The minimum achievable level of significance is defined asthe minimum of
Prob(Y=y) over all y's. If I have GLM with a treatment and replicate and I
would like to find out how to compute the minimum achievable level of
significance for that GLM in R
For example, how do I do this for the following
the prediction for the hold-out data. Is there any
better way for cross-validation to learn a model on training data and test it
on test data in R?
Thanks,
Andra
--- On Mon, 8/22/11, Joshua Wiley jwiley.ps...@gmail.com wrote:
From: Joshua Wiley jwiley.ps...@gmail.com
Subject: Re: [R] GLM
Hi All,
I am trying to fit my data with glm model, my data is a matrix of size n*100.
So, I have n rows and 100 columns and my vector y is of size n which contains
the labels (0 or 1)
My question is:
instead of manually typing the model as
glm.fit = glm(y~ x[,1]+x[,2]+...+x[,100],
Hi Andra,
There are several problems with what you are doing (by the way, I
point them out so you can learn and improve, not to be harsh or rude).
The good news is there is a solution (#3) that is easier than what
you are doing right now!
1) glm.fit() is a function so it is a good idea not to
Peter Flom schrieb:
What do you mean by better?
Dear Peter
Thank you for your kind respons as well. You are right, we are in
constant debate whether it makes sense to remove variables (no matter
whether significant or not) from a total dataset which in itself has a
certain meaning and may not
Hi to all
I think this is more an general question to GLMs.
The result was better in all prior GLMs when I admitted the non
significant factors, but this is the first time that the result is worse
than before. What could be the reason for that?
Knut Krueger r...@krueger-family.de wrote
I think this is more an general question to GLMs.
The result was better in all prior GLMs when I admitted the non
significant factors, but this is the first time that the result is worse
than before. What could be the reason for that?
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