On Feb 26, 2014, at 12:24 AM, Xiaogang Su wrote:
> Dear All,
>
> Does anyone know if there is any way to obtain the variance-covariance
> matrix for any arbitrarily given estimates with the glm() function?
>
> Here is what I really want. Given an arbitrary estimate (i.e., as starting
> points with the start= argument), the glm() function could return the
> corresponding variance-covariance matrix (or Hessian) and other quantities
> with no Netwon-Raphson iteration?
Not with an arbitrary start argument. You have offered a counter-example.
> This could have been done by setting
> maxit=0, but very unfortunately that is not an option in glm.control(). To
> illustrate the problem,
>
> mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv";)
> beta0 <- 1:3
> control0 <- glm.control(epsilon = 1e10, maxit = 0, trace = FALSE)
I admit to curiosity regarding how you interpreted the error message this
generates:
Error in glm.control(epsilon = 1e+10, maxit = 0, trace = FALSE) :
maximum number of iterations must be > 0
If you fix that problem you will be faced with another one induced by your
unrealistic starting values.
--
David.
> fit <- glm(admit ~ gre + gpa, data = mydata, family = "binomial",
> start=beta0, control=control0)
> summary(fit)$"cov.scaled"
>
> By the way, I am aware that I could directly compute the covariance matrix
> using the formula. I also know that I could extract the corresponding
> deviance by using the offset option.
>
> Any suggestion is greatly appreicated.
>
> Thanks,
> Xiaogang Su
>
> =============================
>
>
> [[alternative HTML version deleted]]
>
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David Winsemius
Alameda, CA, USA
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