R help - Sep 2003 - Logit reality check

Hello all:

I've been given the following data and have been asked to run a logit 
model using glm().  The variable, Y, is a proportion ranging from 0 to 
1, X is a covariate.  Without a base number of observations from which Y 
is computed as a proportion, I believe there is not sufficient information.

If I try the model below, R seems to grumble with a complaint.

glm(cbind(Y,1-Y) ~ X, family = binomial)

non-integer counts in a binomial glm! in: eval(expr, envir, enclos)

Am I correct to believe that more information is required?

Thanks,
ANDREW

             Y         X
 [1,]      0.40      41
 [2,]      0.19      69
 [3,]      0.20      60
 [4,]      0.29      85
 [5,]      0.14      48
 [6,]      0.20      32
 [7,]      0.11      69
 [8,]      0.28      17
 [9,]      0.35     115
[10,]     0.03      15
[11,]     0.14      11
[12,]     0.12      25

On Sun, 28 Sep 2003, Andrew Criswell wrote:
> Hello all:
>
> I've been given the following data and have been asked to run a logit
> model using glm().  The variable, Y, is a proportion ranging from 0 to
> 1, X is a covariate.  Without a base number of observations from which Y
> is computed as a proportion, I believe there is not sufficient information.
>
> If I try the model below, R seems to grumble with a complaint.
>
> glm(cbind(Y,1-Y) ~ X, family = binomial)
>
> non-integer counts in a binomial glm! in: eval(expr, envir, enclos)
>
> Am I correct to believe that more information is required?
Yes, probably.

If they are proportions without a well-defined denominator you may be able
to model them using family=quasi() and specifying the link and variance
function for a logistic regression model.  You'd need to look at what the
variance function actually is, though.

McCullagh & Nelder's book has an example using proportions of leaf
damage
that's a bit like this, although they end up using (mu(1-mu))^2 as the
variance function.

	-thomas

> > If I try the model below, R seems to grumble with a complaint.
> >
> > glm(cbind(Y,1-Y) ~ X, family = binomial)
> >
> > non-integer counts in a binomial glm! in: eval(expr, envir, enclos)
> >
For binomial models (as described in the help page), the response must be
either a factor or a n x 2 matrix with the numbers of successes of
failures, not the proportions.

g.,
David