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
[[alternative HTML version deleted]]