# Plots `pgrid` object. `plot.pgrid` plots a grid of pixels based on a `pgrid` object. ```r ## S3 method for class 'pgrid' plot(x, set, col = "gray", add = FALSE, type = "confidence", ...) ``` ## Arguments - `x`: An `pgrid` object returned from the `pgrid` function. - `set`: A vector which contains the indices of the pixels/cells that should be plotted. OR a `confreg` object from the `confreg` function. See Details. - `col`: The color of the plotted pixels. - `add`: A logical value indicating whether the pixels should be added to an existing plot (`add = TRUE`) or should the pixels be plotted on a new plot (`add = FALSE`). - `type`: The type of set of plot if `set` of of class `confreg`. The default is `"confidence"`, while the other option is `complement`, based on the components of the `confreg` object. - `...`: Additional arguments that will be passed to the `image` function (assuming `add=FALSE`). ## Returns This function does not return anything; it only creates a new plot or modifies an existing plot. ## Details If a vector of pixel indices is supplied to `set`, then those pixels will be colored `col` by this function and the `type` argument has no effect. On the other hand, if the `set` argument is of class `confreg`, then the function digs in to display either the `confidence` or `complement` set in the `confreg` object. In that case, `type` is used to decide which set to display. ## Examples ```r library(SpatialTools) # Example for exceedance regions set.seed(10) # Load data data(sdata) # Create prediction grid pgrid <- create.pgrid(0, 1, 0, 1, nx = 26, ny = 26) pcoords <- pgrid$pgrid # Create design matrices coords = cbind(sdata$x1, sdata$x2) X <- cbind(1, coords) Xp <- cbind(1, pcoords) # Generate covariance matrices V, Vp, Vop using appropriate parameters for # observed data and responses to be predicted spcov <- cov.sp(coords = coords, sp.type = "exponential", sp.par = c(1, 1.5), error.var = 1/3, finescale.var = 0, pcoords = pcoords) # Predict responses at pgrid locations krige.obj <- krige.uk(y = as.vector(sdata$y), V = spcov$V, Vp = spcov$Vp, Vop = spcov$Vop, X = X, Xp = Xp, nsim = 100, Ve.diag = rep(1/3, length(sdata$y)) , method = "chol") # Simulate distribution of test statistic for different alternatives statistic.sim.obj.less <- statistic.sim(krige.obj = krige.obj, level = 5, alternative = "less") statistic.sim.obj.greater <- statistic.sim(krige.obj = krige.obj, level = 5, alternative = "greater") # Construct null and rejection sets for two scenarios n90 <- exceedance.ci(statistic.sim.obj.less, conf.level = .90, type = "null") r90 <- exceedance.ci(statistic.sim.obj.greater,conf.level = .90, type = "rejection") # Plot results plot(pgrid, n90, col="blue", add = FALSE, xlab = "x", ylab = "y") plot(pgrid, r90, col="orange", add = TRUE) legend("bottomleft", legend = c("contains true exceedance region with 90 percent confidence", "is contained in true exceedance region with 90 percent confidence"), col = c("blue", "orange"), lwd = 10) ``` ## Author(s) Joshua French

plot.pgrid function