statistic.cv {ExceedanceTools}

Return critical value of distribution.

Description

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.

Usage

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.

Value

Returns the desired critical value.

Author(s)

Joshua French

Examples

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)