Estimating Normal Vector through Kernel Regularized Least Squares

Estimating Normal Vector through Kernel Regularized Least Squares

nv.krls(xmat, resp, sample.size = 300, ...)

Arguments

xmat	Matrix of OC coordinates (i.e., predictors).
resp	Response Variable (i.e., ordered choices).
sample.size	Sample size for data used for the estimation of KRLS.
...	Additional arguments passed to krls.

Value

A vector of coefficients.

References

Jeremy Ferwerda, Jens Hainmueller, Chad J. Hazlett (2017). Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls). Journal of Statistical Software, 79(3), 1-26. doi:10.18637/jss.v079.i03

See also

krls