On 12-09-27 05:34 PM, Bert Gunter wrote:
> Good point, Ben.
>
> I followed up my earlier reply offline with a brief note to Benedikt
> pointing out that "No" was the wrong answer: "maybe, maybe not" would
> have been better.
>
> Nevertheless, the important point here is that even if you do get
>
Good point, Ben.
I followed up my earlier reply offline with a brief note to Benedikt
pointing out that "No" was the wrong answer: "maybe, maybe not" would
have been better.
Nevertheless, the important point here is that even if you do get
convergence, the over-parameterization means that the est
Bert Gunter gene.com> writes:
>
> On Thu, Sep 27, 2012 at 12:43 PM, Benedikt Gehr
> ieu.uzh.ch> wrote:
> > now I feel very silly! I swear I was trying this for a long time and it
> > didn't work. Now that I closed R and restarted it it works also on my
> > machine.
> >
> > So is the only proble
On Thu, Sep 27, 2012 at 12:43 PM, Benedikt Gehr
wrote:
> now I feel very silly! I swear I was trying this for a long time and it
> didn't work. Now that I closed R and restarted it it works also on my
> machine.
>
> So is the only problem that my model is overparametrized with the data I
> have?
P
now I feel very silly! I swear I was trying this for a long time and it
didn't work. Now that I closed R and restarted it it works also on my
machine.
So is the only problem that my model is overparametrized with the data I
have? however shouldn't it be possible to fit an nls to these data?
On 27-09-2012, at 21:15, Benedikt Gehr wrote:
> thanks for your reply
>
> I agree that an lm model would fit just as well, however the expectation from
> a mechanistic point of view would be a non-linear relationship.
>
> Also when I "simulate" data as in
>
> y_val<-115-118*exp(-0.12*(seq(1,
thanks for your reply
I agree that an lm model would fit just as well, however the expectation
from a mechanistic point of view would be a non-linear relationship.
Also when I "simulate" data as in
y_val<-115-118*exp(-0.12*(seq(1,100)+rnorm(100,0,0.8)))
x_val<-seq(1:100)
plot(y_val~x_val)
sum
My guess:
You probably are overfitting your data. A straight line does about as
well as anything except for the 3 high leverage points, which the
minimization is probably having trouble with.
-- Bert
On Thu, Sep 27, 2012 at 10:43 AM, Benedikt Gehr
wrote:
> quantiles<-c(seq(.05,.95,0.05))
> sl
Hi
I would like to fit a non-linear regression to the follwoing data:
quantiles<-c(seq(.05,.95,0.05))
slopes<-c( 0.00e+00, 1.622074e-04 , 3.103918e-03 , 2.169135e-03 ,
9.585523e-04
,1.412327e-03 , 4.288103e-05, -1.351171e-04 , 2.885810e-04 ,-4.574773e-04
, -2.368968e-03, -3.104634e-03, -5
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