Estimates a multidimensional semivariogram (and its first derivatives) using local polynomial kernel smoothing of linearly binned semivariances.
np.svar(x, ...) # S3 method for default np.svar( x, y, h = NULL, maxlag = NULL, nlags = NULL, minlag = maxlag/nlags, degree = 1, drv = FALSE, hat.bin = TRUE, ncv = 0, ... ) # S3 method for svar.bin 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, loss = c("MRSE", "MRAE", "MSE", "MAE"), ncv = 1, warn = FALSE, ... ) 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)) )
x  object used to select a method. Usually a matrix with the coordinates of the data locations (columns correspond with dimensions and rows with data). 

...  further arguments passed to or from other methods. 
y  vector of data (response variable). 
h  (full) bandwidth matrix (controls the degree of smoothing; only the upper triangular part of h is used). 
maxlag  maximum lag. Defaults to 55% of largest lag. 
nlags  number of lags. Defaults to 101. 
minlag  minimun lag. 
degree  degree of the local polynomial used. Defaults to 1 (local linear estimation). 
drv  logical; if 
hat.bin  logical; if 
ncv  integer; determines the number of cells leaved out in each dimension. Defaults to 0 (the full data is used) and it is not normally changed by the user in this setting. See "Details" below. 
loss  character; CV error. See "Details" bellow. 
warn  logical; sets the handling of warning messages
(normally due to the lack of data in some neighborhoods).
If 
lp  local polynomial estimate of the trend function (object of class

tol  convergence tolerance. The algorithm stops if the average of the
relative squared diferences is less than 
max.iter  maximum number of iterations. Defaults to 10. 
plot  logical; if 
ylim  ylimits of the plot (if 
Returns an S3 object of class np.svar
(locpol svar + binned svar + grid par.),
extends svar.bin
, with the additional (some optional) 3 components:
vector or array with the local polynomial semivariogram estimates.
a list of 6 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 requested) matrix of estimated first semivariogram derivatives.
Currently, only isotropic semivariogram estimation is supported.
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 biascorrected nonparametric semivariogram
estimate using an iterative algorithm similar to that described in
FernandezCasal and FranciscoFernandez (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.
Fernandez Casal R., Gonzalez Manteiga W. and Febrero Bande M. (2003) Spacetime dependency modeling using general classes of flexible stationary variogram models, J. Geophys. Res., 108, 8779, doi:10.1029/2002JD002909.
GarciaSoidan P.H., GonzalezManteiga W. and FebreroBande M. (2003) Local linear regression estimation of the variogram, Stat. Prob. Lett., 64, 169179.
FernandezCasal R. and FranciscoFernandez M. (2014) Nonparametric biascorrected variogram estimation under nonconstant trend, Stoch. Environ. Res. Ris. Assess, 28, 12471259.