Hello,

I just released that my first Email was somehow not shown completly, so here
another try:
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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