Hi Federico/Duncan/David/Bert, Thank you for your thoughtful comments and it's a great learning experience. I can see the critical point here is to find a right function to make the prediction. So I was thinking to start with "loess". However the predict.loess gave me an error as follows
Error in `$<-.data.frame`(`*tmp*`, "z", value = c(0.417071766265867, 0.433916401753023, : replacement has 20 rows, data has 400 Here is the code I tried. Thank you for your help again! Jun ===================================== x<-runif(20) y<-runif(20) z<-runif(20) library(rgl) plot3d(x,y,z) loess(z~x+y,control=loess.control(surface='direct'),span=.5,degree=2)->fit.loess xnew <- seq(min(x), max(x), len=20) ynew <- seq(min(y), max(y), len=20) df <- expand.grid(x = xnew, y = ynew) df$z<-predict(fit.loess,newdata=df) On Wed, Dec 4, 2013 at 4:37 PM, Bert Gunter <gunter.ber...@gene.com> wrote: > ... or, more simply > > lm(z ~ polym(x,y, degree=2) ) > > ?polym > > Cheers, > Bert > > > > > > On Wed, Dec 4, 2013 at 10:30 AM, David Winsemius <dwinsem...@comcast.net> > wrote: > > > > On Dec 4, 2013, at 8:56 AM, Duncan Murdoch wrote: > > > >> On 04/12/2013 11:36 AM, Jun Shen wrote: > >>> Hi, > >>> > >>> I have a dataset with two independent variables (x, y) and a response > >>> variable (z). I was hoping to generate a response surface by plotting > x, y, > >>> z on a three dimensional plot. I can plot the data with rgl.points(x, > y, > >>> z). I understand I may not have enough data to generate a surface. Is > there > >>> a way to smooth out the data points to generate a surface? Thanks a > lot. > >> > >> There are many ways to do that. You need to fit a model that predicts > z from (x, y), and then plot the predictions from that model. > >> An example below follows yours. > >>> > >>> Jun > >>> > >>> =========================== > >>> > >>> An example: > >>> > >>> x<-runif(20) > >>> y<-runif(20) > >>> z<-runif(20) > >>> > >>> library(rgl) > >>> rgl.points(x,y,z) > >> > >> Don't use rgl.points, use points3d() or plot3d(). Here's the full > script: > >> > >> > >> x<-runif(20) > >> y<-runif(20) > >> z<-runif(20) > >> > >> library(rgl) > >> plot3d(x,y,z) > >> > >> fit <- lm(z ~ x + y + x*y + x^2 + y^2) > >> > > > > Newcomers to R may think they would be getting a quadratic in x and y. > But R's formula interpretation will collapse x^2 to just x and then it > becomes superfluous and is discarded. The same result is obtained with z ~ > (x + y)^2). I would have thought that this would have been the code: > > > > fit <- lm(z ~ poly(x,2) +poly(y,2) + x:y ) > > > > > >> xnew <- seq(min(x), max(x), len=20) > >> ynew <- seq(min(y), max(y), len=20) > >> df <- expand.grid(x = xnew, > >> y = ynew) > >> > >> df$z <- predict(fit, newdata=df) > >> > >> surface3d(xnew, ynew, df$z, col="red") > > > > With the modified fitting formula one sees a nice saddle (for that > particular random draw) using rgl.snapshot(). > > > > > > The result with the earlier formula is a more restrained: > > > > > > > > > > Continued thanks to you Duncan for making this great tool available. > > > > -- > > David. > > > > > >> Duncan Murdoch > >>> > > > > 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. > > > > -- > > Bert Gunter > Genentech Nonclinical Biostatistics > > (650) 467-7374 > > ______________________________________________ > 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. > [[alternative HTML version deleted]] ______________________________________________ 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.
