georob (version 0.2-3)

plot.georob: Plot Methods for Class georob

Description

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 (Tukey-Anscombe plot, normal QQ plots of residuals and random effects).

Usage

"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", ...)
"lines"(x, what = c("variogram", "covariance", "correlation"), from = 1.e-6, to, n = 501, xy.angle = 90, xz.angle = 90,  col = 1:length(xy.angle), pch = 1:length(xz.angle), lty = "solid", ...)

Arguments

x

an object of class georob, see georobObject.

what

character keyword for the quantity that should be displayed. Possible values are:

• "variogram": the variogram
• "covariance": the covariance function
• "correlation": the correlation function
• "scale-location": square root of absolute regression residals plotted against fitted values (Scale-Location plot)

• "ta": regression residals plotted against fitted values (Tukey-Anscombe plot)

• "qq.res": normal Q-Q plot of standardized errors $hat\epsilon$
• "qq.ranef": normal Q-Q plot of standardized random effects $hatB$

add

logical controlling whether a new plot should be generated (FALSE, default) or whether the information should be added to the current plot (TRUE).

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 sample.variogram. If missing then the sample variogram is not computed.

xy.angle.def

an numeric vector defining angular classes in the horizontal plane for computing directional variograms. xy.angle.def must contain an ascending sequence of azimuth angles in degrees from north (positive clockwise to south), see sample.variogram. Omnidirectional variograms are computed with the default c(0,180).

xz.angle.def

an numeric vector defining angular classes in the $x$-$z$-plane for computing directional variograms. xz.angle.def must contain an ascending sequence of angles in degrees from zenith (positive clockwise to nadir), see sample.variogram. Omnidirectional variograms are computed with the default c(0,180).

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:

• "qn": Genton's robust Qn-estimator (default, Genton, 1998),
• "mad": Dowd's robust MAD-estimator (Dowd, 1984),
• "matheron": non-robust method-of-moments estimator,
• "ch": robust Cressie-Hawkins estimator (Cressie and Hawkins, 1980).

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 (TRUE, default) or from the mid-angles of the respective angular classes (FALSE).

level

an integer giving the level for extracting the residuals from object for what = "ta" or what = "qq.res". level = 0 (default for what == "ta") extracts the regression residuals $hatB(s) + hat\epsilon(s)$ and level = 1 (default for what == "qq.res") only the estimated errors $hat\epsilon(s)$.

smooth

logical controlling whether a loess.smooth is added to the Tukey-Anscombe plot (default TRUE.

id.n

number of points to be labelled in each plot, starting with the most extreme (see plot.lmrob).

labels.id

vector of labels, from which the labels for extreme points will be chosen (see plot.lmrob). NULL uses observation numbers.

cex.id

magnification of point labels (see plot.lmrob).

label.pos

positioning of labels, for the left half and right half of the graph respectively (see plot.lmrob).

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 501).

xy.angle

numeric (vector) with azimuth angles (in degrees, clockwise positive from north) in $x$-$y$-plane for which semivariances should be plotted.

xz.angle

numeric (vector) with angles in $x$-$z$-plane (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 $x$-$y$-plane.

pch

optional symbol for points and curves to distinguish items relating to different azimuth angles in $x$-$z$-plane.

lty

line type for plotting variogram models.

xlab, ylab, main

test annotation, see plot.

...

additional arguments passed to plot.sample.variogram, loess.smooth and graphical methods.

See Also

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; sample.variogram for computing sample variograms.

Examples

## 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)