"Charles C. Berry" <[EMAIL PROTECTED]> wrote in news:[EMAIL PROTECTED]:
> On Wed, 16 Jan 2008, David Winsemius wrote: > >> I am a physician examining an NHANES dataset available at the >> NCHS website: http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/demo_d.xpt snip >> >> TC.ran <- exp(rnorm(400,1.5,.3)) >> HDL.ran <- exp(rnorm(400,.4,.3) ) >> >> f1<-kde2d(HDL.ran,TC.ran,n=25,lims=c(0,4,2,10)) >> >> contour(f1$x,f1$y,f1$z,ylim=c(0,8),xlim=c(0,3),ylab="TC mmol/L", >> xlab="HDL mmol/L") >> lines(f1$x,5*f1$x) # iso-ratio lines >> lines(f1$x,4*f1$x) >> lines(f1$x,3*f1$x) >> >> Two questions: >> Is there a 2d density estimation function that has provision for >> probability weights (or inverse sampling probabilities)? snip > > It looks like you can use bkde2D from the KernSmooth package. > > You might look at the function sqlocpoly in surveyNG which uses > the KernSmooth package for details. The prospect of setting up an SQL database was rather daunting and I continued my search. There were references in the the sql.. functions' documentation that they were providing the functions in package Locfit. Finding locfit() provided the weighting options I needed. This is what I came up with: tc.hdl.fit <- with(small.nh.chol, locfit(~LBDHDDSI+LBDTCSI, weights=WTMEC2YR, xlim=c(0,0,4,10) ) ) plot(tc.hdl.fit) #give warnings but does work title(main="Weighted", xlab="HDL", ylab="TC") # add labels _after_ plotting. # never could figure out how to get plot() to accept xlab or ylab # when passing the locfit object to it. with(tc.hdl.fit, lines(x,x*4)) -- Thanks; and thank you, Andy Liaw, for helpful earlier posts; David Winsemius ______________________________________________ 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.
