Zero random effect variance?
On 13-10-17 08:28 PM, Dave Marvin wrote:
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?
Yes. This is a case, I think (referred to from time to time in threads on this list), where the classical method of moments estimates would give a negative among-group variance, or a compound symmetry model would give negative within-group correlations; the framework used in lme4 can't do either of those things easily. (It would be entertaining but totally impractical to try to figure out what kind of imaginary- or complex-valued values one would need in the computations to get this to work out). Ben Bolker
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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