[R] Cross validation multivariate kernel regression

[Preetam Pal]

Hi,

This question is general- I have a data set of n observations, consisting
of a single response variable y and p regressor variables.( n ~50, p~3 or
4).
I am planning to implement Nadaraya-Watson regression model, with
bandwidths optimized via cross-validation.
For cross-validation, I will need to choose 10 outsample/test data sets of
a given size ( =n/10 ) for each choice of the bandwidth vector, and then
choose the optimum bandwidth vector (in terms of MSE or any reasonable loss
function-we can take it to be MSE,  as example).

The difficulty is I can't find any code to do this under:
A) multiple regressors (p>1) AND
B) I'll get to choose to the outsample datasets.

Thanks for any help/insight you can provide.
Regards,
Preetam

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