TLDR: No, there was no such change in R 4.2.0
> Ralf Goertz
> on Wed, 27 Apr 2022 10:27:33 +0200 writes:
> Hi,
> I just noticed that (with my version 4.2.0) it is no longer possible to
> use glm with family=binomial(link=identity). Why is that? It was
> possible
> Am Wed, 27 Apr 2022 10:27:33 +0200 schrieb Ralf Goertz
>
> > Hi,
> >
> > I just noticed that (with my version 4.2.0) it is no longer possible
> > to use glm with family=binomial(link=identity). Why is that? It was
> > possible with 4.0.x as a colleague of mine just confirmed. After all
> > it
Am Wed, 27 Apr 2022 10:27:33 +0200
schrieb Ralf Goertz :
> Hi,
>
> I just noticed that (with my version 4.2.0) it is no longer possible
> to use glm with family=binomial(link=identity). Why is that? It was
> possible with 4.0.x as a colleague of mine just confirmed. After all
> it is useful to
Hi,
I just noticed that (with my version 4.2.0) it is no longer possible to
use glm with family=binomial(link=identity). Why is that? It was
possible with 4.0.x as a colleague of mine just confirmed. After all it
is useful to compute risk differences with factors.
You should post on r-sig-geo, the list devoted to spatial data analysis,
rather than here.
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Thu, Jun 4, 2020 at
I did a regression analysis with categorical data with a glm model
approach, which worked fine. I have longitude and latitude coordinates for
each observation and I want to add their geographic spillover effect to the
model.
My sample data is structured:
Index DV IVI IVII IVIII IVIV Long Lat
1
> On Oct 16, 2018, at 12:33 PM, Neslin, Scott A.
> wrote:
>
> R-Help:
>
> We are working with your GLM R package. The Summary(Model) now gets printed
> by the program as one object and we want to put the coefficient columns into
> Excel. We took an initial stab at this by counting the
You can just use
'slotNames(modelname)
This will return sub objects for which names can be extracted
Eg
slotNames(modelname)
[1] "mfit" "model"
names(modelname@mfit)
names(modelname@model)
Will return all objects within the model including coed car R cover vcov as
applicable and you can store
The coefficients are best obtained as summary(Model)$coefficients.
This is a matrix can than be saved as a csv file and opened in excel
or other spreadsheet software.
HTH,
Peter
On Tue, Oct 16, 2018 at 9:44 AM Neslin, Scott A.
wrote:
>
> R-Help:
>
> We are working with your GLM R package. The
On 16/10/2018 12:33 PM, Neslin, Scott A. wrote:
R-Help:
We are working with your GLM R package. The Summary(Model) now gets printed by
the program as one object and we want to put the coefficient columns into
Excel. We took an initial stab at this by counting the number of characters
R-Help:
We are working with your GLM R package. The Summary(Model) now gets printed by
the program as one object and we want to put the coefficient columns into
Excel. We took an initial stab at this by counting the number of characters
occupied by each column. But we have now learned that
Thank you so much, Thierry!!
I will try that now and see if that solves the issue
Bests,
Paula
On Feb 26, 2018 03:02, "Thierry Onkelinx" wrote:
Dear Paula,
There are probably missing observations in your data set. Read the
na.action part of the glm help file.
Dear Paula,
There are probably missing observations in your data set. Read the
na.action part of the glm help file. na.exclude is most likely what you are
looking for.
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR
HI there
I am running this model in negative binomial regression, using glm.
I had no problems with running the model with a set of data, but now that
i'm trying to run if for new one. I always have this same error when
running the regression:
>
> #Run Regression
>
See ?effects
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Fri, Jan 12, 2018 at 10:44 AM, Cade, Brian wrote:
> I know I must
I know I must be missing something obvious, but checking help and googling
a bit did not turn up a useable answer. When I've estimated a glm() model
object (my example is with just identity link with gaussian family so I
could have used lm() instead), one of the terms returned in the model
object
This is a question at the border between stats and r.
When I do a glm with many potential effects, and select a model using
stepAIC, many independent variables are selected even if there are no
relationship between dependent variable and the effects (all are random
numbers).
Do someone has
> On 27 Mar 2017, at 17:23 , Gabrielle Perron
> wrote:
>
> Hi,
>
>
> This is my first time using this mailing list. I have looked at the posting
> guide, but please do let me know if I should be doing something differently.
Avoid sending in HTML. It's not
Hi Gabrielle,
With that number of binary predictors it would be no surprise if some
were linear combinations of others.
Jim
On Tue, Mar 28, 2017 at 2:23 AM, Gabrielle Perron
wrote:
> Hi,
>
>
> This is my first time using this mailing list. I have looked at the
Hi,
This is my first time using this mailing list. I have looked at the posting
guide, but please do let me know if I should be doing something differently.
Here is my question, I apologize in advance for not being able to provide
example data, I am using very large tables, and what I am
Your questions are basically statistical and therefore OT here,
although some kind soul may respond. I would strongly suggest that you
consult with a local statistical expert, as you seem to be out of your
depth statistically.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is
Dear colleagues,
I am analyzing a data set of 68 values (integers). In some treatments (exactly
6) the values are "zero". Because I record 0 in my measurement (or really a
small value below zero)
My experiment is designed in such a way that I record values for 6 treatments
at 2 times.
> On Feb 1, 2017, at 5:28 AM, CHIRIBOGA Xavier
> wrote:
>
> Dear colleagues,
>
>
> I am trying to perform a GLM. I tried again without using attach()...but
> still is not working.
>
> Do you have any idea to help me?
>
>
> Thank you again,
>
>
> Xavier
>
>
On 01/02/2017 8:28 AM, CHIRIBOGA Xavier wrote:
Dear colleagues,
I am trying to perform a GLM. I tried again without using attach()...but still
is not working.
Do you have any idea to help me?
Thank you again,
Xavier
a <- read.table(file.choose(), h<-T)
The "h<-T" argument doesn't
Dear colleagues,
I am trying to perform a GLM. I tried again without using attach()...but still
is not working.
Do you have any idea to help me?
Thank you again,
Xavier
a <- read.table(file.choose(), h<-T)
> head(a)
time treatment transinduc
11 CHA0+Db 1,0768
21
Embarrassing but that's true. I wrote 'binamial' instead of 'binomial'. I
tried now with the correct spelling and everything is ok, in fact.
> summary(GLM)
Call:
glm(formula = model, family = binomial(link = logit))
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)
>> And use the parameters returned by GLM to contruct an equation for the
>> regression model:
>>
>> model.eq = -0.446078 + 0.267673*x - 0.014577*I(x^2)
>
> ## Not what I got with your data. I got:
>
> Coefficients:
> (Intercept)x I(x^2)
> -18.5750 5.0403 -0.2845
Inline.
-- Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Wed, Aug 31, 2016 at 10:03 AM, Anderson Eduardo
wrote:
> Hello
>
> I
Hello
I have started to work with GLM and I am facing the following problem:
If I take:
y = c(0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
x = 1:18
model = y ~x + I(x^2)
GLM = glm(model, family=binamial(link = logit))
And use the parameters returned by GLM to contruct an equation for
Hi all,
My problem is the following.
Suppose I have a dataset with observations Y and explanatory variables X1, ...,
Xn, and suppose one of these explanatory variables is geographical area (of
which there are ten, j=1,...,10). For some observations I know the area, but
for others it is
> On Dec 29, 2015, at 5:33 AM, Frank van Berkum
> wrote:
>
> Hi all,
> My problem is the following.
> Suppose I have a dataset with observations Y and explanatory variables X1,
> ..., Xn, and suppose one of these explanatory variables is geographical area
> (of
On 30/12/15 02:33, Frank van Berkum wrote:
Hi all, My problem is the following. Suppose I have a dataset with
observations Y and explanatory variables X1, ..., Xn, and suppose one
of these explanatory variables is geographical area (of which there
are ten, j=1,...,10). For some observations I
Thankyou all by the comments and sorry for not sending in the adequate
format. I don't have the chance to make txt archives as open office doesn't
do it. I'm attaching the data in excel. Please let me know if this is ok.
The graph that is wierd to me is the BBSA vs AMGP. It is supposed that AMGP
Inline.
-- Bert
Bert Gunter
Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom.
-- Clifford Stoll
On Thu, Aug 20, 2015 at 10:47 PM, Peter Langfelder
peter.langfel...@gmail.com wrote:
On Thu, Aug 20, 2015 at 10:04 PM, Bert Gunter
Thanks for the correction, I learned something new.
Peter
On Fri, Aug 21, 2015 at 7:32 AM, Bert Gunter bgunter.4...@gmail.com wrote:
Inline.
-- Bert
Bert Gunter
Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom.
-- Clifford Stoll
On Thu,
.csv format is still not accepted by the server. When I say it needs to be a
.txt file I mean it it needs to be a .txt file. You need to change its
extension to .txt to prevent your mail client from labeling it as csv which is
a different type even though I, too, would have thought they
Thanks. Here is in csv format.
Cheers,
Joaquín.
2015-08-21 12:49 GMT-03:00 Don McKenzie d...@uw.edu:
You can save to .csv from OpenOffice.
Sent from my iPad
On Aug 21, 2015, at 4:45 AM, Joaquín Aldabe joaquin.ald...@gmail.com
wrote:
Thankyou all by the comments and sorry for not
On Aug 19, 2015, at 8:54 AM, Joaquín Aldabe joaquin.ald...@gmail.com wrote:
Dear All, I´m running a glm with poisson errors and have a doubt when
ploting the predicted values. One of my variables has a positive slope in
the summary output, but when I plot the predicted values on the
Inline.
Cheers,
Bert
Bert Gunter
Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom.
-- Clifford Stoll
On Thu, Aug 20, 2015 at 9:06 PM, David Winsemius dwinsem...@comcast.net wrote:
On Aug 19, 2015, at 8:54 AM, Joaquín Aldabe
On Thu, Aug 20, 2015 at 10:04 PM, Bert Gunter bgunter.4...@gmail.com wrote:
I noticed you made two data-frames, ‘my4s' and ‘my4S'. The `my4S` was built
with `cbind` which would create a matrix (probably a character matrix)
rather than a data frame.
False. There is a data.frame method for
Dear All, I´m running a glm with poisson errors and have a doubt when
ploting the predicted values. One of my variables has a positive slope in
the summary output, but when I plot the predicted values on the original
plot it draws a line with negative slope. I appreciate your comments on
this and
matthewjones43 matthew.jones at kellogg.ox.ac.uk writes:
Hi, I am not a statistician and so I am sure whatever it is I
am doing wrong
must be an obvious error for those who are...Basically I can
not understand
why I get NA for variable 'CDSTotal' when running a glm?
Does anyone have an
Hi, I am not a statistician and so I am sure whatever it is I am doing wrong
must be an obvious error for those who are...Basically I can not understand
why I get NA for variable 'CDSTotal' when running a glm? Does anyone have an
idea of why this might be happening?
Call: glm(formula =
On Jul 21, 2015, at 7:30 PM, Rolf Turner r.tur...@auckland.ac.nz wrote:
Psigh! Why do people think that it is perfectly OK to undertake statistical
analyses without knowing or understanding any statistics?
(I guess it's slightly less dangerous than undertaking to do your own wiring
Psigh! Why do people think that it is perfectly OK to undertake
statistical analyses without knowing or understanding any statistics?
(I guess it's slightly less dangerous than undertaking to do your own
wiring without knowing anything about being an electrician, but still )
However, to
For anyone who is looking for an answer to this in the future...
I went for imputation. It's a way of filling in missing variables based
off of what you see elsewhere in the data.
Myself, I simply took a sample of the categorical from the rest of the test
set. Some may argue that this is
Hi thuksu,
Would defining the factor in your training set with all the levels
that occur in the test set solve the problem? That is, there would be
at least one factor level in the training set even though there were
no instances of that factor.
Jim
On Thu, Apr 30, 2015 at 8:05 AM, thuksu
Hi, Thanks for the reply!
I did try this...
# res is a data frame
levels(res$mytypeid.f) - c(levels(res$mytypeid.f),mynewlevel)
logreg - glm(yesno ~ mytypeid.f + amount, data=res, family=binomial)
exp(coef(logreg))
# this result shows that the new level is not included in the regression.
it's
My training set and my test set have some factor levels that are
different It's rare, but it occurs.
What is a good way for dealing with this?
I don't want to throw away the entire row from the data frame, because there
is some valuable information in there.
Is there some way to say
Apologies for cross-posting
We would like to announce the following statistics course in Palm Cove,
Australia.
Course1: GLM with R (Bayesian and frequentist)
Location: Palm Cove, Australia
Date: 11-14 August 2015
Price: 475 GBP
Course website:
I am following the example in the vignette for hdlm (p. 19), but I cannot
get it to to fit a logistic. For those who don't know the package, it
allows one to fit high dimensional data where the number of variables may
exceed the number of cases.
library(hdlm)
LMFUN - function(x,y) return(glm(y ~
wrong list.
Post on stats.stackexchange.com
-- Bert
Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374
Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom.
Clifford Stoll
On Sat, Jan 24, 2015 at 1:53 AM, Mauricio Gomes
I have a strange question concerning the fit of a Gamma generalized linear
model with glm (and further using gamma.shape to measure the shape
parameter).
Actually, I started with rgamma to generate some random vectors because I
wanted to play around with various conditions (and become familiar
Wim Kreinen wkreinen at gmail.com writes:
[snip]
Actually, I started with rgamma to generate some random vectors because I
wanted to play around with various conditions (and become familiar with
gamma shape). But before you can start with gamma shape you need to have a
glm object.
I think you are looking for
~ Region + Region:Helpers - 1
a.k.a.
~ Region/Helpers - 1
Notice that these are actually the same model as your glm3 (and also as
~Region*Helpers), only the parametrization differs. The latter includes an
overall Helpers term so that the interaction coefficients
Hi all,
I have a large set of data that looks something like this, although
this data frame is much smaller and includes made up numbers to make
my question easier.
x.df - data.frame(Region = c(A, A, A, A, A, B, B, B, B,
B, B, C, C, C, C), Group_ID = c(1:15), No_Offspring = c(3, 0, 4,
2, 1,
Dear honorable list-members,
I know how to fit a truncated lognormal distribution (or Gaussian)
(example here:
http://max2.ese.u-psud.fr/epc/conservation/Girondot/Publications/Blog_r/Entrees/2012/5/24_Adjust_a_truncated_lognormal_distribution.html
) but I would like to use it in the context of
I am wondering whether anyone could explain what'd be the difference between
running a 'generalized additive regression' versus 'generalized linear
regression' with splines.
The smooth terms in mgcv::gam are represented using *penalized*
regression splines, with the degree of penalization
Dear R-list,
I am wondering whether anyone could explain what'd be the difference between
running a 'generalized additive regression' versus 'generalized linear
regression' with splines.
Are they same models theoretically? My apologies if this is a silly question.
Any comments or direction
Dear All,
Please consider the snippet at the end of the email.
It is representative of the problems I am experiencing.
I am trying to use glm (without using the formula interface because the
original data is quite large) to model the response in a case where the
predictors are a mix of
On 26/02/14 01:40, Lorenzo Isella wrote:
Dear All,
Please consider the snippet at the end of the email.
It is representative of the problems I am experiencing.
I am trying to use glm (without using the formula interface because the
original data is quite large) to model the response in a case
I think you should have a look at svyglm() from the survey package.
My two cents
Le mercredi 05 février 2014 à 14:41 +1300, Rolf Turner a écrit :
You should direct your inquiry to R-help, not to me personally. I am
taking the liberty of cc-ing my reply back to the list.
I really haven't
On Feb 3, 2014, at 11:12 PM, IamRandom wrote:
I am running a simple example of GLM. If I include weights when
family=poisson then the weights are calculated iteratively and $weights and
$prior.weights return different values. The $prior.weights are what I
supplied and $weights are the
On 04/02/14 20:12, IamRandom wrote:
I am running a simple example of GLM. If I include weights when
family=poisson then the weights are calculated iteratively and
$weights and $prior.weights return different values. The $prior.weights
are what I supplied and $weights are the posterior
You should direct your inquiry to R-help, not to me personally. I am
taking the liberty of cc-ing my reply back to the list.
I really haven't the time at the moment to think the issue through
thoroughly, but off the top of my head: If you are going to use
weighted log likelihoods then any
I am running a simple example of GLM. If I include weights when
family=poisson then the weights are calculated iteratively and
$weights and $prior.weights return different values. The $prior.weights
are what I supplied and $weights are the posterior weights of the
IWLS. If I include weights
Dear all,
I want to fit some observations Y to a set of predictor variables X_i. (and
proceed with model selection with support of the second-order AIC
(AICc)...)
I (think I) know that the distribution of Y[i] is Gaussian and has a
variance, which is proportional to its value Y[i]. Say:
x1 -
Thanks for that. Still I am a bit confused. Please advice me.
Now, I have got minimal adequate model keeping all the those significant
predictors in the model which is shown below:
Coefficients:
Estimate Std. Error z value Pr(|z|)
(Intercept)
On Sep 15, 2013, at 2:15 AM, Lutfor Rahman wrote:
Thanks for that. Still I am a bit confused. Please advice me.
Now, I have got minimal adequate model keeping all the those significant
predictors in the model which is shown below:
Coefficients:
Estimate Std.
On Sep 13, 2013, at 9:38 AM, Lutfor Rahman wrote:
Dear forum members,
Please help me understanding significance value when GLM done in r.
After doing minimal adequate model, I have found a number of
independent
values which are significant. But doing their anova significant
values are
Dear forum members,
Please help me understanding significance value when GLM done in r.
After doing minimal adequate model, I have found a number of independent
values which are significant. But doing their anova significant values are
different. Please find my result following. Which
The model is including the extra terms, but they are folded into, well,
somewhere. The -1 only removes the intercept from the Time effect: the
other factors still have to be contrasts to something. Doesn't Crawley's
book explain this? If not, I wrote a paper a few years ago that might help:
Hi All,
I am working on re-analyzing per a reviewers request.
The goal of the project was to determine if the presence of predatory
fishes caused female crabs to delay the release of larvae. Number of
releases were recorded at three time periods: 1 hour before the
simulated tide, 3 hours
I did not think of something like try. I thouht that there should always
be a value if I do a logistic regression but sometimes the values are far
from being meaningful. So there is a cut-off. My plan was to change the
cut-off.
Thanks
Johannes
2013/7/15 Bert Gunter gunter.ber...@gene.com
I
On Jul 16, 2013, at 8:52 AM, Hermann Norpois wrote:
I did not think of something like try. I thouht that there should always
be a value if I do a logistic regression but sometimes the values are far
from being meaningful. So there is a cut-off.
That seems implausilbe. I think you are just
Hello,
I use glm within a function testing for the appearence of the coexistence
of (minor allels in a subset of) snps. And then I extract the
Pr(|z|)-value for the interaction. Principally it works but sometimes the
function stops because this value for the interaction is NA. For
instance,
I think what you want is
?try ##or
?tryCatch
## The second is more flexible but slightly more complicated.
to trap the error and perhaps refit the model without interaction?
Cheers,
Bert
On Mon, Jul 15, 2013 at 10:45 AM, Hermann Norpois hnorp...@gmail.com wrote:
Hello,
I use glm within a
Thanks a lot, Marc!
Dimitri
On Mon, Mar 11, 2013 at 4:28 PM, Marc Schwartz marc_schwa...@me.com wrote:
On Mar 11, 2013, at 1:46 PM, Dimitri Liakhovitski
dimitri.liakhovit...@gmail.com wrote:
Hello, and apologies for not providing an example. However, my question
is
more general.
I
Hello, and apologies for not providing an example. However, my question is
more general.
I have a lengthy function. This function is using another internal function
that modifies the data frame I am reading in. This internal function is
using the command model.frame (with data and weights inside)
On Mar 11, 2013, at 1:46 PM, Dimitri Liakhovitski
dimitri.liakhovit...@gmail.com wrote:
Hello, and apologies for not providing an example. However, my question is
more general.
I have a lengthy function. This function is using another internal function
that modifies the data frame I am
I am sure, that this is not a pure Poisson! Huge overdispersion!
You get inflated confidence intervals!
(although, the point estimates of the regression coefficients stay the same)
Try to look for the causes of overdispersion! It may be geteroscedastisity?
What is the nature of the response, is
On Thu, Jan 31, 2013 at 2:13 PM, Wim Kreinen wkrei...@gmail.com wrote:
Hello,
I have a question about modelling via glm.
I think you are way off track. Either the data, glm, or both, are not what
you think they are.
I have a dataset
skn300.tab - structure(list(n = 1:97, freq = c(0L, 0L,
Hello,
I have a question about modelling via glm. I have a dataset (see dput)
that looks like as if it where poisson distributed (actually I would
appreciate that) but it isnt because mean unequals var.
mean (x)
[1] 901.7827
var (x)
[1] 132439.3
Anyway, I tried to model it via poisson and
So, with R you use object[int,int] to select the rows and columns you want
to highlight. ([rows,columns]); what you've done here is asked it to apply
to rows 1 to 2 (1:2), across all columns. You'll want [,3:4] to specify two
particular columns. I'm not familiar enough with glm itself to provide
Hi Everyone,
I am new to R and am figuring my way around it. I am trying to determine the
relationship between A B, for each week of the year.
My dataset looks like:
YearWeekA B
19821 11.3 198.53
19822 14.4309.00
19823 23.2
Hello,
I'm fitting a logistic regression as follows and I have this error In
predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == :
prediction from a rank-deficient fit may be misleading
Codes:
##Training
fit- glm(Y.Binary.training~., family=binomial(link=logit),
It may be overkill, but you can specify the model pieces using the offset
function in the model, then the predictions work out (at least for my
simple trial case). Something like:
fit - glm( y ~ 0+offset(-1 + 2*x), family=binomial, data=data.frame(y=0,
x=0) )
predict( fit,
Dear All,
I know this may be a trivial question.
In the past I have used glm to make logistic regressions on data. The output
creates an object with the results of the logistic regression. This object can
then be used to make predictions.
Great.
I have a different problem. I need to make
Message -
From: Steve Lianoglou mailinglist.honey...@gmail.com
To: David Winsemius dwinsem...@comcast.net
Cc: Craig P O'Connell coconne...@umassd.edu, r-help@r-project.org
Sent: Tuesday, November 27, 2012 11:54:48 PM
Subject: Re: [R] GLM Coding Issue
Hi,
On Tuesday, November 27, 2012
It could be that for some levels of your independent factor variables (WS,
SS), the response is either all zeroes or all ones. Or, for your
continuous independent variables (DV, DS), there is a clean break between
the zeroes and ones. For example, if all the CIDs are one when DS = 18
but all
Dear all,
  I am having a recurring problem when I attempt to conduct a GLM. Here is
what I am attempting (with fake data):
First, I created a txt file, changed the directory in R (to the proper folder
containing the file) and loaded the file:
Hi,
Comments inline:
On Tue, Nov 27, 2012 at 1:00 PM, Craig P O'Connell
coconne...@umassd.edu wrote:
Dear all,
I am having a recurring problem when I attempt to conduct a GLM. Here is
what I am attempting (with fake data):
First, I created a txt file, changed the directory in R (to
On 2012-11-27 14:34, Steve Lianoglou wrote:
Hi,
Comments inline:
On Tue, Nov 27, 2012 at 1:00 PM, Craig P O'Connell
coconne...@umassd.edu wrote:
Dear all,
I am having a recurring problem when I attempt to conduct a GLM. Here is
what I am attempting (with fake data):
First, I created
On Nov 27, 2012, at 3:34 PM, Steve Lianoglou wrote:
Hi,
Comments inline:
On Tue, Nov 27, 2012 at 1:00 PM, Craig P O'Connell
coconne...@umassd.edu wrote:
Dear all,
I am having a recurring problem when I attempt to conduct a GLM.
Here is what I am attempting (with fake data):
First, I
Hi,
On Tuesday, November 27, 2012, David Winsemius wrote:
[snip]
`cbind`-ing doesn't make much sense here. What is your target (y)
variable here? are you trying to predict `avoid` or `noavoid` status?
Sorry, Steve. It does make sense. See :
?glm # First paragraph of Details.
Indeed ...
Hi Peter,
On Tue, Nov 27, 2012 at 8:05 PM, Peter Ehlers ehl...@ucalgary.ca wrote:
On 2012-11-27 14:34, Steve Lianoglou wrote:
[snip]
Steve:
re a matrix response: see MASS (the book, 4ed) page 191; also found
in the ch07.R file in the /library/MASS/scripts folder. I seem to
recall that this
Hello,
When I run the following glm model:
modelresult=glm(CID~WS+SS+DV+DS, data=kimu, family=binomial)
I get the following warning messages:
1: glm.fit: algorithm did not converge
2: glm.fit: fitted probabilities numerically 0 or 1 occurred
What I am trying to do is model my response
What kind of special magic does glm have?
I'm working on a logistic regression solver (L-BFGS) in c and I've been
using glm to check my results. I came across a data set that has a very
high condition number (the data matrix transpose the data matrix) that when
running my solver does not
On Nov 6, 2012, at 1:44 PM, Christopher A. Simon wrote:
What kind of special magic does glm have?
I'm working on a logistic regression solver (L-BFGS) in c and I've been
using glm to check my results. I came across a data set that has a very
high condition number (the data matrix
Hi
I am running some simulation in R after resampling from a huge data set 500
columns and 86759 rows and fit these two model and calculate R-square
fit - lm(Y~X,weights = Weights)
bhat - coef(fit)
Yhat - X%*%bhat
SST - sum((Y - mean(Y))^2)
SSE - sum((Y -
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