Displaying 5 results from an estimated 5 matches for "ehg136".
2011 Jun 16
0
Update: Is there an implementation of loess with more than 3 parametric predictors or a trick to a similar effect?
...s for four or more offset groups
with the error message
"Error in simpleLoess(y, x, w, span, degree, parametric, drop.square,
normalize,? :
? only 1-4 predictors are allowed"
See comment #1
I tried overriding the restriction (see lower section of code example) and got
"k>d2MAX in ehg136.? Need to recompile with increased dimensions."
How easy would that be to do? I cannot find a definition of d2MAX
anywhere, and it seems this might be hardcoded -- the error is
apparently triggered by line #1359 in loessf.f
????? if(k .gt. 15)?? call ehg182(105)
Alternatively, does anyone kn...
2011 Jun 11
0
Is there an implementation of loess with more than 3 parametric predictors or a trick to a similar effect? [re-posting as plain text to pass char-set filter]
...offset groups.
Unfortunately, the loess call fails for four or more offset groups
with the error message
"Error in simpleLoess(y, x, w, span, degree, parametric, drop.square,
normalize,? :
? only 1-4 predictors are allowed"
I tried overriding the restriction and got
"k>d2MAX in ehg136.? Need to recompile with increased dimensions."
How easy would that be to do? I cannot find a definition of d2MAX
anywhere, and it seems this might be hardcoded -- the error is
apparently triggered by line #1359 in loessf.f
????? if(k .gt. 15)?? call ehg182(105)
Alternatively, does anyone kn...
2011 Jun 24
2
Is there an implementation of loess with more than 3 parametric ...
Dear John,
> I suggest that you look at the abilities of the mgcv package.
> There are notes of mine at
>
> http://www.maths.anu.edu.au/%7Ejohnm/r-book/xtras/autosmooth.pdf
>
> that may help you get started.
Thank?you very much for the suggestion and the link to your write-up,
it was indeed very helpful!
I have experimented with this library for a while now and am really
happy
2011 Jun 11
1
Is there an implementation loess with more than 4 parametric predictors or a trick to similar effect?
Dear R experts,
I have a problem that is a related to the question raised in this earlier
post
https://stat.ethz.ch/pipermail/r-help/2007-January/124064.html
My situation is different in that I have only 2 predictors (coordinates x,y)
for local regression but a number of global ("parametric") offsets that I
need to consider.
Essentially, I have a spatial distortion overlaid over a
2002 Sep 15
7
loess crash
Hi,
I have a data frame with 6563 observations. I can run a regression with
loess using four explanatory variables. If I add a fifth, R crashes. There
are no missings in the data, and if I run a regression with any four of the
five explanatory variables, it works. Its only when I go from four to five
that it crashes.
This leads me to believe that it is not an obvious problem with the data,