Kingsford Jones wrote: > On Sun, Jan 25, 2009 at 6:43 PM, tavery <trevor.av...@acadiau.ca> wrote: >> Thanks Ben for a speedy response... >> I agree that a GLMM is probably more prudent and will investigate that idea >> further. My guard is against making the analysis too complicated... >> >> For interest and discussion: In the minutes between my post and the response >> I went 'way back' to consider an even simpler design (if it works with >> unbalanced data). In essence the beekeepers are block factors as the >> treatments were applied within these blocks to colonies at random and the >> beekeeper is not of any interest, just the parasite numbers of the colonies. >> In fundamental design terms, the randomized block design appears a viable >> option. However there are likely issues with the count data (that I will >> investigate as I am unaware of the data per se, but do have access to >> similar data that do, in fact, have lots of zeros). > > Hi Trevor, > > Yes, it sounds as though you have a nice, simple RBD that can be > analyzed using the code Ben suggested. The lack of balance shouldn't > be a problem as long as you use one of the mixed models functions > (lmer, lme, glmmPQL, etc) rather than aov. The fact that you have > count data shouldn't be a problem, although if you have an excessive > number of zeros you might want to have a look at the non-CRAN package > glmmADMB. > > hth, > > Kingsford Jones
Just a quick point: *estimation* should be fairly straightforward (easiest with log-transformed data -> lmer, lme, harder with Poisson data -> glmer, glmmML, glmmAK, hardest with negative binomial/overdispersed data -> glmmADMB). Be very careful with glmmPQL, known to be biased with low (<5-10) average counts per unit. *Inference* is a can of worms: read all about it on the r-sig-mixed-models mailing list archive ... You may want to forward further questions along these lines to r-sig-mixed-mod...@r-project.org instead ... Ben Bolker > > >> thanks, >> trevor >> >> Ben Bolker wrote: >>> My two cents: >>> >>> * a GLMM if parasite numbers are small enough to >>> have to deal with them as count data (e.g. lots of zeros). >>> Otherwise (if you're lucky, as GLMMs are harder) most >>> likely a lognormal -- log-transform data or log(1+x) if >>> there are some zeros, and treat as a LMM (nlme or lmer). >>> >>> * "Nesting" is more or less a red herring here, only >>> really has to do with multiple *random* factors (and >>> then more to do with the coding of the random factors >>> than with fundamental experimental design distinctions). >>> >>> * so: antiG vs control is fixed, Beekeeper is probably >>> best treated as random (7 units is enough to make a >>> random effect plausible: if you had only 2 or 3 you >>> would probably have to treat as a fixed effect to >>> make progress) >>> >>> * because unbalanced (and possibly GLMM), aov/sums >>> of squares approaches are probably not viable >>> >>> * fairly straightforward with nlme (something like >>> lme(logparasites ~ antiG, random = ~1|Beekeeper) or >>> lme4: >>> >>> lmer(logparasites ~ antiG + (1|Beekeeper)) or >>> (for GLMM) >>> >>> glmer(logparasites ~ antiG + (1|Beekeeper), family=poisson) >>> >>> * Two more things to watch out for: >>> >>> - lme (nlme package) will give you p-values, lmer (lme4 package) >>> will not >>> - if you end up fitting a GLMM you should definitely >>> worry about/check for overdispersion >>> >>> Ben Bolker >>> >>> >>> tavery wrote: >>> >>>> Hi all, >>>> Maybe an expert of this particular design could provide insights into a >>>> interesting question (or possibly just a derailed view). Possibly outside >>>> of >>>> the R world, but has to be sorted out before R code can be generated - >>>> which >>>> should be trivial... >>>> >>>> - 7 beekeepers each with several hives >>>> - some hives treated with antiG, others left as controls >>>> - unbalanced design (not an equal number of treated or control sites >>>> among or within beekeepers) >>>> - measured parasite numbers (average per hive) >>>> Q: want to know if antiG reduces parasite load >>>> >>>> The initial reaction (from a student) was to consider Beekeeper as a >>>> random factor (although it could be considered fixed), and nest Treatment >>>> (antiG or control) within Beekeeper. This design is intuitive as Beekeepers >>>> are 'groups' and hives are 'subgroups' to which treatments are applied. >>>> Upon >>>> some investigation, it appears that the model could be flipped i.e. >>>> consider >>>> Treatment as a fixed factor and nest Beekeeper within Treatment. In this >>>> latter case, each Beekeeper would be represented in each Treatment and a >>>> crossed design results i.e. not nested at all. Various texts appear to >>>> 'arbitrarily' designate factors in similar models (see Zar on >>>> drug/drugstore >>>> example). >>>> >>>> a) What design is correct? >>>> b) What am I missing in way of determining groups and the ultimate >>>> design? >>>> >>>> thanks in advance, >>>> trevor >>>> biology department >>>> acadia >>>> -- Ben Bolker Associate professor, Biology Dep't, Univ. of Florida bol...@ufl.edu / www.zoology.ufl.edu/bolker GPG key: www.zoology.ufl.edu/bolker/benbolker-publickey.asc _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
