No, i didnt get that warning ("In eval(expr, envir, enclos) ... : non-integer #successes in a binomial glm!") because i used the continuous bounded variable weighted by the effort.
This is, my formula was something like:
 
gam(SER_CD ~ s(DEP)+s(SST)+s(CLA)+s(SSH)+s(WST), weights=EFFORT, data=CD01, family=binomial(link="logit"))
 
Do you still think it is preferable to use a beta / gamma / quasi-poisson distribution?
 
And about the negative value of the UBRE score i still can´t understand...i dont have missing nor negative values in the variables.
Any other suggestion? Which kind of issue might be causing this problem of getting a negative UBRE?
 
Thnaks once again!
 
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Message: 92
Date: Fri, 22 Aug 2008 09:48:02 +0200 (CEST)
From: "Fabrizio Cipollini" <cipollini@ds.unifi.it>
Subject: Re: [R] GAM-binomial logit link
To: r-help@r-project.org
Message-ID: <1661.87.5.105.223.1219391282.squirrel@ds.unifi.it>
Content-Type: text/plain;charset=iso-8859-1

I guess safer to use the option family = quasibinomial since, with a continuous [0,1]-response, the empirical (conditional) variance of y can significantly differ from the corresponding theoretical binomial variance.

You can find larger references in
Papke - Wooldridge (1996), 'Journal of Applied Econometrics' (vol. 11, p.
619-632).

Hmmm... On the basis of the UBRE formula within gam{mgcv}, UBRE scores should be nonnegative. Please inspect the values of the single elements inside the formula for discovering possible problems.

Fabrizio Cipollini
 
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Message: 54
Date: Thu, 21 Aug 2008 20:09:08 +0000
From: Monica Pisica <pisicandru@hotmail.com>
Subject: Re: [R] GAM-binomial logit link
To: <r-help@r-project.org>
Message-ID: <BAY104-W27C9A6D9A85AE284C31366C36B0@phx.gbl>
Content-Type: text/plain; charset="iso-8859-1"


Hi,

I am not sure it is the best to use a binomial distribution for a continuous bounded variable. A beta distribution would be more appropriate, although I don't know how to define one for the gam() function. On the other hand beta distribution is closely linked to the gamma distribution so maybe you can use it to define a beta family for the gam() function.

Some info about beta distribution: http://www.stat.purdue.edu/~jrnolan/portfolio/the_big_ten/beta.pdf

Also, I am not very sure how you did a gam using binomial family without having your response data converted in 0 and 1. Didn't you get a warning saying that: Warning messages: 1: In eval(expr, envir, enclos) ... : non-intege[[elided Yahoo spam]]

Maybe you can contact the author of the mgcv package. I am curious to see his response.

Sorry I cannot help much more,

Monica
 
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 Dear all,

I'm using a binomial distribution with a logit link function to fit a GAM model. I have 2 questions about it. First i am not sure if i've chosen the most adequate distribution. I don't have presence/absence data (0/1) but I do have a rate which values vary between 0 and 1. This means the response variable is continuous even if within a limited interval. Should i use binomial?

Secondly, in the numerical output i get negative values of UBRE score. I would like to know if one should consider the lowest absolute value or the lowest real value to select the best model.

Thank you in advance for your help.
Mar


      
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