Selects the bandwidth of a local polynomial kernel (regression, density or variogram) estimator using (standard or modified) CV, GCV or MASE criteria.
h.cv(bin, ...)
# S3 method for bin.data
h.cv(
bin,
objective = c("CV", "GCV", "MASE"),
h.start = NULL,
h.lower = NULL,
h.upper = NULL,
degree = 1,
ncv = ifelse(objective == "CV", 2, 0),
cov.bin = NULL,
DEalgorithm = FALSE,
warn = TRUE,
tol.mask = npsp.tolerance(2),
...
)
# S3 method for bin.den
h.cv(
bin,
h.start = NULL,
h.lower = NULL,
h.upper = NULL,
degree = 1,
ncv = 2,
DEalgorithm = FALSE,
...
)
# S3 method for svar.bin
h.cv(
bin,
loss = c("MRSE", "MRAE", "MSE", "MAE"),
h.start = NULL,
h.lower = NULL,
h.upper = NULL,
degree = 1,
ncv = 1,
DEalgorithm = FALSE,
warn = FALSE,
...
)
hcv.data(
bin,
objective = c("CV", "GCV", "MASE"),
h.start = NULL,
h.lower = NULL,
h.upper = NULL,
degree = 1,
ncv = ifelse(objective == "CV", 1, 0),
cov.dat = NULL,
DEalgorithm = FALSE,
warn = TRUE,
...
)
object used to select a method (binned data, binned density or binned semivariogram).
further arguments passed to or from other methods (e.g. parameters of the optimization routine).
character; optimal criterion to be used ("CV", "GCV" or "MASE").
vector; initial values for the parameters (diagonal elements) to be optimized over.
If DEalgorithm == FALSE
(otherwise not used), defaults to (3 + ncv) * lag
,
where lag = bin$grid$lag
.
vector; lower bounds on each parameter (diagonal elements) to be optimized.
Defaults to (1.5 + ncv) * bin$grid$lag
.
vector; upper bounds on each parameter (diagonal elements) to be optimized.
Defaults to 1.5 * dim(bin) * bin$grid$lag
.
degree of the local polynomial used. Defaults to 1 (local linear estimation).
integer; determines the number of cells leaved out in each dimension. (0 to GCV considering all the data, \(>0\) to traditional or modified cross-validation). See "Details" bellow.
(optional) covariance matrix of the binned data or semivariogram model
(svarmod
-class) of the (unbinned) data. Defaults to the identity matrix.
logical; if TRUE
, the differential evolution optimization algorithm
in package DEoptim is used.
logical; sets the handling of warning messages
(normally due to the lack of data in some neighborhoods).
If FALSE
all warnings are ignored.
tolerance used in the aproximations. Defaults to npsp.tolerance(2)
.
character; CV error. See "Details" bellow.
covariance matrix of the data or semivariogram model
(of class extending svarmod
). Defaults to the identity matrix
(uncorrelated data).
Returns a list containing the following 3 components:
the best (diagonal) bandwidth matrix found.
the value of the objective function corresponding to h
.
the criterion used.
Currently, only diagonal bandwidths are supported.
h.cv
methods use binning approximations to the objective function values
(in almost all cases, an averaged squared error).
If ncv > 0
, estimates are computed by leaving out binning cells with indexes within
the intervals \([x_i - ncv + 1, x_i + ncv - 1]\), at each dimension i, where \(x\)
denotes the index of the estimation location. \(ncv = 1\) corresponds with
traditional cross-validation and \(ncv > 1\) with modified CV
(it may be appropriate for dependent data; see e.g. Chu and Marron, 1991, for the one dimensional case).
Setting ncv >= 2
would be recommended for sparse data (as linear binning is used).
For standard GCV, set ncv = 0
(the whole data would be used).
For theoretical MASE, set bin = binning(x, y = trend.teor)
, cov = cov.teor
and ncv = 0
.
If DEalgorithm == FALSE
, the "L-BFGS-B"
method in optim
is used.
The different options for the argument loss
in h.cv.svar.bin()
define the CV error
considered in semivariogram estimation:
"MSE"
Mean squared error
"MRSE"
Mean relative squared error
"MAE"
Mean absolute error
"MRAE"
Mean relative absolute error
hcv.data
evaluates the objective function at the original data
(combining a binning approximation to the nonparametric estimates with a linear interpolation),
this can be very slow (and memory demanding; consider using h.cv
instead).
If ncv > 1
(modified CV), a similar algorithm to that in h.cv
is used,
estimates are computed by leaving out binning cells with indexes within
the intervals \([x_i - ncv + 1, x_i + ncv - 1]\).
Chu, C.K. and Marron, J.S. (1991) Comparison of Two Bandwidth Selectors with Dependent Errors. The Annals of Statistics, 19, 1906-1918.
Francisco-Fernandez M. and Opsomer J.D. (2005) Smoothing parameter selection methods for nonparametric regression with spatially correlated errors. Canadian Journal of Statistics, 33, 539-558.
# Trend estimation
bin <- binning(earthquakes[, c("lon", "lat")], earthquakes$mag)
hcv <- h.cv(bin, ncv = 2)
lp <- locpol(bin, h = hcv$h)
# Alternatively, `locpolhcv()` could be called instead of the previous code.
simage(lp, main = 'Smoothed magnitude')
contour(lp, add = TRUE)
with(earthquakes, points(lon, lat, pch = 20))
# Density estimation
hden <- h.cv(as.bin.den(bin))
den <- np.den(bin, h = hden$h)
plot(den, main = 'Estimated log(density)')