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<-lmerTest::lmer(decomposed_weight ~ g_diversity:t_diversity+ g_diversity+t_diversity+strip+block+ (1|block/stt_plot/repetition)+ (1|g_diversity:t_diversity:strip:block)+ (1|g_diversity:t_diversity:block)+ (1|g_diversity:strip:block)+ (1|t_diversity:strip:block)+ (1|g_diversity:block)+ (1|t_diversity:block)+ (1|strip:block), data=Dt, na.action=na.omit, REML = FALSE, control = lmerControl(optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
The anova shows significant effect for the interactions- g_diversity:t_diversity.
Why the tests provides different results?
Which of the tests fits to my situation? (if any)