Trevor Wiens wrote:
On Thu, 10 Mar 2005 16:19:41 -0600
Frank E Harrell Jr [EMAIL PROTECTED] wrote:
The goodness of fit test only works on prespecified models. It is not
valid when stepwise variable selection is used (unless perhaps you use
alpha=0.5).
Perhaps I'm blind, but I can't find any
On Fri, 11 Mar 2005 07:32:30 -0500
Frank E Harrell Jr [EMAIL PROTECTED] wrote:
What I mean is the effective significance level for keeping a variable
in the model. Using AIC for one degree of freedom variables is
effectively using an alpha of 0.16 if I recall properly.
But I hope you got
I was unsure of what suitable goodness-of-fit tests existed in R for logistic
regression. After searching the R-help archive I found that using the Design
models and resid, could be used to calculate this as follows:
d - datadist(mydataframe)
options(datadist = 'd')
fit - lrm(response ~
On Thu, 10 Mar 2005, Trevor Wiens wrote:
I was unsure of what suitable goodness-of-fit tests existed in R for
logistic regression. After searching the R-help archive I found that
using the Design models and resid, could be used to calculate this as
follows:
d - datadist(mydataframe)
Trevor Wiens wrote:
I was unsure of what suitable goodness-of-fit tests existed in R for logistic
regression. After searching the R-help archive I found that using the Design
models and resid, could be used to calculate this as follows:
d - datadist(mydataframe)
options(datadist = 'd')
fit -
On Thu, 10 Mar 2005 22:36:09 +0100 (CET)
Roger Bivand [EMAIL PROTECTED] wrote:
From one geographer to another, and being prepared to bow to
better-founded explanations, you seem to have included a variable - the
offending variable slr_mean - that is very highly correlated with another.
On Thu, 10 Mar 2005 16:19:41 -0600
Frank E Harrell Jr [EMAIL PROTECTED] wrote:
The goodness of fit test only works on prespecified models. It is not
valid when stepwise variable selection is used (unless perhaps you use
alpha=0.5).
Perhaps I'm blind, but I can't find any reference to an
