[R-sig-ME] Vedr. lmer (lme4): % total variance explained by random effect

[Katharina May]

Hi,

great thanks for answering me: now I understand why my question was rather a
bit "naive" in a way....


-Katharina

2009/8/3 Reinhold Kliegl <reinhold.kliegl at gmail.com>:
> There is a recent paper that may give you a start on why this is a
> difficult question.
>
> Edwards, L.J. et al. (2008). An R2 statistic for fixed effects in the
> linear mixed model.
> Statistics in Medicine, 27, 6137-6157.
> DOI: 10.1002/sim.3429
>
> Reinhold Kliegl
>
>
> On Sun, Aug 2, 2009 at 11:27 PM, Liliana Martinez<ltiana_m at yahoo.com> wrote:
>> Yes, I hoped too that somebody would answer this question. I read in Baayen (2008, pp.258-259,  http://www.monkproject.org/MONK.wiki/attachments/2006595/2130450) that the variance described by a random effect can be calculated by dividing the amount of variance accounted for by the random effects with the variance explained jointly by the random and fixed effects . Is there a less roundabout way?
>>
>>
>> regards
>>
>> Liliana Martinez
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> ________________________________
>> Fra: Katharina May <may.katharina at googlemail.com>
>> Til: r-sig-mixed-models at r-project.org
>> Sendt: Søndag, august 2, 2009 17:23:38
>> Emne: Re: [R-sig-ME] lmer (lme4): % total variance explained by random effect
>>
>> Hi,
>>
>> just out of curiosity because nobody is answering:
>> is it not not possible to calculate the variance described by a random
>> effect on slope and intercept as percentage of the total variance
>> (variance of random effect + unexplained variance)?
>>
>> Would be more than happy if somebody can help me...
>>
>> Thanks,
>>
>>       Katharina
>>
>>
>> 2009/7/24 Katharina May <may.katharina at googlemail.com>:
>>> Hello,
>>>
>>> just to say sorry if this questions may be somewhat "inappropriate": I'm a
>>> bachelor student,
>>> recently started with R and with trying to understand mixed models, but I'm
>>> somewhat stuck with
>>> the following problem and hope somebody might be able to help me finding a
>>> solution:
>>>
>>> How can I get the variance (in % of the total variance) which is explained
>>> by the random effect (both on slope
>>> and intercept together)?
>>> My aim is to say something like xx% of the variance is explained by the
>>> random effect...
>>>
>>> As I'm not sure how to deal with this I would be more than happy for any
>>> hints...
>>>
>>> Thank you very much and With Best Wishes from Freising/Germany,
>>>
>>>                         Katharina
>>>
>>>
>>>
>>> here an example output of a mixed model I use with 1 random effect on both
>>> slope and intercept,
>>> fitted with method=ML:
>>>
>>>
>>> Linear mixed model fit by maximum likelihood
>>> Formula: log(AGB) ~ log(BM_roots) + (log(BM_roots) |
>>> as.factor(biomass_data[which(biomass_data$woody ==      1), 2]))
>>>    Data: biomass_data[which(biomass_data$woody == 1), ]
>>>    AIC   BIC logLik deviance REMLdev
>>>  588.6 619.6 -288.3    576.6     583
>>> Random effects:
>>>  Groups                                                     Name
>>>                          Variance   Std.Dev.   Corr
>>>  as.factor(biomass_data[which(biomass_data$woody == 1), 2]) (Intercept)
>>> 1.7568529  1.325463
>>>                                                             log(BM_roots)
>>>                           0.0071313  0.084447  -0.393
>>>  Residual
>>> 0.0809467 0.284511
>>> Number of obs: 1282, groups: as.factor(biomass_data[which(biomass_data$woody
>>> == 1), 2]), 22
>>>
>>> Fixed effects:
>>>               Estimate Std. Error t value
>>> (Intercept)    1.33062    0.29669    4.48
>>> log(BM_roots)  0.93182    0.02441   38.17
>>>
>>> Correlation of Fixed Effects:
>>>             (Intr)
>>> log(BM_rts) -0.446
>>>
>>>
>>
>>
>>
>> --
>> Time flies like an arrow, fruit flies like bananas.
>>
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>



-- 
Time flies like an arrow, fruit flies like bananas.