Hi,
I have a problem with my post hoc test after a linear mixed model.
My goal is to compare the body temperature of mussels with and without bernacles. During a period of 30 min, at each 2min, I took the body temperature. I have 15 samples of each treatment. As it's the same mussel who I collected the temperature during 30min, my data are dependant and I add a column Indiv who will be a random facteur in my model. I did a lme model :
result1 <- lme(fixed = Body_Temperature ~ Treatment + Temps + Treatment:Temps, random = ~1|Indiv, data = Ind)
Results :
Fixed effects: Body_Temperature ~ Treatment + Temps + Treatment:Temps
Value Std.Error DF t-value p-value
(Intercept) 25.351382 0.2825477 448 89.72426 0.000
TreatmentB -1.024686 0.3995828 28 -2.56439 0.016
Temps 0.814983 0.0150528 448 54.14144 0.000
TreatmentB:Temps -0.207201 0.0212879 448 -9.73328 0.000
Now my problem, my goal is with a post hoc, compare the significance of treatments at each time (t=0, t=2, t=4...).
I try this : emmeans(result1, pairwise ~ Treatment:Temps), but it's give me only for one time, not all.
If you have some ideas.
Thank you 