There is no restriction to integer weights in R. Here is a (silly) example.
library(MASS) fit1 <- glm(cbind(pm.y, pm.tot - pm.y) ~ density, binomial, data=rotifer) wt <- runif(20) update(fit1, weights=wt) or glm(pm.y/pm.tot ~ density, binomial, data=rotifer, weights=pm.tot) glm(pm.y/pm.tot ~ density, binomial, data=rotifer, weights=pm.tot*wt) which gives an harmless warning (not an error message). I use this sort of thing for multiple imputation quite frequently. On Sun, 28 Mar 2004, Marie-Pierre Sylvestre wrote: > Hi all, > > I want to use weights for a logistic regression. In SAS, all I have to > do is to specify my weight vector (they are fractions) and use proc > logistic on my binary output. That is all you do in R, too. See the example above. > When I tried to do the same in R, I got an error message because my > weights were not integer. Please read the posting guide and supply a reproducible example of how you got an *error* message here. > I understand that the weight option in R is to > be used when the dependent variable is a proportion so that the weight > is the total from which this proportion is derived. You `understand' incorrectly. Are you familiar with the theory of generalized linear models -- weights are part of the definition of a glm? > So what should I do if I want to use logistic regression but want to use > weight to give more importance to certain observations (e.g. > weight=0.87) and less to others (e.g. weight=.45) ? Should I > reparametrize everything in terms of counts or is there an easier way > out? -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
