09 September 2017 5 3K Report

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!

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