On Apr 3, 2012, at 4:12 PM, Recher She wrote:
Dear R community,
I am trying to understand how the predict function, specifically, the
predict.loess function works.
I understand that the loess function calculates regression
parameters at
each data point in 'data'.
lo <- loess ( y~x, data)
Well, it produces a fitted-value at each point. Whether there are
parameters at each point might depend on the degree of the fit.
p <- predict (lo, newdata)
I understand that the predict function predicts values for 'newdata'
according to the loess regression parameters. How does predict.loess
do
this in the case that 'newdata' is different from the original data
x? How
does the interpolation take place?
Type this at your console:
getAnywhere(predict.loess)
And after seeing that an additional functions is called type this:
getAnywhere(predLoess)
And then you will see that you have descended into a C function called
'C_loess_dfitse'.
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--
David Winsemius, MD
West Hartford, CT
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