rdoch
Thanks
*From:* Duncan Murdoch
*Sent:* Wednesday, July 29, 2020 16:04
*To:* Sebastien Bihorel ; J C Nash
; r-help@r-project.org
*Subject:* Re: [R] Nonlinear logistic regression fitting
Just a quick note about jargon: you are using the word "likelih
+ b*x/(c+x)". Correct?
Thanks
From: Duncan Murdoch
Sent: Wednesday, July 29, 2020 16:04
To: Sebastien Bihorel ; J C Nash
; r-help@r-project.org
Subject: Re: [R] Nonlinear logistic regression fitting
Just a quick note about jargon: you are using the word &q
b*x/(c+x)
From: Duncan Murdoch
Sent: Wednesday, July 29, 2020 16:04
To: Sebastien Bihorel ; J C Nash
; r-help@r-project.org
Subject: Re: [R] Nonlinear logistic regression fitting
Just a quick note about jargon: you are using the word "likelihood" in
mu = ~ p_a, pmu = c(a) )
emax_mod <- gnlm::bnlr( y = df[,2:3], link = 'logit', mu = ~ p_a +
p_b*x/(p_c+x), pmu = c(a, b, c) )
int_mod
emax_mod
From: J C Nash
Sent: Tuesday, July 28, 2020 14:16
To: Sebastien Bihorel ; r-help@r-project.org
Subject: R
My earlier posting on this thread was misleading. I thought the OP was trying to
fit a sigmoid to data. The problem is about fitting 0,1 responses.
The reproducible example cleared this up. Another strong demonstration that
a "simple reproducible example" can bring clarity so much more quickly tha
= df[,2:3], link = 'logit', mu = ~ p_a, pmu = c(a) )
emax_mod <- gnlm::bnlr( y = df[,2:3], link = 'logit', mu = ~ p_a +
p_b*x/(p_c+x), pmu = c(a, b, c) )
int_mod
emax_mod
From: J C Nash
Sent: Tuesday, July 28, 2020 14:16
To: Sebast
There is a large literature on nonlinear logistic models and similar
curves. Some of it is referenced in my 2014 book Nonlinear Parameter
Optimization Using R Tools, which mentions nlxb(), now part of the
nlsr package. If useful, I could put the Bibtex refs for that somewhere.
nls() is now getting
Dear Sebastien,
On 2020-07-28 14:13 +, Sebastien Bihorel wrote:
| Hi
|
| I need to fit a logistic regression
| model using a saturable
| Michaelis-Menten function of my
| predictor x. The likelihood could be
| expressed as:
|
| L = intercept + emax * x / (EC50+x)
|
| Which I guess coul
adas
> *Sent:* Tuesday, July 28, 2020 12:42
> *To:* Sebastien Bihorel ;
> r-help@r-project.org
> *Subject:* Re: [R] Nonlinear logistic regression fitting
> Hello,
>
> glm might not be the right tool for the MM model but nls is meant to fit
> non-linear models.
> A
From: Rui Barradas
Sent: Tuesday, July 28, 2020 12:42
To: Sebastien Bihorel ;
r-help@r-project.org
Subject: Re: [R] Nonlinear logistic regression fitting
Hello,
glm might not be the right tool for the MM model but nls is meant to fit
non-linear models.
And, after an on-line search, there
Hello,
glm might not be the right tool for the MM model but nls is meant to fit
non-linear models.
And, after an on-line search, there is also package drc, function drm.
I will use the data and examples in the links below. (The second gave me
right, it uses nls.)
#install.packages("drc")
l
for search engine FAQ!
>
> Thank anyways
>
>
> From: Bert Gunter
> Sent: Tuesday, July 28, 2020 11:12
> To: Sebastien Bihorel
> Cc: r-help@r-project.org
> Subject: Re: [R] Nonlinear logistic regression fitting
>
> Search!
> ... for "nonline
Subject: Re: [R] Nonlinear logistic regression fitting
You said:
"As far as I know (please, correct me if I am wrong), fitting such a model is
to not doable with glm, since the function is not linear."
My reply responded to that.
AFAIK, opinions on packages are off topic
> *Sent:* Tuesday, July 28, 2020 11:12
> *To:* Sebastien Bihorel
> *Cc:* r-help@r-project.org
> *Subject:* Re: [R] Nonlinear logistic regression fitting
>
> Search!
> ... for "nonlinear logistic regression" at rseek.org.
>
> Bert Gunter
>
> "The trouble
11:12
To: Sebastien Bihorel
Cc: r-help@r-project.org
Subject: Re: [R] Nonlinear logistic regression fitting
Search!
... for "nonlinear logistic regression" at rseek.org<http://rseek.org>.
Bert Gunter
"The trouble with having an open mind is that people keep coming along
Search!
... for "nonlinear logistic regression" at rseek.org.
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 Tue, Jul 28, 2020 at 7:25 AM Sebastien Bihorel via
Hi
I need to fit a logistic regression model using a saturable Michaelis-Menten
function of my predictor x. The likelihood could be expressed as:
L = intercept + emax * x / (EC50+x)
Which I guess could be expressed as the following R model
~ emax*x/(ec50+x)
As far as I know (please, correct
17 matches
Mail list logo
