R help - Aug 2007 - Variance explained in mixed models

Hi,

I realise this has come up before in various reincarnations but I couldnt
find the answer...

I wish to quote the "percentage variance explained" by each of three
components in my mixed model, one random effect and two fixed effects.

lmer(response~x1+x2+(1|random), data=data)

Using lmer I can get the variance explained by the random effect but not
the fixed effects obviously.

I was using a suggestion by someone that you can compare the variance
components of the model with with both fixed terms included, with another
model that has one of the fixed terms removed. When I did this the
Variance + residual variance did not add up to the same figure each time.

#first model with both fixed terms
          Variance StdDev
(Intercept) 1.074666 1.036661
Residual    1.136264 1.065957

#second model with one term ommitted
            Variance StdDev
(Intercept) 1.113713 1.055326
Residual    1.154527 1.074489

#third model with the other term ommitted
Variance StdDev
(Intercept) 1.069478 1.034156
Residual    1.145590 1.070322

Actually the second model with only one fixed effect is saying that the
percentage variance explained is higher than the one with both fixed
effects which is impossible right?

Any better ideas?

Thanks in advance, Simon.


Simon Pickett
PhD student
Centre For Ecology and Conservation
Tremough Campus
University of Exeter in Cornwall
TR109EZ
Tel 01326371852

http://www.uec.ac.uk/biology/research/phd-students/simon_pickett.shtml

Ahh, the key to getting what you want is to ask the same question over
and over again. This question is not about R and an answer can be found
in all basic books on hierarchical linear models. 
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Simon Pickett
> Sent: Friday, August 31, 2007 9:43 AM
> To: r-help at stat.math.ethz.ch
> Subject: [R] Variance explained in mixed models
> 
> Hi,
> 
> I realise this has come up before in various reincarnations 
> but I couldnt find the answer...
> 
> I wish to quote the "percentage variance explained" by each 
> of three components in my mixed model, one random effect and 
> two fixed effects.
> 
> lmer(response~x1+x2+(1|random), data=data)
> 
> Using lmer I can get the variance explained by the random 
> effect but not the fixed effects obviously.
> 
> I was using a suggestion by someone that you can compare the 
> variance components of the model with with both fixed terms 
> included, with another model that has one of the fixed terms 
> removed. When I did this the Variance + residual variance did 
> not add up to the same figure each time.
> 
> #first model with both fixed terms
>           Variance StdDev
> (Intercept) 1.074666 1.036661
> Residual    1.136264 1.065957
> 
> #second model with one term ommitted
>             Variance StdDev
> (Intercept) 1.113713 1.055326
> Residual    1.154527 1.074489
> 
> #third model with the other term ommitted Variance StdDev
> (Intercept) 1.069478 1.034156
> Residual    1.145590 1.070322
> 
> Actually the second model with only one fixed effect is 
> saying that the percentage variance explained is higher than 
> the one with both fixed effects which is impossible right?
> 
> Any better ideas?
> 
> Thanks in advance, Simon.
> 
> 
> Simon Pickett
> PhD student
> Centre For Ecology and Conservation
> Tremough Campus
> University of Exeter in Cornwall
> TR109EZ
> Tel 01326371852
> 
> http://www.uec.ac.uk/biology/research/phd-students/simon_pickett.shtml
> 
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