2011/4/11 Yuan-Ye Zhang <zhangyuanye0706@gmail.com>

> Dear list,
>
>  Sorry I am not sure if I sent this mail several times to this mail list,
but I kept receiving fail delivered information......


> Hi,
>
> I have a fixed effect (treatment) in my lmer model with 3 levels (C, D and
> N). My question is whether treatment D or N respectively has a significant
> effect compared to treatment C?
>
> My model is written as
>
> model<-lmer (Y~ harvest.time + treatment + (1|table) + (treatment | family)
> )
>
> Y: continuous
> harvest.time: continuous, positively influence Y
> treatment: categorical, levels =3  (C, D, N)
> table: categorical, levels=6 (C1, C2, D1, D2, N1, N2) each treatment has
> two tables
> family: factored, categorical, levels=134 (1,2,3,....134) one family has 3
> replicates on each table, therefore 6 replicates in each treatment, but I
> have several missing values.
>
>
> If I use LRT anova (model, update (model, ~. - treatment) ), I get an idea
> of whether treatment overall has a significant effect on Y.
> *But how can I know whether this significance is due to the significant
> effect of D versus C, or the effect of N versus C?*
> *
> *
> similarly, if I anova model, with model.1<- lmer (Y~ harvest.time +
> treatment + (1|table) + (1 | family) ), I get an idea of whether there is
> significant random slope of families among treatments (or family* treatment
> interaction).
> But how can I know whether there is significant family* treatment
> interaction between C and D, or significant family* treatment interaction
> between C and N?
>
> One solution that I can think of is just to divide the dataset into two
> subset, C&D, C&N. Is it correct?
> And is it any solution in lmer model comparable to TukeyHSD or Dunnett
> of multiple comparisons in t.test?
>
>
> Many thanks,
>
> Yuanye
>

	[[alternative HTML version deleted]]


