It could be that for some levels of your independent factor variables (WS,
SS), the response is either all zeroes or all ones. Or, for your
continuous independent variables (DV, DS), there is a clean break between
the zeroes and ones. For example, if all the CIDs are one when DS <= 18
but all
Hello,
When I run the following glm model:
modelresult=glm(CID~WS+SS+DV+DS, data=kimu, family=binomial)
I get the following warning messages:
1: glm.fit: algorithm did not converge
2: glm.fit: fitted probabilities numerically 0 or 1 occurred
What I am trying to do is model my response variable
On 17/05/2012 20:35, Sophie Baillargeon wrote:
Hi,
When I run the following code :
Y<- c(rep(0,35),1,2,0,6,8,16,43)
cst<- log(choose(42, 42:1))
beta<- 42:1
tau<- (beta^2)/2
fit<- glm(formula = Y ~ offset(cst) + beta + tau, family = poisson)
fit
fit$converged
glm prints a warning saying that th
Hi Sophie
It helps if you do some detective work
Try
fit1 <- glm(formula = Y ~ offset(cst) + beta + tau, family =
poisson,trace = T, maxit = 200)
and compare
Regards
Duncan
Duncan Mackay
Department of Agronomy and Soil Science
University of New England
Armidale NSW 2351
Email: home: mac...
Hi,
When I run the following code :
Y <- c(rep(0,35),1,2,0,6,8,16,43)
cst <- log(choose(42, 42:1))
beta <- 42:1
tau <- (beta^2)/2
fit <- glm(formula = Y ~ offset(cst) + beta + tau, family = poisson)
fit
fit$converged
glm prints a warning saying that the algorithm did not converge.
However, fit$
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