124 60 - 30 == 0 ?-0.3036 ? ? 0.4074 ?-0.745 ?0.98971 75 - 30 == 0 ?-0.8187 ? ? 0.4052 ?-2.021 ?0.40123 90 - 30 == 0 ?-1.0494 ? ? 0.4076 ?-2.574 ?0.13377 60 - 45 == 0 ? 0.1099 ? ? 0.4077 ? 0.269 ?0.99997 75 - 45 == 0 ?-0.4052 ? ? 0.4059 ?-0.998 ?0.95449 90 - 45 == 0 ?-0.6359 ? ? 0.4078 ?-1.559 ?0.70835 75 - 60 == 0 ?-0.5151 ? ? 0.4050 ?-1.272 ?0.86496 90 - 60 == 0 ?-0.7458 ? ? 0.4074 ?-1.831 ?0.52730 90 - 75 == 0 ?-0.2307 ? ? 0.4049 ?-0.570 ?0.99763 Why does the Tukey not match the lme results? Any help would be much appreciated! Thanks, Alen ---------- Alen Hajnal, PhD. Department of Psychology The University of Southern Mississippi 118 College Drive #5025 Hattiesburg, MS 39406 USA Tel. +1 (601) 266-4617 alen.hajnal @ usm.edu http://ocean.otr.usm.edu/~w785427/lab.html
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