My experimental design is GRBD's (generalized randomized block design) with split plot (strips 1&2).
I made my model with the lmerTest package to check the effects of g_diversity and t_diversity on the response variable decomposed_weight:
m3b lsmeans(m3b, list(pairwise ~ g_diversity:t_diversity), adjust = "tukey")
Mix,2 years - No-Mix,2 years 0.8687
Mix,2 years - Mix,4 years 0.0526
Mix,2 years - No-Mix,4 years 0.6410
No-Mix,2 years - Mix,4 years 0.2573
No-Mix,2 years - No-Mix,4 years 0.1999
Mix,4 years - No-Mix,4 years 0.0212
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> difflsmeans(m3b, test.effs = "g_diversity:t_diversity")
g_diversity:t_diversity Mix 2 years - No-Mix 2 years 0.474
g_diversity:t_diversity Mix 2 years - Mix 4 years 0.013 *
g_diversity:t_diversity Mix 2 years - No-Mix 4 years 0.258
g_diversity:t_diversity No-Mix 2 years - Mix 4 years 0.076 .
g_diversity:t_diversity No-Mix 2 years - No-Mix 4 years 0.056 .
g_diversity:t_diversity Mix 4 years - No-Mix 4 years 0.005 **
I would like to know-
Why the tests provides different results?
Which of the tests fits to my situation? (if any)
Thanks!