On 12/01/2011 08:00 PM, Ben quant wrote:
The data I am using is the last file called l_yx.RData at this link (the
second file contains the plots from earlier):
http://scientia.crescat.net/static/ben/
The logistic regression model you are fitting assumes a linear
relationship between x and the
Thank you so much for your help.
The data I am using is the last file called l_yx.RData at this link (the
second file contains the plots from earlier):
http://scientia.crescat.net/static/ben/
Seems like the warning went away with pmin(x,1) but now the OR is over
15k. If I multiple my x's by 1000
On Dec 1, 2011, at 23:43 , Ben quant wrote:
> I'm not proposing this as a permanent solution, just investigating the
> warning. I zeroed out the three outliers and received no warning. Can someone
> tell me why I am getting no warning now?
It's easier to explain why you got the warning before.
I'm not proposing this as a permanent solution, just investigating the
warning. I zeroed out the three outliers and received no warning. Can
someone tell me why I am getting no warning now?
I did this 3 times to get rid of the 3 outliers:
mx_dims = arrayInd(which.max(l_yx), dim(l_yx))
l_yx[mx_dims
Oops! Please ignore my last post. I mistakenly gave you different data I
was testing with. This is the correct data:
Here you go:
> attach(as.data.frame(l_yx))
> range(x[y==0])
[1] 0.0 14.66518
> range(x[y==1])
[1] 0.0 13.49791
How do I know what is acceptable?
Also, here are the scr
Here you go:
> attach(as.data.frame(l_yx))
> range(x[y==1])
[1] -22500.746.
> range(x[y==0])
[1] -10076.5303653.0228
How do I know what is acceptable?
Also, here are the screen shots of my data that I tried to send earlier
(two screen shots, two pages):
http://scientia.crescat.n
On Dec 1, 2011, at 21:32 , Ben quant wrote:
> Thank you for the feedback, but my data looks fine to me. Please tell me if
> I'm not understanding.
Hum, then maybe it really is a case of a transition region being short relative
to the range of your data. Notice that the warning is just that: a
Thank you for the feedback, but my data looks fine to me. Please tell me if
I'm not understanding.
I followed your instructions and here is a sample of the first 500 values :
(info on 'd' is below that)
> d <- as.data.frame(l_yx)
> x = with(d, y[order(x)])
> x[1:500] # I have 1's and 0's dispers
On Dec 1, 2011, at 18:54 , Ben quant wrote:
> Sorry if this is a duplicate: This is a re-post because the pdf's mentioned
> below did not go through.
Still not there. Sometimes it's because your mailer doesn't label them with the
appropriate mime-type (e.g. as application/octet-stream, which is
Sorry if this is a duplicate: This is a re-post because the pdf's mentioned
below did not go through.
Hello,
I'm new'ish to R, and very new to glm. I've read a lot about my issue:
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
...including:
http://tolstoy.newcastle.e
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