np.svar(x, ...)
"np.svar" (x, y, h = NULL, maxlag = NULL, nlags = NULL, minlag = maxlag/nlags, degree = 1, drv = FALSE, hat.bin = TRUE, ncv = 0, ...)
"np.svar" (x, h = NULL, degree = 1, drv = FALSE, hat.bin = TRUE, ncv = 0, ...)
np.svariso(x, y, h = NULL, maxlag = NULL, nlags = NULL, minlag = maxlag/nlags, degree = 1, drv = FALSE, hat.bin = TRUE, ncv = 0, ...)
np.svariso.hcv(x, y, maxlag = NULL, nlags = NULL, minlag = maxlag/nlags, degree = 1, drv = FALSE, hat.bin = TRUE, objective = c("CV", "GCV", "MASE"), ncv = ifelse(objective == "GCV", 0, 1), cov.bin = NULL, ...)
np.svariso.corr(lp, x = lp$data$x, h = NULL, maxlag = NULL, nlags = NULL, minlag = maxlag/nlags, degree = 1, drv = FALSE, hat.bin = TRUE, tol = 0.05, max.iter = 10, plot = FALSE, ylim = c(0, 2 * max(svar$biny, na.rm = TRUE)))
TRUE
, the hat matrix of
the binned semivariances is returned.locpol.bin
).tol
. Defaults to 0.04.TRUE
, the estimates
obtained at each iteration are plotted.plot ==
TRUE
).TRUE
, the matrix of
estimated first derivatives is returned.np.svar
(locpol svar
+ binned svar + grid par.), extends
svar.bin
, with the additional (some
optional) 3 components: degree
degree of the local polinomial used.
h
smoothing matrix. rm
mean of
residual semivariances. rss
sum of squared
residual semivariances. ncv
number of cells
ignored in each direction. hat
(if
requested) hat matrix of the binned semivariances.
nrl0
(if appropriate) number of cells with
binw > 0
and est == NA
. If parameter nlags
is not specified is set to
101
.
The computation of the hat matrix of the binned
semivariances (hat.bin = TRUE
) allows for the
computation of approximated estimation variances (e.g. in
fitsvar.sb.iso
).
A multiplicative triweight kernel is used to compute the weights.
np.svariso.hcv
calls h.cv
to obtain
an "optimal" bandwith (additional arguments ...
are passed to this function). Argument ncv
is only
used here at the bandwith selection stage (estimation is
done with all the data).
np.svariso.corr
computes a bias-corrected
nonparametric semivariogram estimate using an iterative
algorithm similar to that described in Fernandez-Casal
and Francisco-Fernandez (2014). This procedure tries to
correct the bias due to the direct use of residuals
(obtained in this case from a nonparametric estimation of
the trend function) in semivariogram estimation.
Garcia-Soidan P.H., Gonzalez-Manteiga W. and Febrero-Bande M. (2003) Local linear regression estimation of the variogram, Stat. Prob. Lett., 64, 169-179.
Fernandez-Casal R. and Francisco-Fernandez M. (2014) Nonparametric bias-corrected variogram estimation under non-constant trend, Stoch. Environ. Res. Ris. Assess, 28, 1247-1259.
svar.bin
, data.grid
,
locpol
.