At 04:46 27/08/2013, Murat Tasan wrote:
hi all -- i'm running into a strange problem that i can't seem to
easily get around, but i'm probably just missing something obvious.
I think you just subtract it from y. But perhaps I too am missing the obvious.
i have a model to which some data is fi
hi all -- i'm running into a strange problem that i can't seem to
easily get around, but i'm probably just missing something obvious.
i have a model to which some data is fit using glm with no intercept
term (using my data variables "x" and "y" and a specific link function
"mylink"):
On Jan 16, 2010, at 1:34 PM, Maurice Charbit wrote:
Hi,
See below I reply your message for <https://stat.ethz.ch/pipermail/r-help/2008-April/160966.html
>[R] predict.glm & newdata posted on Fri Apr 4 21:02:24 CEST 2008
You say it ##works fine but it does not: if you look at
wrote:
Hi,
See below I reply your message for <https://stat.ethz.ch/pipermail/r-help/2008-April/160966.html>[R] predict.glm & newdata posted on Fri Apr 4 21:02:24 CEST 2008
You say it ##works fine but it does not: if you look at the length of yhat2,
you will find 100 and not 200
Hi,
See below I reply your message for
<https://stat.ethz.ch/pipermail/r-help/2008-April/160966.html>[R] predict.glm &
newdata posted on Fri Apr 4 21:02:24 CEST 2008
You say it ##works fine but it does not: if you look at the length of yhat2,
you will find 100 and not 200 as ex
As for why it's not the other way around, well, if it had been, then you
could have asked the same question
...and come to think about it, it is rather convenient that it meshes
with the default ordering of levels in factor(x) is x is 0/1 or FALSE/TRUE.
--
O__ Peter Dalgaard
2009/7/10 Peter Dalgaard :
> Peter Schüffler wrote:
>>
>> Hi,
>>
>> I have a question about logistic regression in R.
>>
>> Suppose I have a small list of proteins P1, P2, P3 that predict a
>> two-class target T, say cancer/noncancer. Lets further say I know that I can
>> build a simple logistic re
Peter Schüffler wrote:
Hi,
I have a question about logistic regression in R.
Suppose I have a small list of proteins P1, P2, P3 that predict a
two-class target T, say cancer/noncancer. Lets further say I know that I
can build a simple logistic regression model in R
model <- glm(T ~ ., data=
On Jul 10, 2009, at 9:46 AM, Peter Schüffler wrote:
Hi,
I have a question about logistic regression in R.
Suppose I have a small list of proteins P1, P2, P3 that predict a
two-class target T, say cancer/noncancer. Lets further say I know
that I can build a simple logistic regression model
Hi,
I have a question about logistic regression in R.
Suppose I have a small list of proteins P1, P2, P3 that predict a
two-class target T, say cancer/noncancer. Lets further say I know that I
can build a simple logistic regression model in R
model <- glm(T ~ ., data=d.f(Y), family=binomial)
Anke,
mgcv:predict.gam certainly didn't produce `something like a
negative log-likelihood of occurrence', but is it possible that one of
your maps is on the probability scale and the other on the linear
predictor scale?
If you used predict.glm(model1,type="response"), but
predict.gam(model2
On Thu, 2009-06-18 at 13:23 -0600, Anke Konrad wrote:
> Hi all,
>
> I am currently trying to compare different plant occurrence prediction
> maps generated in R and exported into GRASS. One of these maps was
> generated from a glm fitted to some data, and subsequently applying this
> glm model
On Jun 18, 2009, at 3:23 PM, Anke Konrad wrote:
Hi all,
I am currently trying to compare different plant occurrence
prediction maps generated in R and exported into GRASS. One of these
maps was generated from a glm fitted to some data, and subsequently
applying this glm model to a wider
Hi all,
I am currently trying to compare different plant occurrence prediction
maps generated in R and exported into GRASS. One of these maps was
generated from a glm fitted to some data, and subsequently applying this
glm model to a wider region using predict.glm. The outcome here was a
prob
There is a bug in your code: Try
reg2<-predict.glm(reg1, se.fit=T, data.frame(male=1, edu=1,
married=1,inc=1, relig=1, YEAR=seq(1,33,1)), type="response")
You put type="response" into your newdata frame, so it wasn't visible to
predict.glm. So predict.glm assumed the default type, which is "link"
I have a puzzle
When I include an interaction in the model, many predicted probabilities are
above 1. Is that a problem with my model? I thought the predicted prob can't be
bigger than 1...
Any help would be really appreciated! Thanks!
K.
reg1<-glm(pyea~male+edu+married+inc+relig+factor(
gt; -
>
> -Ursprüngliche Nachricht-
> Von: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Im
> Auftrag von Tom Guston
> Gesendet: Friday, April 04, 2008 1:29 PM
> An: r-help@r-project.org
> Betreff: [R] predict.glm & newdata
>
>
> Hi a
hricht-
Von: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Im
Auftrag von Tom Guston
Gesendet: Friday, April 04, 2008 1:29 PM
An: r-help@r-project.org
Betreff: [R] predict.glm & newdata
Hi all -
I'm stumped by the following
mdl <- glm(resp ~ . , data = df, family=binomial, offset
Hi all -
I'm stumped by the following
mdl <- glm(resp ~ . , data = df, family=binomial, offset = ofst) WORKS
yhat <- predict(mdl) WORKS
yhat <- predict(mdl,newdata = df) FAILS
Error in drop(X[, piv, drop = FALSE] %*% beta[piv]) :
subscript out of bounds
I've tried without offset, quoting
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