[R-sig-ME] multiple comparison (many to one) of fixed effect and random slop in lmer model
Ben Bolker
bbolker at gmail.com
Tue Apr 12 16:54:01 CEST 2011
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 at gmail.com <mailto:bbolker at gmail.com>>
>
> On 04/12/2011 09:50 AM, Yuan-Ye Zhang wrote:
> > 2011/4/11 Yuan-Ye Zhang <zhangyuanye0706 at gmail.com
> <mailto:zhangyuanye0706 at 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.
>
>
>
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