On Mar 13, 2012, at 9:38 AM, D_Tomas wrote:
Dear userRs,
when applied the summary function to a glm fit (e.g Poisson) the
parameter
table provides the categorical variables assuming that the first level
estimate (in alphabetical order) is 0.
Not really. It returns an estimate for the contrast of two Poisson
parameters which have support on the real line. This is not really the
correct list for fixing your misconceptions about GLMs. Your
misconceptions are more of a conceptual character rather than an R
coding problem. Maybe you should post follow-ups to:
stats.stackexchange.com
What is the standard error for that variable then?
It (meaning I assume the coefficient estimate) is not a variable, at
least not in the sense of being a data element.
Are the standard errors calculated assuming a normal distribution?
The standard errors are simply the square roots of the diagonals of
the variance-covariance matrix (estimated from the deviations on the
specified scale of the data from a best fit in a modeling framework).
The assumption one makes when turning this into a confidence interval
is that _parameters_ are approximately normally distributed using a
glm method. You do not necessarily need to accept this method. The
'confint' function in MASS will return CI's based on the profile
likelihood.
Many thanks,
--
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