How to estimate the variance of the LME model?

How to estimate the variance of the LME model?

Where I am struggling is with the interpretation of the results from the initial lme model (with treatment and source as fixed effects) and the random model to estimate the variance components (with treatment and source as random effect).

What are the problems with a mixed model?

The problem with mixed models is that the variance estimates for the random effects are constrained to be positive, thus placing a boundary within the optimization region. But suppose a particular random effect is not really needed in your model — i.e. the variance of the random effect is 0.

How to extract the standard error of variance?

I need to extract the standard error of variance component from the output of lmer . It is NOT the standard error of the variance. And I want the standard error . How can I have it ?

How to extract standard error from lmer output?

R codes for extracting the Estimate , annotated by 4 in the table 12-17 : Now I want to extract the results of Std Errors , annotated by 5 in the table 12-17 from lmer output . Many Thanks ! I think you are looking for the Wald standard error of the variance estimates.

When does percentage of variance relate to significance?

I find in most cases that the percentage of variance explained by each factor does not correspond to the significance of the fixed effect. For example for the trait HD, The initial lme suggests a tendency for the interaction as well as a significance for Treatment.

How to calculate variance without a random effect?

Without a random effect, I could easily use the sums of squares (SS) to calculate the variance explained by each factor. But for a mixed model (with ML estimation), there are no SS, hence I thought I could use Treatment and Source as random effects too to estimate the variance, i.e.

Which is an example of an initial LME?

For example for the trait HD, The initial lme suggests a tendency for the interaction as well as a significance for Treatment. Using a backward procedure, I find that Treatment has a close to significant tendency.

How is the variance of a predictor inflated?

As you can see, three of the variance inflation factors — 8.42, 5.33, and 4.41 — are fairly large. The VIF for the predictor Weight, for example, tells us that the variance of the estimated coefficient of Weight is inflated by a factor of 8.42 because Weight is highly correlated with at least one of the other predictors in the model.

Why is the variance of the estimated weight coefficient inflated?

Therefore, the variance inflation factor for the estimated coefficient Weight is by definition: Again, this variance inflation factor tells us that the variance of the weight coefficient is inflated by a factor of 8.4 because Weight is highly correlated with at least one of the other predictors in the model.

How to calculate variance in a mixed effect model?

A mixed effects model. Character value, indicating the variance component that should be returned. By default, all variance components are returned. The distribution-specific ( “distribution”) and residual ( “residual” ) variance are the most computational intensive components, and hence may take a few seconds to calculate.

How to extract random effect from lme4 model?

Also checked any extractor functions in the lme4 package to no avail. Please help! …In general, you can look at the source of the print and summary methods for “mer” objects:

How to calculate the variance of a model?

Character value, indicating the variance component that should be returned. By default, all variance components are returned. The distribution-specific ( “distribution”) and residual ( “residual” ) variance are the most computational intensive components, and hence may take a few seconds to calculate. Toggle off warnings. Currently not used.