R help - Jan 2008 - Probability weights with density estimation

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
http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/hdl_d.xpt
http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/tchol_d.xpt

   Thank you to the R authors and the foreign package authors in 
particular. Importing from the SAS export fomat file was a snap. It 
consists of demographic data linked to laboratory measurements. Each 
subject has an associated sampling weight. I have gotten informative 
displays following the examples using kde2d() in V&R MASSe2 (more 
thanks), but these were unweighted analyses. The ratio of total 
cholesterol (TC) to HDL cholesterol is used clinically to estimate risk 
of future heart disease, and I am looking at how such ratios "divide" 
or intersect with the TC x HDL-C distribution. Rather than include all 
the real data, let me just post a simulation that shows a contourplot 
reasonably similar to what I am seeing.

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)? I seem to 
remember a discussion on the list about whether such a procedure would 
be meaningful, but my searches cannot locate that thread or any worked 
examples that incorporate sampling weights. 

If there is such a function, would it be a simple matter to calculate 
the proportion of the total population that would be expected to have a 
ratio of y.ran/x.ran of less than a particular number, say 4.0?

-- 
Respectfully;
David Winsemius

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
> http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/hdl_d.xpt
> http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/tchol_d.xpt
>
>   Thank you to the R authors and the foreign package authors in
> particular. Importing from the SAS export fomat file was a snap. It
> consists of demographic data linked to laboratory measurements. Each
> subject has an associated sampling weight. I have gotten informative
> displays following the examples using kde2d() in V&R MASSe2 (more
> thanks), but these were unweighted analyses. The ratio of total
> cholesterol (TC) to HDL cholesterol is used clinically to estimate risk
> of future heart disease, and I am looking at how such ratios
"divide"
> or intersect with the TC x HDL-C distribution. Rather than include all
> the real data, let me just post a simulation that shows a contourplot
> reasonably similar to what I am seeing.
>
> 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)? I seem to
> remember a discussion on the list about whether such a procedure would
> be meaningful, but my searches cannot locate that thread or any worked
> examples that incorporate sampling weights.

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.

>
> If there is such a function, would it be a simple matter to calculate
> the proportion of the total population that would be expected to have a
> ratio of y.ran/x.ran of less than a particular number, say 4.0?
Maybe my eyesight is failing, but I did not see where you define 'y.ran'
and 'x.ran'. If they, like 'TC.ran' and 'HDL.ran', are
just variables that
are dierctly measured in your survey, then estimating the proportion less 
than a given value for y.ran/x.ran is standard survey sampling fare and no 
density estimation is needed. In which case, the 'survey' package at
CRAN
is what you want.

HTH,

Chuck
>
> -- 
> Respectfully;
> David Winsemius
>
> ______________________________________________
> R-help at 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.
>
Charles C. Berry                            (858) 534-2098
                                             Dept of Family/Preventive Medicine
E mailto:cberry at tajo.ucsd.edu	            UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/  La Jolla, San Diego 92093-0901