... as would the loess predict() method do from a loess() fit in the base package.
I have used loess() for this purpose primarily to take advantage of its "robustness" capabilities. I hasten to add that the algorithm is not infallible in this regard, as it may not recover from a sufficiently bad set of (least squares)starting values, as ?loess explicitly says. It can also be slow. Cheers, Bert Bert Gunter Genentech Nonclinical Biostatistics (650) 467-7374 "Data is not information. Information is not knowledge. And knowledge is certainly not wisdom." Clifford Stoll On Tue, Nov 11, 2014 at 12:56 PM, Duncan Murdoch <[email protected]> wrote: > On 11/11/2014, 2:17 PM, Gyanendra Pokharel wrote: >> Hi R users, >> I am trying to plot two dimensional posterior likelihood surface. I have a >> data like >> >> para1 para2 likehood >> ....... ........ ........... >> ....... ........ ........... >> >> >> >> I looked at contour plot but it needs a shorted values of parameters and a >> matrix of likelihood values. Is there any way to get the plot? or how can I >> change my likelihood values to a matrix for the function "contour"? > > The interp() function in the akima package should be able to do what you > want. > > Duncan Murdoch > > ______________________________________________ > [email protected] 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. ______________________________________________ [email protected] 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.
