[R] regression with proportion data

[Doran, Harold]

The logit link requires a binary response variable, not a proportion. Better bet is a beta regression. You can also do some stuff with linear regression if you do some transformations, but linear regression assumes the outcome is any number on the real number line bounded between -Inf and Inf. 

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
> Behalf Of Georgiana May
> Sent: Monday, March 19, 2012 10:06 AM
> To: r-help at r-project.org
> Subject: [R] regression with proportion data
> 
> Hello,
> I want to determine the regression relationship between a proportion (y)
> and a continuous variable (x).
> Reading a number of sources (e.g. The R Book, Quick R,help), I believe I
> should be able to designate the model as:
> 
> model<-glm(formula=proportion~x, family=binomial(link="logit"))
> 
> this runs but gives me error messages:
> Warning message:
> In eval(expr, envir, enclos) : non-integer #successes in a binomial glm!
> 
> If I transform the proportion variable with log, it doesn't like that
> either (values not: 0<y<1)
> 
> I understand that the binomial function concerns successes vs. failures and
> can use those raw data, but the R Book and other sources seem to suggest
> that proportion data are usable as well.  Not so?
> 
> Thank you,
> Georgiana May
> 
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