[R] Mixed effects model where nested factor is not the repeated across treatments lme???
ctu at bigred.unl.edu
ctu at bigred.unl.edu
Wed Jul 30 17:40:26 CEST 2008
Hi Miki,
I just got the same problem with you couple hours ago.
Rusers (Anna, and Mark {thank you guys}) provide me a vary valuable
information.
link to following address.
http://www.nabble.com/Tukey-HSD-(or-other-post-hoc-tests)-following-repeated-measures-ANOVA-td17508294.html#a17559307
for the A vs. B, A vs. C....
You could install and download the multcomp package and perform the
post hoc test
such as
summary(glht(lmel,linfct=mcp(treatment="Tukey")))
hopefully it helps
Chunhao
Quoting M Ensbey <m.ensbey at unimelb.edu.au>:
> Hi,
>
>
>
> I have searched the archives and can't quite confirm the answer to this.
> I appreciate your time...
>
>
>
> I have 4 treatments (fixed) and I would like to know if there is a
> significant difference in metal volume (metal) between the treatments.
> The experiment has 5 blocks (random) in each treatment and no block is
> repeated across treatments. Within each plot there are varying numbers
> of replicates (random) (some plots have 4 individuals in them some have
> 14 and a range in between). NOTE the plots in one treatment are not
> replicated in the others.
>
>
>
> So I end up with a data.frame with 4 treatments repeated down one column
> (treatment=A, B, C, D), 20 plots repeated down the next (block= 1 to 20)
> and records for metal volume (metal- 124 of these)
>
> I have made treatment and block a factor. But haven't grouped them (do I
> need to and how if so)
>
>
>
> The main question is in 3 parts:
>
>
>
> 1. is this the correct formula to use for this situation:
> lme1<-lme(metal~treatment,data=data,random=~1|block) (or is lme even the
> right thing to use here?)
>
>
>
> I get:
>
>> summary(lme1)
>
> Linear mixed-effects model fit by REML
>
> Data: data
>
> AIC BIC logLik
>
> 365.8327 382.5576 -176.9163
>
>
>
> Random effects:
>
> Formula: ~1 | block
>
> (Intercept) Residual
>
> StdDev: 0.4306096 0.9450976
>
>
>
> Fixed effects: Cu ~ Treatment
>
> Value Std.Error DF t-value p-value
>
> (Intercept) 5.587839 0.2632831 104 21.223688 0.0000 ***
>
> TreatmentB -0.970384 0.3729675 16 -2.601792 0.0193 ***
>
> TreatmentC -1.449250 0.3656351 16 -3.963651 0.0011 ***
>
> TreatmentD -1.319564 0.3633837 16 -3.631323 0.0022 ***
>
> Correlation:
>
> (Intr) TrtmAN TrtmCH
>
> TreatmentB -0.706
>
> TreatmentC -0.720 0.508
>
> TreatmentD -0.725 0.511 0.522
>
>
>
> Standardized Within-Group Residuals:
>
> Min Q1 Med Q3 Max
>
> -2.85762206 -0.68568460 -0.09004478 0.56237152 3.20650288
>
>
>
> Number of Observations: 124
>
> Number of Groups: 20
>
>
>
> 2. if so how can I get p values for comparisons between every
> group... ie is A different from B, is A different from C, is A different
> from D, is B different from C, is B different from D etc... is there a
> way to get all of these instead of just "is A different from B, is A
> different from C, is A different from D" which summary seems to give?
> 3. last of all what is the best way to print out all the residuals
> for lme... I can get qqplot(lme1) is there a pre-programmed call for
> multiple diagnostic plots like in some other functions...
>
>
>
>
>
> Thankyou so Much for your time....
>
>
>
> It is much appreciated
>
> ;-)
>
>
>
> Miki
>
>
>
>
> [[alternative HTML version deleted]]
>
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