variogram {gstat} | R Documentation |
Calculates the sample variogram from data, or in case of a linear model is given, for the residuals, with options for directional, robust, and pooled variogram, and for irregular distance intervals.
variogram(object, ...) variogram(formula, locations, data, ...) variogram(y, locations, X, cutoff, width = cutoff/15, alpha = 0, beta = 0, tol.hor = 90/length(alpha), tol.ver = 90/length(beta), cressie = FALSE, dX = numeric(0), boundaries = numeric(0), cloud = FALSE, trend.beta = NULL, debug.level = 1, cross = TRUE, ...) print.variogram(v, ...) print.variogram.cloud(v, ...)
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 right hand (explanatory
variable) side e.g. ~x+y ; see examples.
For variogram.default: a matrix, with the number of rows matching that of y, the number of columns should match the number of spatial dimensions spanned by the data (1 (x), 2 (x,y) or 3 (x,y,z)). |
... |
any other arguments that will be passed to variogram.default |
y |
vector with responses |
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 pooled estimation of within-strata variograms (use a factor variable as regressor, and dX=0.5), or variograms of (near-)replicates in a linear model (addressing point pairs having similar values for regressors variables) |
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 |
cross |
logical; if FALSE, no cross variograms are calculated
when object is of class gstat and has more than one variable |
v |
object of class variogram or variogram.cloud
to be printed |
an object of class "variogram" with the following fields:
np |
the number of point pairs for this estimate;
in case of a variogram.cloud see below |
dist |
the average distance of all point pairs considered for this estimate |
gamma |
the actual sample variogram estimate |
dir.hor |
the horizontal direction |
dir.ver |
the vertical direction |
id |
the combined id pair |
left |
for variogram.cloud: data id (row number) of one of the data pair |
right |
for variogram.cloud: data id (row number) of the other data in the pair |
Edzer J. Pebesma
Cressie, N.A.C., 1993, Statistics for Spatial Data, Wiley.
print.variogram, plot.variogram, plot.variogram.cloud, for variogram models: vgm, to fit a variogram model to a sample variogram: fit.variogram
data(meuse) # no trend: variogram(log(zinc)~1, loc=~x+y, meuse) # residual variogram w.r.t. a linear trend: variogram(log(zinc)~x+y, loc=~x+y, meuse) # directional variogram: variogram(log(zinc)~x+y, loc=~x+y, meuse, alpha=c(0,45,90,135))