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)
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?
Thank you.
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