exceedance.ci returns a confidence set for an exceedance region or contour line.
Usage
exceedance.ci(statistic.sim.obj, conf.level = 0.95, type = "null")
Arguments
statistic.sim.obj
An object returned from the statistic.sim function.
conf.level
The desired confidence level of the confidence region.
type
Whether the function should return the null region or rejection region of exceedance confidence region Options are "null" or "rejection". Default is "null".
Value
Returns a numeric vector with the set of pixels comprising the null or rejection region related to statistic.sim.obj.
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")
# 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)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(ExceedanceTools)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ExceedanceTools/exceedance.ci.Rd_%03d_medium.png", width=480, height=480)
> ### Name: exceedance.ci
> ### Title: Return confidence region
> ### Aliases: exceedance.ci
>
> ### ** Examples
>
> library(SpatialTools)
# This research was partially supported under NSF Grant ATM-0534173
>
> # 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)
>
>
>
>
>
> dev.off()
null device
1
>