Zero random effect variance?

For the following dataset (described at the bottom of this email), 

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).

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 ...

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?

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 ...