# ShowModels

0th

Percentile

##### Interactive Choice of Models and Parameters

ShowModels is an interactive plot for the selection of models and their one- or two-dimensional simulations; it also allows for the fitting of variogram models by eye.

CURRENTLY NOT AVAIABLE IN VERSION 2 OF RANDOMFIELDS. PLEASE USE VERSION 1.3.47 INSTEAD.

Keywords
spatial
##### Usage
ShowModels(x, y=NULL,
covx=ifelse(is.null(empirical), diff(range(x))/5,
max(empirical$c)), fixed.rs=TRUE, method=NULL, empirical=NULL, model=NULL, param=NULL, anisotropy = FALSE, all.param=NULL, legends = TRUE, register=0, Mean=NULL, erase=TRUE, x.fraction=0.60, cex.names=1, covx.default = 100, link.fct=NULL, Zlim=NULL, Col.rect="red", Col.bg="blue", Col.sep="grey", Col.line="red", Col.txt="black", Col.flash="red", Col.vario="blue", Col.main="black", Col.model=c("red", "black"), vario.lty=c(1,2), cex.leg = 0.7 * cex.names, cex.eval = 0.8 * cex.names, update=TRUE, screen.new=TRUE, use.outer.RFparameters=FALSE, debug=FALSE, ...) ##### Arguments x if NULL simulations are not performed; otherwise it gives the$x$coordinates of a grid as a sequence of increasing numbers y if NULL at most one-dimensional simulations are performed (depending on the value of x); otherwise y gives the$y$coordinates of a two-dimensional grid (as a sequence of increasing numbers). covx if a single value is given, it is the largest distance for which the covariance functions or the variograms are plotted; otherwise the models are plotted for the given values, and the origin. fixed.rs if TRUE then the same random seed is used for all simulations until the user clicks on the formula, the title or the subtitles. method simulation method, see RFMethods; if NULL then a suitable simulation method is chosen automatically. empirical empirical variogram; a list as returned by EmpiricalVariogram. Also empirical variograms with a pair number of anisotropy directions may be passed. Then the first and the middle model covariance model, see CovarianceFct, or type PrintModelList() to get all options. If given, this model is shown at the begin param parameter vector: param=c(mean, variance, nugget, scale,...); the parameters must be given in this order; see CovarianceFct for more details. Only considered if anisotropy logical. If TRUE then an isotropic model is considered. all.param all.param=c(mean, variance, nugget, scale); the parameters must be given in this order; If all.param is given then the parameters of all covariance functions are set to the given values. The values are overwritten legends if TRUE then a legend is added to the two-dimensional plot. register register where intermediate results of the simulations are stored, see also GaussRF. Mean mean of the random field erase parameter of split.screen, which is called at the very beginning x.fraction the current screen is split into 2 x 2 screens. The parameter x.fraction gives the size of the left screens in the x directions as part of 1. See also the Details. cex.names font size for model names covx.default if length(cov.x)==1 then$[0, \code{cov.x}]$is covered by covx.default points of equal distance link.fct NULL or function(values) or "MaxStable". Transformation of the Gaussian random field. If link.fct="MaxStable" then max-stable random fields are simulated for the given covariance function and the Zlim Vector of two elements or list of two vectors of two elements. Graphical limits for the Gaussian random process (and the transformed field). Col.rect colour for interactive plot; see eval.parameters. Col.bg colour for interactive plot; see eval.parameters. Col.sep colour for interactive plot; see eval.parameters. Col.line colour for interactive plot; see eval.parameters. Col.txt colour for interactive plot; see eval.parameters. Col.flash colour for the previously chosen model Col.vario colour for the empirical variogram plot Col.main colour for the title of the random field Col.model vector of two colours for plotting the variogram of the Gaussian random field and the transformed field vario.lty vector of two line types for primary and secondary axis of the variogram cex.leg font size used in the legends cex.eval font size used in the menue entries update logical. If TRUE the plots are updated after each interactive change of the values. Otherwise, the bottom 'simulate' is added in the menu. screen.new logical. If FALSE the screen is erased before a simulation and completely rebuild; otherwise the screen is updated. If FALSE flickering appears during the update of the current screen, otherwise it may happen durin use.outer.RFparameters logical. If FALSE the following parameters usually set by RFparameters are internally set • PracticalRange=FALSE • PrintLevel=1if debug logical. If TRUE then internally the RFparameter()$PrintLevel is set to 5.
...
additional graphics options for the plot of the one- or two-dimensional simulations, see plot and image.
##### Details

The interactive plot consists of 3 parts:

• top left: graph of the covariance function or the variogram. In caseempiricalis given the empirical variogram is also plotted. Iflink.fctis given, then also the covariance function or the variogram is plotted. If the correlation model is for a non-stationary random field, the variogram for the transformed random field is not estimated in a primitive way -- this is indicated with a star in the legend
• bottom left: one- or two-dimensional simulation
• right:

-- list of implemented models; a specific model is chosen by the left mouse button, or: -- menu for the parameters of the chosen model. The list includes the variance, a nugget effect, the mean and the scale or the anisotropy parameters. Further, some global parameters can be changed. They are thePracticalRange(seeRFparametersfor details) and the angle of the main variogram direction (or NA, then it follows the angle of the anisotropy). Finally, the user can choose between the plot of the covariance and the corresponding variogram.

The interactive plot is left by clicking any mouse button different from the left when the top right part is active.

##### Value

• list of the last model and its parameters.

CovarianceFct, eval.parameters, GaussRF, RFMethods, RandomFields.

• ShowModels
##### Examples
# first example: one-dimensional simulations

# library(RandomFields)

options(locatorBell=FALSE)
x <-  seq(1,10,0.1)
ShowModels(x=x, covx=10, cov.def=100, type="l")

x <-  seq(1,10,0.1)
ShowModels(x=x, y=x, covx=10, cov.def=100)

# second example: two-dimensional simulations and
#                 empirical variogram
dx <- runif(300,0,8)
dy <- runif(300,0,8)
dz <- GaussRF(x=dx, y=dy, grid=FALSE, model="gaus",
param=c(1,2,1,2))
ev <- EmpiricalVariogram(x=dx, y=dy, data=dz, grid=FALSE,
bin=(-1:20)/4)
x <-  seq(1,5,0.1);
ShowModels(x=x, y=x, empirical=ev)

# third example: two-dimensional anistropic simulations and
#                link functionx <- seq(1,10,0.1)
ShowModels(x=x, y=x, link=function(x) exp(x),
model=list(list(model="spheric", var=1, aniso=c(1,0,0,5))))

x <-  seq(1,10,0.1)
model=list(list(model="gauss",var=1, scale=1)), type="l")