# Hello,
# I have a data set that looks something like the following:
site<-c(rep('a',5),rep('b',2),rep('c',4),rep('d',11))
year<-c(1980, 1981, 1982, 1993, 1995, 1980, 1983, 1981, 1993, 1995,
1999, c(1980:1990))
count<-c(60,35,36,12,8,112,98,20,13,15,15,65,43,49,51,34,33,33,33,40,11,0)
data<-data.frame(site, year, count)
# > site year count
# 1 a 1980 60
# 2 a 1981 35
# 3 a 1982 36
# 4 a 1993 12
# .
# .
# .
# I ran a negative binomial glm using:
library(MASS)
model_a<-glm.nb(count~year, data=data)
# then extracted predicted values using:
py<-data.frame(seq(from=min(data$year), to=max(data$year), by=1))
p1<-predict(model_a, newdata=py, se.fit=TRUE, type='response')
f1<-p1$fit
plot(count~year, data=data)
lines(py$year, f1)
# Works perfectly.
# Now, I want to add site as a factor:
model_b<-glm.nb(count~year+as.factor(site), data=data)
# I have tried a couple different ways of predicting the values based
on model_b, but am having trouble.
py<-data.frame(seq(from=min(data$year), to=max(data$year), by=1))
p1<-predict(model_b, newdata=py, se.fit=TRUE, type='response')
# gives:
# >Error in model.frame.default(Terms, newdata, na.action = na.action,
xlev = object$xlevels) :
# variable lengths differ (found for 'as.factor(site)')
# In addition: Warning message:
# 'newdata' had 20 rows but variable(s) found have 22 rows
py<-expand.grid(data$site, data$year)
names(py)<-c('site','year')
p1<-predict(model_b, newdata=py)
# This works, but results in 484 values, and I can't plot a line over
my points.
# There is probably a simple solution, but I'm having trouble wrapping
my mind around it. Mind you, this is also a last
# minute change to my thesis, and I haven't slept in about three days.
# Any suggestions? I would be extremely grateful...
# Banging my head against the wall,
# A stressed out grad student
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