RandomFields: plot-method – R documentation

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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, ...)

## S4 method for signature 'RFgridDataFrame,missing'
plot(x, y, ...)
## S4 method for signature 'RFpointsDataFrame,missing'
plot(x, y, ...)
## S4 method for signature 'RFspatialGridDataFrame,missing'
plot(x, y, ...)
## S4 method for signature 'RFspatialPointsDataFrame,missing'
plot(x, y, ...)

## S4 method for signature 'RFgridDataFrame,matrix'
plot(x, y, ...)
## S4 method for signature 'RFpointsDataFrame,matrix'
plot(x, y, ...)
## S4 method for signature 'RFspatialGridDataFrame,matrix'
plot(x, y, ...)
## S4 method for signature 'RFspatialPointsDataFrame,matrix'
plot(x, y, ...)

## S4 method for signature 'RFgridDataFrame,data.frame'
plot(x, y, ...)
## S4 method for signature 'RFpointsDataFrame,data.frame'
plot(x, y, ...)
## S4 method for signature 'RFspatialGridDataFrame,data.frame'
plot(x, y, ...)
## S4 method for signature 'RFspatialPointsDataFrame,data.frame'
plot(x, y, ...)

## S4 method for signature 'RFgridDataFrame,RFgridDataFrame'
plot(x, y, ...)
## S4 method for signature 'RFgridDataFrame,RFpointsDataFrame'
plot(x, y, ...)
## S4 method for signature 'RFpointsDataFrame,RFgridDataFrame'
plot(x, y, ...)
## S4 method for signature 'RFpointsDataFrame,RFpointsDataFrame'
plot(x, y, ...)
## S4 method for signature 'RFspatialGridDataFrame,RFspatialGridDataFrame'
plot(x, y, ...)
## S4 method for signature 'RFspatialGridDataFrame,RFspatialPointsDataFrame'
plot(x, y, ...)
## S4 method for signature 'RFspatialPointsDataFrame,RFspatialGridDataFrame'
plot(x, y, ...)
## S4 method for signature 'RFspatialPointsDataFrame,RFspatialPointsDataFrame'
plot(x, y, ...)

## S4 method for signature '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 stationary or a variogram model
`y`	ignored in most methods; in case of `RFplotSimulation` data might be given
`MARGIN`	vector of two; two integer values giving the coordinate dimensions w.r.t. whether 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.pixel` x `height.pixel` are created.
`...`	arguments to be passed to methods; mainly graphical arguments, or further models in case of class `CLASS_CLIST`, 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 (excluding the plot for the variances, if applicable). If `select.variables` is a vector of integers then exactly these components are shown. If `select.variables` is a list, each element should be a vector up to length l <= 3: l=1 the component is shown in the usual way l=2 the two components are interpreted as vector and arrows are plotted l=3 the first component is shown as single component; the remaining two component are interpreted as a vector and plotted into the picture of the first component
`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 with the value ‘joint’ indicating that all plotted components shall have the same zlim OR a matrix with two rows, where the i-th column gives the zlim of the i-th variable OR a list with entries named `data` and `var` if a separate `zlim` for the Kriging variance is to be used.
`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 CLASS_CLIST. 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.

Methods

signature(x = "RFspatialGridDataFrame", y = "missing"): Generates nice image plots of simulation results for simulation on a grid and space-time-dimension >1. If space-time-dimension >2, plots are on 2-dimensional subspaces. Handles multivariate random fields (.RFparams$vdim>1) as well as repeated iid simulations (.RFparams$vdim>n).
signature(x = "RFspatialGridDataFrame", y = "RFspatialPointsDataFrame"): Similar to method for y="missing", but additionally adds the points of y. Requires MARGIN.slices=NULL and all.equal(x@.RFparams, y@.RFparams).
signature(x = "RFspatialGridDataFrame", y = "matrix"): Similar to method for y="missing", but additionally adds the points of y. Requires MARGIN.slices=NULL and all.equal(x@.RFparams, y@.RFparams).
signature(x = "RFspatialPointsDataFrame", y = "RFspatialGridDataFrame"): Throws an error. Probably x and y have been interchanged.
signature(x = "RFspatialPointsDataFrame", y = "missing"): Similar to method for class RFspatialGridDataFrame, but for non-gridded simulation results. Instead of a grid, only existing points are plotted with colors indicating the value of the random field at the respective location. Handles multivariate random fields (.RFparams$vdim>1) as well as repeated iid simulations (.RFparams$vdim>n).
signature(x = "RFspatialPointsDataFrame", y = "RFspatialPointsDataFrame"): Similar to method for y="missing", but additionally adds the points of y. Requires all.equal(x@.RFparams, y@.RFparams).
signature(x = "RFgridDataFrame", y = "missing"): Generates plots of simulation results for space-time-dimension =1. Handles different values for the number of repetitions as well as multivariate responses.
signature(x = "RFpointsDataFrame", y = "missing"): Similar to method for class RFgridDataFrame, but for non-gridded data.

Author(s)

Alexander Malinowski, Martin Schlather, schlather@math.uni-mannheim.de, https://www.wim.uni-mannheim.de/schlather/

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)
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
 
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(model, x=cbind(x))
ev1 <- RFvariogram(data=X, bin=bin)
plot(ev1)

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




#############################################################
## 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, x=x, y=y)

interpolate <- RFinterpolate(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, 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")

RandomFields

Simulation and Analysis of Random Fields

v3.3.10

GPL (>= 3)

Authors

Martin Schlather [aut, cre], Alexander Malinowski [aut], Marco Oesting [aut], Daphne Boecker [aut], Kirstin Strokorb [aut], Sebastian Engelke [aut], Johannes Martini [aut], Felix Ballani [aut], Olga Moreva [aut], Jonas Auel[ctr], Peter Menck [ctr], Sebastian Gross [ctr], Ulrike Ober [ctb], Paulo Ribeiro [ctb], Brian D. Ripley [ctb], Richard Singleton [ctb], Ben Pfaff [ctb], R Core Team [ctb]

Initial release