[R-sig-ME] is a mixed effect model appropiate?

Alday, Phillip Phillip.Alday at mpi.nl
Wed Aug 9 23:41:18 CEST 2017

With only three sites, you don't have enough levels to use site as a
grouping variable / random effect. Random effects are *variance*
components and it doesn't make too much sense to discuss variance with
only three group members.

You could include site as a fixed effect, as you're doing now; adding
interaction terms would largely address the independence issue. Note
however that the inference from fixed and random effects is slightly
different: with fixed effects, you get estimates for each level, but
for random effects you get an estimate of the variance between / due to
sites and, optionally, a prediction for individual sites. So the random
effect will tend to generalize better to across all possible sites,
assuming that you sampled enough sites to begin with, while the fixed
effect will better model individual sites.

In your case, I would focus on including interaction terms before
modelling site. If you are able to do that, I would include site as a
fixed effect (too few levels as a random effect), but I suspect site
will correlate strongly with some of the other variables and so you
might have some issues with collinearity.

One final thing: you can fit (Gaussian) linear models with glm(), but
lm() will tend to be faster and offer some additional summary info. You
of course still need glm() for generalized variants such as logit, etc.
For lmer and glmer, the distinction is stricter -- you must use lmer()
for the (Gaussian) linear case and glmer() for the generalized case or
glmer() will complain.


On Wed, 2017-08-09 at 16:26 -0300, Tamara R wrote:
> Hi, i'm working with survey data regarding leptospirosis knowledge,
> attitudes and practices on residents from three slum settlements and
> i'm
> using socio-demographic indicators, knowledge score and attitude
> score as
> predictors of preventive practices score.
> I started analyzing my data as a linear model with both categorical
> and
> continuous predictors:
> glm(practices~site + sex + education + occupation + knowledge score +
> attitude score
> But discussing the results with my phD advisor she suggested me to
> put site
> as a random effect in a linear mixed model because of lack of
> independence
> between observations from the same site:
> lmer(practices~sex + education + occupation + knowledge score +
> attitude
> score + (1|site))
> Thing is that i have less than 100 observations and the variance of
> random
> effects equals to 0. I read in a previous post on this group that it
> indicates that the model could be simplified by removing the random
> effect
> but i wish to know if simplifying my model (going back to the
> original
> regression model) will be appropiate to model the lack of
> independence of
> the data or should i also include random slopes for knowledge and
> attitude
> scores into the model? Thanks in advance
> Tamara Ricardo
> Lic. en Biodiversidad - Becaria CONICET
> FHUC - Universidad Nacional del Litoral
> Ciudad Universitaria - Pje. el Pozo
> Santa Fe (3000) - Argentina
> 	[[alternative HTML version deleted]]
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