On May 29, 2011, at 5:10 AM, ashley wrote:
Thanks Josh,
This makes sense. The coefficient of one parameter is given in
reference to
another parameter. (Aha! The "reference" parameter.)
I'm still a little confused on the standard errors (SEs) and why
they change
too? Should I change the reference around until I find the best-
looking SEs?
Somehow that doesn't seem right...
If you choose a reference level that only has a few members, then all
the comparisons will reflect that data situation, i.e. the standard
errors for any contrast with a groups with scanty numbers will be large.
I am trying to eventually make statements like:
"Fish A showed a weak, yet positive, response to HS and the low SE
gives us
confidence in this association"
I didn't see any contrasts in your models that merited that
representation. In general, you should be using likelihood ratio tests
for inference, rather than Wald tests. The example you provide is one
good illustration why that is so.
--
David.
"Fish A showed a weak negative response to SH and the low SE gives us
confidence in this association (though less confidence than for HS)"
"Fish A showed a strong negative response to SS, however the SE is
very high
so we cannot say this with high certainty"
Do these sound like an accurate reflection of what the output is
saying? (I
know that "low SE" is arguable but...)
So then the SE for HH 923.60 (2nd run) or 0.2781 (1st run)?
Thank you!
Ashley
On Sat, May 28, 2011 at 11:49 PM, Joshua Wiley-2 [via R] <
ml-node+3558477-671379230-241...@n4.nabble.com> wrote:
Hi Ashley,
It does not look like you have done the wrong thing to me. The
results will be different because eacho f the parameter estimates is
now the change from SS to ___ instead of from HH to ____. In fact,
from your first table, you can calculate all the parameters in the
second. The intercept for SS as reference is:
(-5.2671) + (-18.2990) = -23.5661
the difference between SH and SS is:
(-0.5736) - (-18.2990)
[1] 17.7254
which is now the parameter estimate for SH in the SS as reference
model. You could go on in like fashion for the rest.
HTH,
Josh
On Sat, May 28, 2011 at 4:27 PM, ashley <[hidden email]<http://user/SendEmail.jtp?type=node&node=3558477&i=0
>>
wrote:
Hello list readers,
I am running a set of GLMs on fish spp presence/absence as a
function of
various habitat characteristics. My response is binomial and I
have four
predictors, three of which are categorical.
So, R takes one of my predictor-variables away to use as the
intercept
(the
first one alphabetically). However, I want to know the coefficient
and SE
of
this predictor. I tried relevel() and reran the model. Abbreviated
summary()
results for each run are below. The results seem drastically
different.
Have
I done the wrong thing?
(Below is a result from the model with only one predictor, to save
space
and
hassle.)
Thanks,
Ashley
#Default reference level = HH:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.2671 0.2781 -18.942
<2e-16 ***
raw.table$SubsComboHS 0.8127 0.6438 1.262 0.207
raw.table$SubsComboSH -0.5736 1.0393 -0.552 0.581
raw.table$SubsComboSS -18.2990 923.6023 -0.020 0.984
#Command used to change reference level:
raw.table$SubsCombo<-relevel(raw.table$SubsCombo, ref="SS")
#New reference level = SS:
Estimate Std. Error z value Pr(>|
z|)
(Intercept) -23.57 923.60 -0.026 0.980
raw.table$SubsComboHH 18.30 923.60 0.020 0.984
raw.table$SubsComboHS 19.11 923.60 0.021 0.983
raw.table$SubsComboSH 17.73 923.60 0.019 0.985
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