On Mar 7, 2012, at 12:13 PM, Lucas wrote:
Thank you.
What could be a "User Error"? where could I be making a mistake?
Please consider what you are requesting. Attempting to enumerate the
possible errors would start with data input errors, progress through
transformational errors and finish up with analysis errors. It would
expand from that broad classification into something like the
accumulated sampling of user errors recorded in the 15 years of Rhelp
archives as a lower bound, which all users know is not inclusive since
we have made additional errors in the privacy of our own bedrooms.
Without a reproducible sample of data and code there can be no
specificity. The discipline of constructing a reproducible question to
R-help has led to quite a few solutions to my errors, (which were then
not posted.)
--
David.
I cannot use a lm because my data is not normal, is categorical (count
data). So my first option was Poisson, but had severe overdispersion
problems so I used Binomial Negative as an option.
Thank you for taking the time to answer my questions.
Lucas.
2012/3/7 peter dalgaard <pda...@gmail.com>
On Mar 7, 2012, at 15:02 , Lucas wrote:
Hi Pascal.
I applied my analysis in time. I have 25 fire seasons, each season
starts
on November and ends up on April (our summer)
Hey, why are you worrying about regression coefficients.
_Everything_ is
upside-down at your place... ;-)
, so I have used them as
independent observations. I know that assumption it could be
wrong, but
is
the only way I can use the information available.
As a general matter, there are three possibilities
1) User error
2) Method artifact
3) Counterintuitive (but true) relation
and you really need to keep the possibility of 3) in mind rather than
poking around hoping that the counterintuitive signs would go away by
themselves.
To investigate, I think I would make some stabs that try to get
closer to
the raw data. If you produce a plot showing that the average number
of
fires is increasing with temperature and a model fit with
temperature as
the only predictor apparently shows the opposite, then I'd suspect
a user
error causing coefficients not to mean what you think they mean.
Thank you.
2012/3/7 Pascal Oettli <kri...@ymail.com>
Hi Lucas,
Do you apply your analysis in time or in space?
Regards,
Pascal
----- Mail original -----
De : Lucas <lpchaparro...@gmail.com>
À : r-help@r-project.org
Cc :
Envoyé le : Mercredi 7 mars 2012 22h34
Objet : [R] Problems with generalized linear model (glm)
coefficients.
Hello to everyone.
I´m writing you because I´m feeling a bit frustrated with my
work.
My work consists in finding the relation between the amount of
fires
and
the weather, so, my response variable is the amount of fires in a
fire
season and the explanatory variables are the temperature, the
amount of
precipitation and the some others∑. my problem is this; I keep
getting
the
wrong sign in the coefficients estimated, I get a negative sign for
temperature and a positive sign for precipitation, which is
unreasonable,
the greater the temperature I would expect more fire, on the
contrary,
the
greater the precipitation I would expect less fires. So far I
have deal
with overdispersion, multicollinearity and the amount of zeroes
through
passing from Poisson to Negative Binomial and Hurdle. I believe
I have
used all my options and still have the wrong signs on my
coefficients.
Do I have more options? What does it mean that I keep getting those
signs?
If anyone could help me I would really appreciate it.
David Winsemius, MD
West Hartford, CT
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