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. >> >> Thank you. >> >> >> Lucas. >> >> [[alternative HTML version deleted]] >> >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> >> > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Peter Dalgaard, Professor Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
