RandomFields (version 3.1.12)

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). The plot methods not described here are described together with the class itself, for instance, RFfit, RFempVariog RMmodel.

Usage

RFplotSimulation(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, file=NULL, speed = 0.3,
 height.pixel=300, width.pixel=height.pixel,
 ..., plotmethod="image")
RFplotSimulation1D(x, y, nmax=6,
  plot.variance=!is.null(x@.RFparams$has.variance) && x@.RFparams$has.variance,
  legend=TRUE, ...)
## S3 method for class 'RFgridDataFrame,missing':
plot(x, y, ...)
## S3 method for class 'RFpointsDataFrame,missing':
plot(x, y, ...)
## S3 method for class 'RFspatialGridDataFrame,missing':
plot(x, y, ...)
## S3 method for class 'RFspatialPointsDataFrame,missing':
plot(x, y, ...)
## S3 method for class 'RFgridDataFrame,matrix':
plot(x, y, ...)
## S3 method for class 'RFpointsDataFrame,matrix':
plot(x, y, ...)
## S3 method for class 'RFspatialGridDataFrame,matrix':
plot(x, y, ...)
## S3 method for class 'RFspatialPointsDataFrame,matrix':
plot(x, y, ...)
## S3 method for class 'RFgridDataFrame,data.frame':
plot(x, y, ...)
## S3 method for class 'RFpointsDataFrame,data.frame':
plot(x, y, ...)
## S3 method for class 'RFspatialGridDataFrame,data.frame':
plot(x, y, ...)
## S3 method for class 'RFspatialPointsDataFrame,data.frame':
plot(x, y, ...)
## S3 method for class 'RFgridDataFrame,RFgridDataFrame':
plot(x, y, ...)
## S3 method for class 'RFgridDataFrame,RFpointsDataFrame':
plot(x, y, ...)
## S3 method for class 'RFpointsDataFrame,RFgridDataFrame':
plot(x, y, ...)
## S3 method for class 'RFpointsDataFrame,RFpointsDataFrame':
plot(x, y, ...)
## S3 method for class 'RFspatialGridDataFrame,RFspatialGridDataFrame':
plot(x, y, ...)
## S3 method for class 'RFspatialGridDataFrame,RFspatialPointsDataFrame':
plot(x, y, ...)
## S3 method for class 'RFspatialPointsDataFrame,RFspatialGridDataFrame':
plot(x, y, ...)
## S3 method for class 'RFspatialPointsDataFrame,RFspatialPointsDataFrame':
plot(x, y, ...)
## S3 method for class 'RFspatialGridDataFrame':
persp(x, ..., zlab="")
## S3 method for class 'RFspatialGridDataFrame':
contour(x, ...)

Arguments

x

object of class RFsp or RMmodel; in the latter case, x can be any sophisticated model but it must be either

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.

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.

file, speed, height.pixel, width.pixel

In case MARGIN.movie and file is given an 'avi' movie is stored using the mencoder command with speed argument speed. As temporary files file__###.png of size width

...

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

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

plotmethod

string or function. Internal.

zlab

character. See persp

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
StartExample()


## 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)
image(model, xlim=c(-10, 20), fct.type="Variogram")
persp(model, xlim=c(-10, 20), fct.type="Variogram")

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

## define the locations:
from <- 0
step <- .1 
len <- 50  # nicer if len=100 %ok
if(RFoptions()$internal$examples_red){warning("reduced 'len'"); len<-3} 
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, 1, len=21)
 
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 <- 200
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, 0.5)
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()