In summary.glm I'm trying to get a better feel for the z output.  The
following lines can be found in the function

 

1     if (p > 0) {

2        p1 <- 1:p

3        Qr <- object$qr

4        coef.p <- object$coefficients[Qr$pivot[p1]]

5        covmat.unscaled <- chol2inv(Qr$qr[p1, p1, drop = FALSE])

6        dimnames(covmat.unscaled) <- list(names(coef.p), names(coef.p))

7        covmat <- dispersion * covmat.unscaled

8        var.cf <- diag(covmat)

9        s.err <- sqrt(var.cf)

10      tvalue <- coef.p/s.err

11      dn <- c("Estimate", "Std. Error")

        

In line 10 where the tvalue is calculated I understand where s.err is
coming from and why it is used, but coef.p is throwing me for a loop.
First off what is:

 

coef.p <- object$coefficients[Qr$pivot[p1]]

 

giving us?  This should be the distance the sample is from the null
hypothesis so that when we divide by the standard error we get the t
value.  Then we would check distance to decide to reject or not.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 

Jacob Davis

Actuarial Analyst


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