Home

Welcome to the stat_tests_correlated_climdata wiki!

#Multivariate tests for autocorrelated data

Significance tests for autocorrelated data (modified t-test and moving blocks bootstrapping) and two field significance tests to be used with the modified t-test (Walkers test and False discovery rate). The moving blocks bootstrapping also includes a test for field significance.

##Details:

• Package: multVarTestAutocorr
• Type: Package
• Version: 1.0
• Date: 2016-02-23
• License: GPL-2

This package contains four main functions, modTtest3d, mbbTest, walkerTest and fdrTest. All functions are designed for spatial data (longitude x latitude). modTtest3d and mbbTest need multiple timesteps while walkerTest and fdrTest can have multiple time steps but do not need to also work with longitude x latitide data only .

##Author: Ruth Lorenz

email: ruthl.lorenz22@gmail.com

##References: Zwiers and von Storch, 1995, Taking serial correlation into account in tests of the mean, J. Clim, 8, p. 336--351.

Wilks, D.S., 1997, Resampling Hypothesis Tests for Autocorrelated Fields, J. Clim., 10, p.65--82.

Wilks, D.S., 2006, On "Field Significance" and the False Discovery Rate, J. Appl. Meteorol. Climatol., 45, p. 1181--1189.

##Examples:

x<-array(NA,dim=c(20,50,31))
y<-array(NA,dim=c(20,50,31))

for (lon in 1:20){
        for (lat in 1:50){
                #create timeseries with AR(1) correlation
                x[lon,lat,]<-arima.sim(list(ar = 0.3),n=31,rand.gen=rnorm,sd=0.1,mean=0)
                y[lon,lat,]<-arima.sim(list(ar = 0.3),n=31,rand.gen=rnorm,sd=0.1,mean=0)
        }
}

test3d<-modTtest3d(x,y,alternative = c("two.sided"),conf.level=0.95)
print(test3d)

walk<-walker.test(test3d$p.value, siglev=0.05)
print(walk)

fdr<-fdr.test(test3d$p.value, siglev=0.05)
print(fdr)

mbb <- mbbTest(x,y,siglev=0.05,nb=10,verbose=FALSE)
print(mbb)