[R-sig-ME] Zero random effect variance?

[Dave Marvin]

Sorry, I included it as a text file attachment. Guessing the list-serv strips attachments... Here is a link to the text file: http://goo.gl/e5q2hO

If that is the issue (which after looking back at my boxplot is probably the case) should I still expect literally zero variance attributed to the chambers?


On Oct 17, 2013, at 8:16 PM, Ben Bolker wrote:

> Dave Marvin <marvs at ...> writes:
> 
>> 
>> For the following dataset (described at the bottom of this email), 
> 
>  Did you mean to include an actual data set, or just the text description?
> Without the data set itself, we can't do better than guessing.
> 
>> a boxplot of the data by chamber
>> 
>>> height=read.table("height.txt",header=TRUE)
>>> boxplot(HtChg~Chamber,data=height)
> 
>> shows there is clearly a lot of chamber-to-chamber variation in the
>> response variable. However, if I run a random intercept-only model:
> 
>>> lmer(HtChg~1+(1|Chamber),data=height)
>> 
>> I get 0 variance for the random intercept. Same is true if I then 
>> include any categorical fixed effects. Does
>> this seem correct, and if so why? 
>> -Dave
> 
>>> I am analyzing the growth response (Height Change) of two plant
>> types (vines vs. trees) to different CO2 levels, for a mix of
>> species of each plant type in plant growth chambers (Chamber). CO2
>> and FT are categorical predictors, each with two levels
>> (elevated/ambient CO2, vine/tree plant Functional Types). Each
>> growth chamber had the same mix of 8 species (Spp).
> 
>  Presumably the within-chamber variation is large enough that
> it adequately accounts for the among-chamber variation?
> Again, hard to say without seeing the data ... you could do the 
> math yourself (e.g. is variance among >= (variance within)/(n within)?),
> or simulate some representative examples ...
> 
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