Usage
variogram(object, ...)
variogram(formula, locations, data, ...)
variogram(y, locations, X, cutoff, width, alpha, beta, tol.hor,
tol.ver, cressie, dX, boundaries, cloud, trend.beta, debug.level, ...)
print.variogram(v, ...)
print.variogram.cloud(v, ...)Arguments
object
object of class gstat; in this form, direct
and cross (residual) variograms are calculated for all variables and
variable pairs defined in object
formula
formula defining the response vector and (possible)
regressors, in case of absence of regressors, use e.g. z~1
data
data frame where the names in formula are to be found
locations
spatial data locations. For variogram.formula: a
formula with only the coordinate variables in the left hand (dependent
variable) side (see examples).For variogram.default: a matrix, with the number of rows matching
that of y, the number of columns sho
X
(optional) matrix with regressors/covariates; the number of
rows should match that of y, the number of columns equals the number
of regressors (including intercept)
cutoff
spatial separation distance up to which point pairs
are included in semivariance estimates
width
the width of subsequent distance intervals into which
data point pairs are grouped for semivariance estimates
alpha
direction in plane (x,y), in positive degrees clockwise
from positive y (North): alpha=0 for direction North (increasing y),
alpha=90 for direction East (increasing x); optional a vector of
directions in (x,y)
beta
direction in z, in positive degrees up from the (x,y) plane;
tol.hor
horizontal tolerance angle in degrees
tol.ver
vertical tolerance angle in degrees
cressie
logical; if TRUE, use Cressie's robust variogram estimate;
if FALSE use the classical method of moments variogram estimate
dX
include a pair of data points $y(s_1),y(s_2)$ taken at
locations $s_1$ and $s_2$ for sample variogram calculation only when
$||x(s_1)-x(s_2)|| < dX$ with and $x(s_i)$ the vector with regressors at
location $s_i$, and $||.||$ the 2-norm. This allows poole
boundaries
numerical vector with distance interval boundaries;
values should be strictly increasing
cloud
logical; if TRUE, calculate the semivariogram cloud
trend.beta
vector with trend coefficients, in case they are
known. By default, trend coefficients are estimated from the data.
debug.level
integer; set gstat internal debug level
v
object of class variogram or variogram.cloud
to be printed