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.
In case spatio-temporal data is provided, the function variogramST
is called with a different set of parameters.
# S3 method for gstat
variogram(object, ...)
# S3 method for formula
variogram(object, locations = coordinates(data), data, ...)
# S3 method for default
variogram(object, 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, grid, map = FALSE, g = NULL, ..., projected = TRUE,
lambda = 1.0, verbose = FALSE, covariogram = FALSE, PR = FALSE,
pseudo = -1)
# S3 method for gstatVariogram
print(x, ...)
# S3 method for variogramCloud
print(x, ...)If map is TRUE (or a map is passed), a grid map is returned containing the (cross) variogram map(s). See package sp.
In other cases, an object of class "gstatVariogram" with the following fields:
the number of point pairs for this estimate;
in case of a variogramCloud see below
the average distance of all point pairs considered for this estimate
the actual sample variogram estimate
the horizontal direction
the vertical direction
the combined id pair
If cloud is TRUE: an object of class variogramCloud, with the field
np encoding the numbers of the point pair that contributed to a
variogram cloud estimate, as follows. The first point is found by 1 + the
integer division of np by the .BigInt attribute of the returned
object, the second point by 1 + the remainder of that division.
as.data.frame.variogramCloud returns no np field,
but does the decoding into:
for variogramCloud: data id (row number) of one of the data pair
for variogramCloud: data id (row number) of the other data in the pair
In case of a spatio-temporal variogram is sought see variogramST
for details.
object of class gstat; in this form, direct
and cross (residual) variograms are calculated for all variables and
variable pairs defined in object; in case of variogram.formula,
formula defining the response vector and (possible)
regressors, in case of absence of regressors, use e.g. z~1;
in case of variogram.default: list with for each variable
the vector with responses (should not be called directly)
data frame where the names in formula are to be found
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: list with coordinate matrices, each with the number of rows matching that of corresponding vectors in 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 (ignored)
(optional) list with for each variable the matrix with regressors/covariates; the number of rows should match that of the correspoding element in y, the number of columns equals the number of regressors (including intercept)
spatial separation distance up to which point pairs are included in semivariance estimates; as a default, the length of the diagonal of the box spanning the data is divided by three.
the width of subsequent distance intervals into which data point pairs are grouped for semivariance estimates
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)
direction in z, in positive degrees up from the (x,y) plane;
optional a vector of directions
horizontal tolerance angle in degrees
vertical tolerance angle in degrees
logical; if TRUE, use Cressie''s robust variogram estimate; if FALSE use the classical method of moments variogram estimate
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)
numerical vector with distance interval upper boundaries; values should be strictly increasing
logical; if TRUE, calculate the semivariogram cloud
vector with trend coefficients, in case they are known. By default, trend coefficients are estimated from the data.
integer; set gstat internal debug level
logical or character; if FALSE, no cross variograms are computed
when object is of class gstat and has more than one variable; if
TRUE, all direct and cross variograms are computed; if
equal to "ST", direct and cross variograms are computed for all pairs
involving the first (non-time lagged) variable; if equal to "ONLY",
only cross variograms are computed (no direct variograms).
formula, specifying the dependent variable and possible covariates
object of class variogram or variogramCloud
to be printed
grid parameters, if data are gridded (not to be called directly; this is filled automatically)
logical; if TRUE, and cutoff and width
are given, a variogram map is returned. This requires package
sp. Alternatively, a map can be passed, of class SpatialDataFrameGrid
(see sp docs)
NULL or object of class gstat; may be used to pass settable parameters and/or variograms; see example
logical; if FALSE, data are assumed to be unprojected,
meaning decimal longitude/latitude. For projected data, Euclidian
distances are computed, for unprojected great circle distances
(km). In variogram.formula or variogram.gstat, for data
deriving from class Spatial, projection is detected automatically using
is.projected
test feature; not working (yet)
logical; print some progress indication
integer; use pseudo cross variogram for computing time-lagged spatial variograms? -1: find out from coordinates -- if they are equal then yes, else no; 0: no; 1: yes.
logical; compute covariogram instead of variogram?
logical; compute pairwise relative variogram (does NOT check whether variable is strictly positive)
Edzer Pebesma
Cressie, N.A.C., 1993, Statistics for Spatial Data, Wiley.
Cressie, N., C. Wikle, 2011, Statistics for Spatio-temporal Data, Wiley.
Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers and Geosciences, 30: 683-691.
print.gstatVariogram,
plot.gstatVariogram,
plot.variogramCloud;
for variogram models: vgm,
to fit a variogram model to a sample variogram:
fit.variogram
variogramST for details on the spatio-temporal sample variogram.
library(sp)
data(meuse)
# no trend:
coordinates(meuse) = ~x+y
variogram(log(zinc)~1, meuse)
# residual variogram w.r.t. a linear trend:
variogram(log(zinc)~x+y, meuse)
# directional variogram:
variogram(log(zinc)~x+y, meuse, alpha=c(0,45,90,135))
variogram(log(zinc)~1, meuse, width=90, cutoff=1300)
# GLS residual variogram:
v = variogram(log(zinc)~x+y, meuse)
v.fit = fit.variogram(v, vgm(1, "Sph", 700, 1))
v.fit
set = list(gls=1)
v
g = gstat(NULL, "log-zinc", log(zinc)~x+y, meuse, model=v.fit, set = set)
variogram(g)
if (require(sf)) {
proj4string(meuse) = CRS("+init=epsg:28992")
meuse.ll = sf::st_transform(sf::st_as_sf(meuse), sf::st_crs("+proj=longlat +datum=WGS84"))
# variogram of unprojected data, using great-circle distances, returning km as units
print(variogram(log(zinc) ~ 1, meuse.ll))
}
Run the code above in your browser using DataLab