Sorry I am a bit late to this discussion, but I can't see if you ever
got an answer. Anyhow, on your first question:
On 31/10/10 19:14, Lorenzo Isella wrote:
Dear All,
I have some questions about probit regressions.
I saw a nice introduction at
http://bit.ly/bU9xL5
and I mainly have two questions.
(1) The first is almost about data manipulation. Consider the
following snippet
[...]
mydata <- read.csv(url("http://www.ats.ucla.edu/stat/r/dae/binary.csv";))
names(mydata) <- c("outcome","x1","x2","x3")
myprobit <-
glm(mydata$outcome~mydata$x1+mydata$x2+as.factor(mydata$x3),
family=binomial(link="probit"))
[...]
#Now assume I can make a regression only on x1
myprobit2 <- glm(mydata$outcome~mydata$x1,
family=binomial(link="probit"))
[...]
Finally, I generate the data frame mydatanew (see some of its entries
below)
> mydatanew
x1 successes failures
1 220 0 1
2 300 1 2
3 340 1 3
4 360 0 4
5 380 0 8
[...................]
where for every value of x1 I count the number of 0 and 1 outcomes
(namely number of failures and number of successes). [...] How can I
actually feed R with mydatanew to perform again a logistic regression
on x1 only?
myprobit3 <- glm(cbind(successes, failures) ~ x1,
family=binomial(link="probit"), data = mydatanew )
all.equal(coef(myprobit2), coef(myprobit3), check.attributes = FALSE)
# [1] TRUE
Your second question we could discuss offline: it is not really an R
question, but you might want to have a look at something like the MNP
and perhaps survey packages.
Hope this helps a little.
Allan
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
