Detachpos2 see how to reference a contributed package

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Unformatted text preview: R. Must do for each new session. library( fields) library( evd) library( evdbayes) library( ismev) library( SpatialExtremes) R preliminaries See hierarchy of loaded packages: search() # Detach the ’SpatialExtremes’ package. detach(pos=2) See how to reference a contributed package: citation("fields") R preliminaries Simulate some random fields From the help file for the fields function sim.rf help( sim.rf) #Simulate a Gaussian random field with an exponential # covariance function, range parameter = 2.0 and the # domain is [0, 5] × [0, 5] evaluating the field at a 100 × 100 # grid. grid < − list( x= seq( 0,5„100), y= seq(0,5„100)) obj < − Exp.image.cov( grid=grid, theta=.5, setup=TRUE) look < − sim.rf( obj) # Now simulate another ... look2 < − sim.rf( obj) R preliminaries Plotting the simulated fields # setup the plotting device to have two plots side-by-side set.panel(2,1) # Image plot with a color scale. image.plot( grid$x, grid$y, look) title("simulated gaussian field") image.plot( grid$x, grid$y, look2) title("another (independent) realization ...") R preliminaries Basics of plotting in R: • First must open a device on which to plot. – Most plotting commands (e.g., plot) open a device (that you can see) if one is not already open. If a device is open, it will write over the current plot. – X11() will also open a device that you can see. – To create a file with the plot(s), use postscript, jpeg, png, or pdf (before calling the plotting routines. Use to close the device and create the file. • plot and many other plotting functions use the par values to define various characteristics (e.g., margins, plotting symbols, character sizes, etc.). Type help( plot) and help( par) for more information. R preliminaries Simple plot example. plot( 1:10, z <- rnorm(10), type="l", xlab="", ylab="z", main="Std Normal Random Sample") points( 1:10, z, col="red", pch="s", cex=2) lines( 1:10, rnorm(10), col="blue", lwd=2, lty=2) # Make a standard normal qq-plot of ’z’. qqnorm( z) # Shut off the device. Background on Extreme Value Analysis (EVA) Motivation Sums, averages and proportions (Normality) • Central Limit Theorem (CLT) • Limiting distribution of binomial distribution Extremes • Normal distribution inappropriate • Bulk of data may be misleading • Extremes are often rare, so often not enough data Background on Extreme Value Analysis (EVA) Simulations # Simulate a sample of 1000 from a Unif(0,1) distribution. U < − runif( 1000) hist( U) # Simulate a sample of 1000 from a N(0,1) distribution. Z < − rnorm( 1000) hist( Z) # Simulate a sample of 1000 from a Gumbel distribution. M < − rgev( 1000) hist( M) Background on Extreme Value Analysis (EVA) Simulations # Simulate 1000 maxima from samples of size 30 from # the normal distribution. Zmax < − matrix( NA, 30, 1000) dim( Zmax) for( i in 1:1000) Zmax[,i] < − rnorm( 30) Zmax < − apply( Zmax, 2, max) dim( Zmax) class( Zmax) length( Zmax) class( Zmax) hist( Zmax, breaks="...
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