Prof Brian D Ripley
ripley at stats.ox.ac.uk
Sat Nov 24 09:57:40 CET 2001
On Sat, 24 Nov 2001, Troels Ring wrote:
> Dear friends. I have these well known data on horsekicks:
> years <- c(109, 65, 22, 3 , 1, 0)
> deaths <- 0:5
> and get a nice but wrong fit from
> summary(z1 <- glm(years~deaths,family=poisson))
> Can I take away the intercept ?
glm(years ~ deaths-1, family=poisson)
glm(years ~ 0+deaths, family=poisson)
*but* it would be nonsense. That's a log-linear model with mean forced to
be one at 0 deaths!
My (decades old) memory of that dataset is that the numbers are observed
frequencies for years with Y occurrences, so the correct model is
glm(deaths ~ 1, family=poisson, weights = years)
This fits a Poisson with mean 0.6099978 = exp(-0.4943) with fitted values
> round(dpois(0:5, 0.6099978)*sum(years), 2)
 108.67 66.29 20.22 4.11 0.63 0.08
a rather good match. But, the estimate of the mean is the mean of the
> weighted.mean(deaths, years)
so you don't need glm to do this.
Brian D. Ripley, ripley at stats.ox.ac.uk
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 272860 (secr)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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