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
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