Thank you for your advices.
I will try even increased "gamma" values, and all-out cross-validations.
2007/10/3, Frank E Harrell Jr <[EMAIL PROTECTED]>:
> Ariyo Kanno wrote:
> > Sorry, let me fix 1 sentence.
> >
> > "Here I try to mean by "overfitting" that GCV was significantly SMALLER
> > than t
Ariyo Kanno wrote:
> Sorry, let me fix 1 sentence.
>
> "Here I try to mean by "overfitting" that GCV was significantly SMALLER
> than the mean square error of prediction of the validation data, which
> was randomly selected and not used for regression."
>
>> Thank you for valuable advices.
If yo
>
> "Here I try to mean by "overfitting" that GCV was significantly SMALLER
> than the mean square error of prediction of the validation data, which
> was randomly selected and not used for regression."
--- so you could try increasing gamma until this is no longer the case.
--
> Simon Wood, Ma
Sorry, let me fix 1 sentence.
"Here I try to mean by "overfitting" that GCV was significantly SMALLER
than the mean square error of prediction of the validation data, which
was randomly selected and not used for regression."
> Thank you for valuable advices.
> I'm sorry Dr. N. Wood that by mistak
Thank you for valuable advices.
I'm sorry Dr. N. Wood that by mistake I sent this reply firstly to
your personal e-mail address.
I will use the "min.sp" argument when the data size is very small. I'd
like to know if there is any criteria for selecting "min.sp."
I compared gamma=1.0 and 1.4, and I
On Wednesday 03 October 2007 10:49, Ariyo Kanno wrote:
> I appreciate your quick reply.
> I am using the model of the following structure :
>
> fit <- gam(y~x1+s(x2))
>
> ,where y, x1, and x2 are quantitative variables.
> So the response distribution is assumed to be gaussian(default).
>
> Now I un
I appreciate your quick reply.
I am using the model of the following structure :
fit <- gam(y~x1+s(x2))
,where y, x1, and x2 are quantitative variables.
So the response distribution is assumed to be gaussian(default).
Now I understand that the data size was too small.
Thank you.
Best Wishes,
A
What sort of model structure are you using? In particular what is the response
distribution? For poisson and binomial then overfitting can be a sign of
overdispersion and quasipoisson or quasibinomial may be better. Also I would
not expect to get useful smoothing parameter estimates from 10 data
Dear listers,
I'm using gam(from mgcv) for semi-parametric regression on small and
noisy datasets(10 to 200
observations), and facing a problem of overfitting.
According to the book(Simon N. Wood / Generalized Additive Models: An
Introduction with R), it is
suggested to avoid overfitting by infla
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