[R] logistic regression weights problem

[Federico Calboli]

Hi All,

I have a problem with weighted logistic regression. I have a number of
SNPs  and a case/control scenario, but not all genotypes are as
"guaranteed" as others, so I am using weights to downsample the
importance of individuals whose genotype has been heavily "inferred".

My data is quite big, but with a dummy example:

> status <- c(1,1,1,0,0)
> SNPs <- matrix( c(1,0,1,0,0,0,0,1,0,1,0,1,0,1,1), ncol =3)
> weight <- c(0.2, 0.1, 1, 0.8, 0.7)
> glm(status ~ SNPs, weights = weight, family = binomial)

Call:  glm(formula = status ~ SNPs, family = binomial, weights = weight)

Coefficients:
(Intercept)        SNPs1        SNPs2        SNPs3
     -2.079       42.282      -18.964           NA

Degrees of Freedom: 4 Total (i.e. Null);  2 Residual
Null Deviance:      3.867
Residual Deviance: 0.6279       AIC: 6.236
Warning messages:
1: non-integer #successes in a binomial glm! in: eval(expr, envir,
enclos)
2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y
= Y, weights = weights, start = start, etastart = etastart,

NB I do not get warning (2) for my data so I'll completely disregard it.

Warning (1) looks suspiciously like a multiplication of my C/C status by
the weights... what exacly is glm doing with the weight vector? 

In any case, how would I go about weighting my individuals in a logistic
regression?

Regards,

Federico Calboli


-- 
Federico C. F. Calboli
Department of Epidemiology and Public Health
Imperial College, St Mary's Campus
Norfolk Place, London W2 1PG

Tel  +44 (0)20 7594 1602     Fax (+44) 020 7594 3193

f.calboli [.a.t] imperial.ac.uk
f.calboli [.a.t] gmail.com