I am currently using the relaimpo package to estimate the relative
importance of regressors (N= 4000):

> m1 <- lm(y ~ x1+x2+x3+x4+x5+, data=data)
> calc.relimp(m1, rela=TRUE)
> m2=boot.relimp(m1, boot = 500, rela=TRUE, type="lmg")
> booteval.relimp(m2)
> plot(booteval.relimp(m2))

In a new dataset with 3 measurement points (0,6,12 weeks), I want to
perform a similar analysis, and want overall relative importance estimates
over all 3 time points.

A standard mixed effects model would be adding time as fixed and subject as
random effect:

> m1 <- lmer(y ~ x1+x2+x3+x4+x5+time+(1| subject), data=data)
> m1.p = pvals.fnc(m1)

Unfortunately, that does not allow me to estimate relative importance
because relaimpo cannot handle LME4 output.
> Error in calc.relimp.default.intern(object = <S4 object of class
> "mer">,  :  If x is NULL, then object must be a data frame or a
> matrix.

Would it be possible to transform the LME4 output in a way that relaimpo
could read it? Do you have other ideas as to how to tackle this, e.g. other
ways of tackling the concept of relative importance / strength of the
regressors?

Thank you
EF

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