Re: [R] Plotting GAM fit using RGL
On Aug 16, 2013, at 10:55 AM, Duncan Murdoch wrote:
> On 13-08-15 1:15 PM, David Winsemius wrote:
>>
>> On Aug 15, 2013, at 2:23 AM, Lucas Holland wrote:
>>
>>> Hello all,
>>>
>>> I’ve fitted a bivariate smoothing model (with GAM) to some data, using two
>>> explanatory variables, x and y. Now I’d like to add the surface
>>> corresponding to my fit to a 3D scatterplot generated using plot3d().
>>>
>>> My approach so far is to create a grid of x and y values and the
>>> corresponding predicted values and to try to use surface3d with that grid.
>>>
>>> grid <- expand.grid(x = seq(-1,1,length=20),
>>>y = seq(-1,1, length=20))
>>>
>>> grid$z <- predict(fit.nonparametric, newdata=grid)
>>>
>>> surface3d(grid$x, grid$y, matrix(grid$z, nrow=length(grid$x),
>>> ncol=length(grid$y)))
>>>
>> ?surface3d
>> # Should be:
>>
>> surface3d( unique(grid$x), unique(grid$y),
>> z= matrix(grid$z, nrow=length(grid$x),
>> ncol=length(grid$y)))
>
> Or you could make x and y into matrices as well. In this case you'll get the
> same result, but if x or y weren't strictly increasing sequences, there'd be
> a difference.
Thanks for increasing my knowledge on this point. And for providing rgl to the
world. After looking at the Details section of the help page more carefully
than I had previously, I wondered: Has anyone ever done a projection of a Klein
bottle into rgl?
I didn't find one and my initial efforts with surface3d failed. (I managed to
crash that seesion with a misguided call to the global replace function.)
I did get success with misc3d's parameteric3d with a parametrisation attributed
to Robert Israel:
require(rgl); require(misc3d)
x = function(u,v){-(2/15)*cos(u)*(3*cos(v)-30*sin(u)+90*cos(u)^4*sin(u)-
60*cos(u)^6*sin(u)+5*cos(u)*cos(v)*sin(u))}
y =
function(u,v){-(1/15)*sin(u)*(3*cos(v)-3*cos(u)^2*cos(v)-48*cos(u)^4*cos(v)+48*cos(u)^6*cos(v)-60*sin(u)+5*cos(u)*cos(v)*sin(u)
-5*cos(u)^3*cos(v)*sin(u)
-80*cos(u)^5*cos(v)*sin(u)+80*cos(u)^7*cos(v)*sin(u))}
z = function(u,v){ (2/15)*(3+5*cos(u)*sin(u))*sin(v) }
parametric3d(x,y,z, seq(0,pi,length=100), seq(0,2*pi,length=100) )
--
David.
> Duncan Murdoch
>
>>
>>
>>> This however plots a number of surfaces that do not look like the fitted
>>> surface obtained by vis.gam(fit.nonparametric which actually looks a lot
>>> like the „truth“ (I’m using simulated data so I know the true regression
>>> surface).
>>>
>>> I think I’m using surface3d wrong but I can’t seem to spot my mistake.
>>
>>
>> Always look at the Arguments section of help pages carefully.
>>
>
David Winsemius
Alameda, CA, USA
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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Re: [R] Plotting GAM fit using RGL
On 13-08-15 1:15 PM, David Winsemius wrote: On Aug 15, 2013, at 2:23 AM, Lucas Holland wrote: Hello all, I’ve fitted a bivariate smoothing model (with GAM) to some data, using two explanatory variables, x and y. Now I’d like to add the surface corresponding to my fit to a 3D scatterplot generated using plot3d(). My approach so far is to create a grid of x and y values and the corresponding predicted values and to try to use surface3d with that grid. grid <- expand.grid(x = seq(-1,1,length=20), y = seq(-1,1, length=20)) grid$z <- predict(fit.nonparametric, newdata=grid) surface3d(grid$x, grid$y, matrix(grid$z, nrow=length(grid$x), ncol=length(grid$y))) ?surface3d # Should be: surface3d( unique(grid$x), unique(grid$y), z= matrix(grid$z, nrow=length(grid$x), ncol=length(grid$y))) Or you could make x and y into matrices as well. In this case you'll get the same result, but if x or y weren't strictly increasing sequences, there'd be a difference. Duncan Murdoch This however plots a number of surfaces that do not look like the fitted surface obtained by vis.gam(fit.nonparametric which actually looks a lot like the „truth“ (I’m using simulated data so I know the true regression surface). I think I’m using surface3d wrong but I can’t seem to spot my mistake. Always look at the Arguments section of help pages carefully. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Plotting GAM fit using RGL
On Aug 15, 2013, at 2:23 AM, Lucas Holland wrote: > Hello all, > > I’ve fitted a bivariate smoothing model (with GAM) to some data, using two > explanatory variables, x and y. Now I’d like to add the surface > corresponding to my fit to a 3D scatterplot generated using plot3d(). > > My approach so far is to create a grid of x and y values and the > corresponding predicted values and to try to use surface3d with that grid. > > grid <- expand.grid(x = seq(-1,1,length=20), >y = seq(-1,1, length=20)) > > grid$z <- predict(fit.nonparametric, newdata=grid) > > surface3d(grid$x, grid$y, matrix(grid$z, nrow=length(grid$x), > ncol=length(grid$y))) > ?surface3d # Should be: surface3d( unique(grid$x), unique(grid$y), z= matrix(grid$z, nrow=length(grid$x), ncol=length(grid$y))) > This however plots a number of surfaces that do not look like the fitted > surface obtained by vis.gam(fit.nonparametric which actually looks a lot like > the „truth“ (I’m using simulated data so I know the true regression surface). > > I think I’m using surface3d wrong but I can’t seem to spot my mistake. Always look at the Arguments section of help pages carefully. -- David Winsemius Alameda, CA, USA __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Plotting GAM fit using RGL
Hello all, I’ve fitted a bivariate smoothing model (with GAM) to some data, using two explanatory variables, x and y. Now I’d like to add the surface corresponding to my fit to a 3D scatterplot generated using plot3d(). My approach so far is to create a grid of x and y values and the corresponding predicted values and to try to use surface3d with that grid. grid <- expand.grid(x = seq(-1,1,length=20), y = seq(-1,1, length=20)) grid$z <- predict(fit.nonparametric, newdata=grid) surface3d(grid$x, grid$y, matrix(grid$z, nrow=length(grid$x), ncol=length(grid$y))) This however plots a number of surfaces that do not look like the fitted surface obtained by vis.gam(fit.nonparametric which actually looks a lot like the „truth“ (I’m using simulated data so I know the true regression surface). I think I’m using surface3d wrong but I can’t seem to spot my mistake. Thanks! __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
