Description Usage Arguments Author(s) See Also Examples
The plot
and lines
methods for class
georob
plot the variogram model, estimated by (robust) restricted
maximum likelihood.
plot.georob
computes and plots in addition the
sample variogram of the (robust) regression residuals and can be used to
generate residual diagnostics plots (TukeyAnscombe plot, normal QQ plots
of residuals and random effects).
1 2 3 4 5 6 7 8 9 10 11 12 13  ## S3 method for class 'georob'
plot(x, what = c( "variogram", "covariance", "correlation",
"ta", "sl", "qq.res", "qq.ranef" ), add = FALSE, lag.dist.def,
xy.angle.def = c(0, 180), xz.angle.def = c(0, 180), max.lag = Inf,
estimator = c("mad", "qn", "ch", "matheron"), mean.angle = TRUE,
level = what != "ta", smooth = what == "ta"  what == "sl",
id.n = 3, labels.id = names(residuals(x)), cex.id = 0.75,
label.pos = c(4,2), col, pch, xlab, ylab, main, lty = "solid", ...)
## S3 method for class 'georob'
lines(x, what = c("variogram", "covariance", "correlation"),
from = 1.e6, to, n = 501, xy.angle = 90, xz.angle = 90,
col = 1:length(xy.angle), pch = 1:length(xz.angle), lty = "solid", ...)

x 
an object of class 
what 
character keyword for the quantity that should be displayed. Possible values are:

add 
logical controlling whether a new plot should be
generated ( 
lag.dist.def 
an optional numeric scalar defining a constant bin
width for grouping the lag distances or an optional numeric vector with
the upper bounds of a set of contiguous bins for computing the sample
variogram of the regression residuals, see

xy.angle.def 
an numeric vector defining angular classes in the
horizontal plane for computing directional variograms.

xz.angle.def 
an numeric vector defining angular classes in the
xzplane for computing directional variograms.

max.lag 
positive numeric defining the largest lag distance for which semivariances should be computed (default no restriction). 
estimator 
character keyword defining the estimator for computing the sample variogram. Possible values are:

mean.angle 
logical controlling whether the mean lag vector (per
combination of lag distance and angular class) is computed from the mean
angles of all the lag vectors falling into a given class ( 
level 
an integer giving the level for extracting the residuals
from 
smooth 
logical controlling whether a

id.n 
number of points to be labelled in each plot, starting
with the most extreme (see 
labels.id 
vector of labels, from which the labels for extreme
points will be chosen (see 
cex.id 
magnification of point labels (see

label.pos 
positioning of labels, for the left half and right half
of the graph respectively (see 
from 
numeric, minimal lag distance for plotting variogram models. 
to 
numeric, maximum lag distance for plotting variogram models (default: largest lag distance of current plot). 
n 
positive integer specifying the number of equally spaced lag
distances for which semivariances are evaluated in plotting variogram
models (default 
xy.angle 
numeric (vector) with azimuth angles (in degrees, clockwise positive from north) in xyplane for which semivariances should be plotted. 
xz.angle 
numeric (vector) with angles in xzplane (in degrees, clockwise positive from zenith to south) for which semivariances should be plotted. 
col 
optional color of points and curves to distinguish items relating to different azimuth angles in xyplane. 
pch 
optional symbol for points and curves to distinguish items relating to different azimuth angles in xzplane. 
lty 
line type for plotting variogram models. 
xlab, ylab, main 
test annotation, see

... 
additional arguments passed to

Andreas Papritz [email protected].
georobIntro
for a description of the model and a brief summary of the algorithms;
georob
for (robust) fitting of spatial linear models;
georobObject
for a description of the class georob
;
profilelogLik
for computing profiles of Gaussian likelihoods;
control.georob
for controlling the behaviour of georob
;
georobModelBuilding
for stepwise building models of class georob
;
cv.georob
for assessing the goodness of a fit by georob
;
georobMethods
for further methods for the class georob
;
predict.georob
for computing robust Kriging predictions;
lgnpp
for unbiased backtransformation of Kriging prediction
of logtransformed data;
georobSimulation
for simulating realizations of a Gaussian process
from model fitted by georob
; and finally
sample.variogram
and fit.variogram.model
for robust estimation and modelling of sample variograms.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ## Not run:
################
## meuse data ##
################
data(meuse)
## Gaussian REML fit
r.logzn.reml < georob(log(zinc) ~ sqrt(dist), data = meuse, locations = ~ x + y,
variogram.model = "RMexp",
param = c(variance = 0.15, nugget = 0.05, scale = 200),
tuning.psi = 1000)
summary(r.logzn.reml, correlation = TRUE)
## robust REML fit
r.logzn.rob < update(r.logzn.reml, tuning.psi = 1)
summary(r.logzn.rob, correlation = TRUE)
plot(r.logzn.reml, lag.dist.def = seq(0, 2000, by = 100))
lines(r.logzn.rob, col = "red")
## End(Not run)

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