Dear R-people,
I am analyzing epidemiological data using GLMM using the lmer package. I
usually explore the assumption of linearity of continuous variables in the
logit of the outcome by creating 4 categories of the variable, performing a
bivariate logistic regression, and then plotting the coefficients of each
category against their mid points. That gives me a pretty good idea about the
linearity assumption and possible departures from it.
I know of people who create 0,1 dummy variables in order to relax the linearity
assumption. However, I've read that dummy variables are never needed (nor are
desireble) in R! Instead, one should make use of factors variable. That is much
easier to work with than dummy variables and the model itself will create the
necessary dummy variables.
Having said that, if my data violates the linearity assumption, does the use of
a factors for the variable in question helps overcome the lack of linearity?
Thanks in advance,
Luciano
Yahoo! Cocina
Encontra las mejores recetas con Yahoo! Cocina.
http://ar.mujer.yahoo.com/cocina/
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