| 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.
## S3 method for class 'formula':
variogram(object, ...)
## S3 method for class 'gstat':
variogram(formula, locations, data, ...)
## Default S3 method:
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, grid, map = FALSE, g = NULL, ..., projected = TRUE, lambda = 1.0)
## S3 method for class 'line':
variogram(..., deprecate = TRUE)
## S3 method for class 'gstatVariogram':
print(v, ...)
## S3 method for class 'variogramCloud':
print(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: 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) |
y |
list with for each variable the vector with responses |
X |
(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) |
cutoff |
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. |
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 variogramCloud
to be printed |
grid |
grid parameters, if data are gridded |
map |
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) |
deprecate |
logical; if TRUE, a message will be printed to say
that this function is deprecated. Function variogram.line will
be deprecated in favour of the identical variogramLine |
g |
NULL or object of class gstat; may be used to pass settable parameters and/or variograms; see example |
projected |
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 |
lambda |
test feature; not working (yet) |
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:
np |
the number of point pairs for this estimate;
in case of a variogramCloud 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 variogramCloud: data id (row number) of one of the data pair |
right |
for variogramCloud: data id (row number) of the other data in the pair |
In the past, gstat returned an object of class "variogram"; however,
this resulted in confusions for users of the package geoR: the geoR
variog function also returns objects of class "variogram", incompatible
to those returned by this function. That's why I changed the class name.
variogram.line is DEPRECATED; it is and was never meant as a variogram
method, but works automatically as such by the R dispatch system. Use
variogramLine instead.
Edzer J. Pebesma
Cressie, N.A.C., 1993, Statistics for Spatial Data, Wiley.
Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & 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
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))
# 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(rgdal)) {
proj4string(meuse) = CRS("+init=epsg:28992")
meuse.ll = spTransform(meuse, CRS("+proj=longlat"))
# variogram of unprojected data, using great-circle distances, returning km as units
variogram(log(zinc) ~ 1, meuse.ll)
}