Many thanks, Ben!

I think it is a cool idea to combine the two datasets and then check the
interaction by orig.
But the t-test still sounds tempting because it is simpler.
I am curious whether they give qualitative similar result.

Best,
yuanye

2011/4/12 Ben Bolker <bbolker@gmail.com>

> On 04/12/2011 10:26 AM, Yuan-Ye Zhang wrote:
> > Dear Ben,
> >
> > Thank you very much for your kind explanation.
> >
> > And this also assured me that my mail is not rejected, because I kept
> > receive rejection mails.... :(
> >
> > (1) Maybe a further related question. In lmer, I got SE estimated for
> > fixed effect in the summary,  can I use simple X +/- 1.96 * SE for the
> > confidence intervals? Or I need to do parametric bootstrap?
>
>   It depends on your sample size.  Since you have 134 families I would
> guess that your sample is large enough that the +/- 1.96 SE confidence
> intervals will be very close to the parametric bootstrap results.  (You
> might try one example to convince yourself.)
>
> > And if I
> > have two dataset analyzed using the same model, how can I compare
> > whether treatment effect (or treatment * family interaction) in data.1
> > is larger than in data.2?  What kind of bootstrap should I use?
>
>   Comparing across data sets is sometimes tricky because the standard
> model comparison recipes don't work.  I would think the most rigorous
> approach would be to combine the two data sets into a single data set
> with an additional variable that marks the origin of each data point
> (e.g. rbind(data.frame(data1,orig=1),data.frame(data2,orig=2))) and then
> run an analysis that includes interactions with "orig" and see if the
> treatment:orig interaction (for example) is significant.  More crudely,
> you could assume normal sampling distributions for the parameters and do
> a t-test for equality of the parameters.
>
> >
> > (2) I will try it. Many thanks for the code.
> >
> > Best,
> > yuanye
> >
> >
> > 2011/4/12 Ben Bolker <bbolker@gmail.com <mailto:bbolker@gmail.com>>
> >
> >     On 04/12/2011 09:50 AM, Yuan-Ye Zhang wrote:
> >     > 2011/4/11 Yuan-Ye Zhang <zhangyuanye0706@gmail.com
> >     <mailto: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?
> >
> >      I have three answers to this:
> >
> >      (1) I'm not wild about _post hoc_ pairwise tests in any case. They
> have
> >     their place, but I think it is often sufficient to show that there is
> an
> >     overall significant pattern, and then to interpret the pattern of
> >     coefficients and confidence intervals on them, rather than
> specifically
> >     fishing for p-values on particular contrasts.
> >
> >      (2) for fixed effect comparisons, the multcomp package seems to
> behave
> >     sensibly (use with caution, there may be some issue with
> finite-sample
> >     corrections?)
> >
> >     library(lme4)
> >     cbpp$obs <- 1:nrow(cbpp)
> >     gm2 <- glmer(cbind(incidence, size - incidence) ~ period +
> >             (1 | herd) +  (1|obs),
> >                      family = binomial, data = cbpp)
> >     library(multcomp)
> >     glht(gm2,linfct=mcp(period="Tukey"))
> >
> >     (3) I'm not sure about the comparison you want to make above, which
> >     involves random effects.  You want to know "whether there is
> significant
> >     family*treatment interaction between C and D" ...  I think your idea
> of
> >     dividing the data into subsets is a good one in this case.
> >
> >
> >
>
>

	[[alternative HTML version deleted]]


