[R-sig-ME] lmer (lme4): % total variance explained by random effect
Katharina May
may.katharina at googlemail.com
Sun Aug 2 17:23:38 CEST 2009
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
>
>
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