R help - Feb 2014 - Fitting glm with maxit=0?

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? 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)
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

============================Xiaogang Su, Ph.D.
Associate Professor
Department of Mathematical Sciences
University of Texas at El Paso
500 W. University Ave.
El Paso, Texas 79968-0514
xsu@utep.edu
xiaogangsu@gmail.com
https://sites.google.com/site/xgsu00/

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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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