RandomFields (version 3.0.10)

plot-method: Methods for function plot in package RandomFields

Description

Plot methods are implemented for simulated random fields (objects of class RFsp), explicit covariance models (objects of class RMmodel), empirical variograms (objects of class RFempVariog) and fitted models (objects of class RFfit).

Usage

## S3 method for class 'RFspatialGridDataFrame,missing':
plot(x, 
 MARGIN=c(1,2), MARGIN.slices=NULL,
 n.slices = if (!missing(MARGIN.slices) &&
 !is.null(MARGIN.slices)) 10 else 1,
 nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), select.variables, zlim, legend=TRUE, ...)
## S3 method for class 'RFspatialPointsDataFrame,missing':
plot(x, y, MARGIN=c(1,2), nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), select.variables, zlim, legend=TRUE, ...)
## S3 method for class 'RFgridDataFrame,missing':
plot(x, y, nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), ...)
## S3 method for class 'RFpointsDataFrame,missing':
plot(x, y, nmax=6, sorted=FALSE, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), ...)
## S3 method for class 'RFempVariog,missing':
plot(x, y, nmax.phi=6, nmax.theta=3, nmax.T=3,
 plot.nbin=TRUE, plot.sd=FALSE, model=NULL, variogram=TRUE, ...)
## S3 method for class 'RFfit,missing':
plot(x, y, fit.method="ml",
 nmax.phi=6, nmax.theta=3, nmax.T=3,
 plot.nbin=TRUE, plot.sd=FALSE, model=NULL, variogram = TRUE, ...)
## S3 method for class 'RMmodel,missing':
plot(x, y, dim=1, n.points=200,
 fct.type=NULL, MARGIN, fixed.MARGIN, ...)
## S3 method for class 'RMmodel':
points(x, y, n.points=200, fct.type=NULL, ...)
## S3 method for class 'RMmodel':
lines(x, y, n.points=200, fct.type=NULL, ...)

Arguments

x

object of class RFsp or RFempVario or RFfit or

y

ignored in most methods

MARGIN

vector of two; two integer values giving the coordinate dimensions w.r.t. which the field or the covariance model is to be plotted; in all other directions, the first index is taken

MARGIN.slices

integer value; if $[space-time-dimension>2]$, MARGIN.slices can specify a third dimension w.r.t. which a sequence of slices is plotted

fixed.MARGIN

only for class(x)=="RMmodel" and if dim > 2; a vector of length dim-2 with distance values for the coordinates that are not displayed

n.slices

integer value; the number of slices to be plotted; if n.slices>1, nmax is set to 1

nmax

the maximal number of the x@.RFparams$n iid copies of the field that are to be plotted

sorted

logical; indicates whether the point locations are in increasing order

...

arguments to be passed to methods; mainly graphical parameters; internally, ... are passed to image and plot.default, respectively; if, by default, multiple colors, xlabs or ylabs are used, also vectors of suitabl

fit.method

character; only for class(x)=="RFfit"; a vector of slot names for which the fitted covariance or variogram model is to be plotted; should be a subset of slotNames(x) for which the corresponding slots are of class "RMmode

nmax.phi

integer; only for class(x)=="RFempVario"; the maximal number of bins of angle phi that are to be plotted

nmax.theta

integer; only for class(x)=="RFempVario"; the maximal number of bins of angle theta that are to be plotted

nmax.T

integer; only for class(x)=="RFempVario"; the maximal number of different time bins that are to be plotted

plot.nbin

logical; only for class(x)=="RFempVario"; indicates whether the number of pairs per bin are to be plotted

plot.sd

logical; only for class(x)=="RFempVario"; indicates whether the calculated standard deviation (x@sd) is to be plotted (in form of arrows of length +-1*sd)

variogram

logical; This parameter should currently not be set by the user. If TRUE then the empirical variogram is plotted, else an estimate for the covariance function

dim

must equal 1 or 2; only for class(x)=="RMmodel"; the covariance function and the variogram are plotted as a function of $\R^\code{dim}$.

n.points

integer; only for class(x)=="RMmodel"; the number of points at which the model evaluated (in each dimension); defaults to 200

fct.type

character; only for class(x)=="RMmodel"; must equal NULL, "Cov" or "Variogram"; controls whether the covariance (fct.type="Cov") or the variogram (fct.type="Variogram") is pl

model

object of class RMmodel; only for class(x)=="RFempVario" or class(x)=="RFfit"; a list of covarianve or variogram models that are to be plotted into the same

plot.variance

logical, whether variances should be plotted if available

select.variables

vector of integers or list of vectors. The parameter is only of interest for multivariate models. Here, length(select.variables) gives the number of pictures shown (excuding the plot for the variances, if applicable). If select.vari

legend

logical, whether a legend should be plotted

zlim

vector of length 2 with the usual meaning. In case of multivariate random fields, zlim can also be a character wih the value joint indicating that all plotted compoments shall have the same zlim OR a matrix with two rows,

Examples

RFoptions(seed=0)

## define the model:
 model <- RMtrend(mean=0.5) + # mean
 RMstable(alpha=1, var=4, scale=10) +
 # see help("RMstable")
 # for additional parameters
 RMnugget(var=1) # nugget

 #############################################################
 ## Plot of covariance structure

 plot(model, xlim=c(0, 30))
 plot(model, xlim=c(0, 30), fct.type="Variogram")
 plot(model, xlim=c(-10, 20), fct.type="Variogram", dim=2)

 #############################################################
 ## Plot of simulation results

 ## define the locations:
 from <- 0
 step <- .1 
 len <- if (interactive()) 50 else 5 ## nicer, but also time consuming if len <- 200
 x1D <- GridTopology(from, step, len)
 x2D <- GridTopology(rep(from, 2), rep(step, 2), rep(len, 2))
 x3D <- GridTopology(rep(from, 3), rep(step, 3), rep(len, 3))

 ## 1-dimensional
 sim1D <- RFsimulate(model = model, x=x1D, n=6) 
 plot(sim1D, nmax=4)

 ## 2-dimensional
 sim2D <- RFsimulate(model = model, x=x2D, n=6) 
 plot(sim2D, nmax=4)
 plot(sim2D, nmax=4, col=terrain.colors(64),
 main="My simulation", xlab="my_xlab")

 ## 3-dimensional
 model <- RMmatern(nu=1.5, var=4, scale=2)
 sim3D <- RFsimulate(model = model, x=x3D) 
 plot(sim3D, MARGIN=c(2,3), MARGIN.slices=1, n.slices=4)

 
 #############################################################
 ## empirical variogram plots

 x <- c(0,.05,200)
 bin <- seq(from=0, by=.2, to=3)

 model <- RMexp()
 X <- RFsimulate(x=cbind(x), model=model, grid=TRUE)
 ev1 <- RFempiricalvariogram(data=X, bin=bin)
 plot(ev1)

 model <- RMexp(Aniso = cbind(c(10,0), c(0,1)))
 X <- RFsimulate(x=cbind(x,x), model=model, grid=TRUE)
 ev2 <- RFempiricalvariogram(data=X, bin=bin, phi=3)
 plot(ev2, model=list(Exp = model))


 #############################################################
 ## plot Fitting results
 x <- c(0,.1,20)
 model <- RMexp(Aniso = cbind(c(10,0), c(0,1)))
 X <- RFsimulate(x=cbind(x,x), model=model, grid=TRUE)
 fit <- RFfit(~RMexp(Aniso=matrix(nc=2, nr=2, NA)), data=X, fit.nphi = 2)
 plot(fit) 


 #############################################################
 ## plot Kriging results 
 
 model <- RMwhittle(nu=1.2, scale=2)
 x <- runif(200, max=step*len/2) 
 y <- runif(200, max=step*len/2) # 200 points in 2 dimensional space
 sim <- RFsimulate(model = model, x=x, y=y)

 interpolate <- RFinterpolate(model=model, x=x2D, data=sim)
 plot(interpolate)
 plot(interpolate, sim)


#############################################################
## plotting vector-valued results
model <- RMdivfree(RMgauss(), scale=4)
x <- y <- seq(-10,10, if (interactive()) 0.5 else 5)
simulated <- RFsimulate(model = model, x=x, y=y, grid=TRUE, n=1)
plot(simulated)
plot(simulated, select.variables=list(1, 1:3, 4))




#############################################################
## options for the zlim parameter
model <- RMdelay(RMstable(alpha=1.9, scale=2), s=c(0, 4)) +
 RMdelay(RMstable(alpha=1.9, scale=2), s=c(4, 0))
simu <- RFsimulate(model, x, y, grid=TRUE)

plot(simu, zlim=list(data=cbind(c(-6,2), c(-2,1)), var=c(5,6)))
plot(simu, zlim=cbind(c(-6,2), c(-2,1)))
plot(simu, zlim=c(-6,2))
plot(simu, zlim="joint")
RFoptions(seed=NA)