bw.CV | R Documentation |
This function provides a least squares cross-validation smoothing parameter or a likelihood cross-validation smoothing parameter for density estimation.
bw.CV(x, method="LCV", lower=0, upper=50, tol=1e-2, np=500)
x |
Data from which the smoothing parameter is to be computed. The object is coerced to class |
method |
Character string giving the cross-validation rule to be used. This must be one of |
lower, upper |
|
tol |
Convergence tolerance for |
np |
Number of points where to evaluate the estimator for numerical integration when |
The LCV smoothing parameter is obtained as the value of ν that maximizes the logarithm of the likelihood cross-validation function (8) in Oliveira et al. (2013). The LSCV smoothing parameter is obtained as the value of ν that minimizes expression (7) in Oliveira et al. (2013). See also Hall et al. (1987) and Oliveira et al. (2012). The NAs will be automatically removed.
Value of the smoothing parameter.
Maria Oliveira, Rosa M. Crujeiras and Alberto Rodriguez–Casal
Hall, P., Watson, G.S. and Cabrera, J. (1987) Kernel density estimation with spherical data, Biometrika, 74, 751–762.
Oliveira, M., Crujeiras, R.M. and Rodriguez–Casal, A. (2012) A plug–in rule for bandwidth selection in circular density. Computational Statistics and Data Analysis, 56, 3898–3908.
Oliveira, M., Crujeiras R.M. and Rodriguez–Casal, A. (2013) Nonparametric circular methods for exploring environmental data. Environmental and Ecological Statistics, 20, 1–17.
Oliveira, M., Crujeiras R.M. and Rodriguez–Casal, A. (2014) NPCirc: an R package for nonparametric circular methods. Journal of Statistical Software, 61(9), 1–26. https://www.jstatsoft.org/v61/i09/
kern.den.circ
, bw.rt
, bw.pi
, bw.boot
set.seed(2012) n <- 100 x <- rcircmix(n, model=11) bw.CV(x, method="LCV", lower=0, upper=20) bw.CV(x, method="LSCV", lower=0, upper=20)
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