Dear Axel,
If you look at the content of the list returned by glm.fit, you'll see that it
contains almost everything in a "glm" object, and what's needed to compute the
coefficient covariance matrix. Here's one way to do what you want (but note
that your example was faulty in that you didn't include the regression constant
in the call to glm.fit):
> set.seed(1)
> n <- 100
> x <- rnorm(n)
> y1 <- rnorm(n)
> y2 <- rbinom(n, 1, .25) # you never use this in your example
>
> M1 <- glm (y1 ~ x)
> M2 <- glm.fit(x = cbind(1, x), y = y1) # corrected
> class(M2) <- "glm"
> vcov(M1)
(Intercept) x
(Intercept) 0.009406535 -0.00126365
x -0.001263650 0.01160511
> vcov(M2)
x
0.009406535 -0.00126365
x -0.001263650 0.01160511
You may have a reason to use glm.fit in preference to glm, but I'm not sure why
you'd want to do that.
I hope this helps,
John
-----------------------------------------------
John Fox, Professor
McMaster University
Hamilton, Ontario, Canada
http://socserv.socsci.mcmaster.ca/jfox/
> -----Original Message-----
> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Axel
> Urbiz
> Sent: Tuesday, December 29, 2015 9:10 AM
> To: R-help@r-project.org
> Subject: [R] Extract Standard Errors of Model Coefficients
>
> Hello,
>
> Is it possible to extract or compute the standard errors of model
> coefficients from a glm.fit object? This can be easily done from a
> fitted glm object, but I need glm.fit.
>
>
> set.seed(1)
> n <- 100
> x <- rnorm(n)
> y1 <- rnorm(n)
> y2 <- rbinom(n, 1, .25)
>
> M1 <- glm (y1 ~ x)
> M2 <- glm.fit(x = x, y = y1)
> seCoef <- sqrt(diag(vcov(M1)))
> seCoef
>
> (Intercept) x
> 0.09698729 0.10772703
>
> Thank you,
> Axel.
> ______________________________________________
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