RandomFields (version 3.0.62)

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 'RFspatialDataFrame,missing':
plot(x, y,
 MARGIN=c(1,2), MARGIN.slices=NULL,
 n.slices = if (is.null(MARGIN.slices)) 1 else 10,
 nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), select.variables, zlim, legend=TRUE,
 MARGIN.movie = NULL, ...)
## S3 method for class 'RFspatialDataFrame,RFspatialGridDataFrame':
plot(x, y,
 MARGIN=c(1,2), MARGIN.slices=NULL,
 n.slices = if (is.null(MARGIN.slices)) 1 else 10,
 nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), select.variables, zlim, legend=TRUE,
  MARGIN.movie = NULL,...)
## S3 method for class 'RFspatialDataFrame,RFspatialPointsDataFrame':
plot(x, y,
 MARGIN=c(1,2), MARGIN.slices=NULL,
 n.slices = if (is.null(MARGIN.slices)) 1 else 10,
 nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), select.variables, zlim, legend=TRUE,
  MARGIN.movie = NULL,...)
## S3 method for class 'RFspatialDataFrame,matrix':
plot(x, y,
 MARGIN=c(1,2), MARGIN.slices=NULL,
 n.slices = if (is.null(MARGIN.slices)) 1 else 10,
 nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), select.variables, zlim, legend=TRUE,
  MARGIN.movie = NULL,...)
## S3 method for class 'RFspatialDataFrame,data.frame':
plot(x, y,
 MARGIN=c(1,2), MARGIN.slices=NULL,
 n.slices = if (is.null(MARGIN.slices)) 1 else 10,
 nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), select.variables, zlim, legend=TRUE,
  MARGIN.movie = NULL,...)

## S3 method for class 'RFspatialGridDataFrame':
persp(x, y,
 MARGIN=c(1,2), MARGIN.slices=NULL,
 n.slices = if (is.null(MARGIN.slices)) 1 else 10,
 nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), select.variables, zlim, legend=TRUE, 
 MARGIN.movie = NULL,...)

## S3 method for class 'RFdataFrame,missing':
plot(x, y, nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), legend=TRUE, ...)
## S3 method for class 'RFdataFrame,RFdataFrame':
plot(x, y, nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), legend=TRUE, ...)
## S3 method for class 'RFdataFrame,matrix':
plot(x, y, nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), legend=TRUE, ...)
## S3 method for class 'RFdataFrame,data.frame':
plot(x, y, nmax=6, 
 plot.variance = (!is.null(x@.RFparams$has.variance) &&
 x@.RFparams$has.variance), legend=TRUE, ...)
## S3 method for class 'RFempVariog,missing':
plot(x, model=NULL,
%nmax.phi=6, nmax.theta=3, nmax.T=3,
nmax.phi=NA, nmax.theta=NA, nmax.T=NA,
 plot.nbin=TRUE, plot.sd=FALSE, variogram=TRUE, boundaries = TRUE,...)
## S3 method for class 'RFfit,missing':
plot(x, model=NULL,  fit.method="ml",
 nmax.phi=NA, nmax.theta=NA, nmax.T=NA,
 plot.nbin=TRUE, plot.sd=FALSE, variogram = TRUE, boundaries = TRUE,...)
## S3 method for class 'RMmodel,missing':
plot(x, y, dim=1, n.points=200,
 fct.type=NULL, MARGIN, fixed.MARGIN, maxchar=15, ...)
## 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. Currently only works for grids.

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. Or n.slices is a vector of 3 elements: start, end, length. Currently only works for grids.

nmax

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

MARGIN.movie

integer. If given a sequence of figures is shown for this direction. This option is in an experimental stage. It works only for grids.

...

arguments to be passed to methods; mainly graphical arguments, or further models in case of class 'RMmodel', see Details.

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

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

boundaries

logical; only for class(x)=="RFempVario" and the anisotropic case where model is given. As the empirical variogram is calculated on a sector of angles, no exact variogram curve corresponds to the mean values in this

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"

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

plot.variance

logical, whether variances should be plotted if available

select.variables

vector of integers or list of vectors. The argument 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

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 t

maxchar

maximum number of characters used in the legend (for multiplots only)

Details

Internally, ... are passed to image and plot.default, respectively; if, by default, multiple colors, xlabs or ylabs are used, also vectors of suitable length can be passed as col, xlab and ylab, respectively.

One exception is the use of ... in plot for class 'RMmodel'. Here, further models might be passed. All models must have names starting with model. If '.' is following in the name, the part of the name after the dot is shown in the legend. Otherwise the name is ignored and a standardized name derived from the model definition is shown in the legend. Note that for the first argument a name cannot be specified.

See Also

RFpar.

Examples

RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again


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


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

plot(model)
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 3 # nicer, but also time consuming if len=100
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 <- seq(0, 10, 0.05)
bin <- seq(from=0, by=.2, to=3)

model <- RMexp()
X <- RFsimulate(x=cbind(x), model=model)
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)
ev2 <- RFempiricalvariogram(data=X, bin=bin, phi=3)
plot(ev2, model=list(exp = model))


#############################################################
## plot Fitting results
x <- seq(0, 2, len=if (interactive()) 21 else 6)
 
model <- RMexp(Aniso = cbind(c(10,0), c(0,1)))
X <- RFsimulate(x=cbind(x,x), model=model)
fit <- RFfit(~RMexp(Aniso=diag(c(NA, NA))), data=X, fit.nphi = 2,
             modus="easygoing")
plot(fit) 


#############################################################
## plot Kriging results 
model <- RMwhittle(nu=1.2, scale=2)
n <- if (interactive()) 200 else 5
x <- runif(n, max=step*len/2) 
y <- runif(n, 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 10)
simulated <- RFsimulate(model = model, x=x, y=y, n=1)
plot(simulated)
plot(simulated, select.variables=list(1, 1:3, 4))



#############################################################
## options for the zlim argument
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)

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")

FinalizeExample()