[R] Mixed effects model where nested factor is not the repeated across treatments lme???

[Mark Difford]

Hi Miki and Chunhao,

<< Rusers (Anna, and Mark {thank you guys}) provide me a vary valuable  
<< information.

Also see Gavin Simpson's posting earlier today: apparently multcomp does now
work with lmer objects (it's gone through phases of not working, then
working: it's still being developed). Beware, though, that random effects
are specified differently, so it's not as easy to recast an aov(... +
Error(...)) term structure as an equivalent random effect's structure.

HTH, Mark.


ctu wrote:
> 
> 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]]
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
> 
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
> 
> 

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