1d:T79a,# Return critical value of distribution. `statistic.cv` returns the critical value of the distribution of the test statistics from `statistic.sim` based on the specified confidence level. However, it is not recommended for general usage. It is recommedned that the `exceedance.ci` function be used to automatically create confidence regions. ```r statistic.cv(statistic.sim.obj, conf.level = 0.95) ``` ## Arguments - `statistic.sim.obj`: An object returned from the `statistic.sim` function. - `conf.level`: The desired confidence level of the confidence interval we want to construct. ## Returns Returns the desired critical value. ## 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") # Calculate quantiles of distribution of statistic q90.less <- statistic.cv(statistic.sim.obj.less, conf.level = .90) q90.greater <- statistic.cv(statistic.sim.obj.greater, conf.level = .90) ``` ## Author(s) Joshua French

statistic.cv function