Sorry for this third posting - the second method is the same as the
first after all: the coefficients of the first linear model *is* a
linear transformation of the second. Just got confused with the
pasting, tis all.
On Oct 21, 2:51 pm, andrew wrote:
> Oh dear, that doesn't look right at all.
Oh dear, that doesn't look right at all. I shall have a think about
what I did wrong and maybe follow my own advice and consult the doco
myself!
On Oct 21, 2:45 pm, andrew wrote:
> The following is *significantly* easier to do than try and add in
> dummy variables, although the dummy variable a
The following is *significantly* easier to do than try and add in
dummy variables, although the dummy variable approach is going to give
you exactly the same answer as the factor method, but possibly with a
different baseline.
Basically, you might want to search the lm help and possibly consult a
On Oct 20, 2009, at 4:00 PM, Luciano La Sala wrote:
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
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 co
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