Hi All,

I have two related practical questions concerning GLMMs. I hope my
questions aren't too simplistic for this list, but other forums have been
not been responsive.

My model is specified as follows:

lmer(countData ~ 3LevelFactor + continuous + (1|5LevelFactor),
family=poisson)

and there is some redundancy between the fixed components and between the
continuous variable and random factor, but not loads.

1) How can I visualize the effect of the fixed factor while accounting for
the continuous variable and random factor?

I'm looking to make a column graph with standard error bars that would be
the multiple-regression equivalent of a partial plot. I think some people
would refer to this as a "least-squared means" plot? Ideally, I could
obtain a vector of adjusted values for the dependent variable and plot them
over levels of the factor. Because redundancy is low, I'm OK with plotting
raw data, but on principle I like my visualizations to accurately reflect
my analysis if possible.

2) Are there statistics like the R-sq of linear regression that will denote
how much variation in my dependent variable is explained by my model? What
about statistics like partial-R-sq, that denote the amount of variation
explained by individual fixed parameters?

Cheers,

tim
--

Timothy E. Farkas
Dept. Animal and Plant Sciences
Alfred Denny Building
University of Sheffield
Western Bank
Sheffield S10 2TN
United Kingdom

	[[alternative HTML version deleted]]

