dplRCon (version 1.0)

correlation.function: Performs correlations functions

Description

Uses the imported climate variables and tree ring data to produce seasonal correlation functions. Also, uses the bootstrapped chronologies to produce confidence intervals.

Usage

correlation.function(climate.anom.season.data, site.chron.data, site.boot.data, period.RF, col.names.season, Climate.name, Subset.name)

Arguments

climate.anom.season.data

climate data anomalies for seasons

site.chron.data

site chronologies, matrix: rows=year, col=subset.1, subset.2,fullest

site.boot.data

bootstrapped site chronologies, list: matrices for each subset with: row=year, col=bootstrapped series

period.RF

the period used to calculate response functions, vector: (start,end)

col.names.season

col.names.season<- list("SON_2", "DJF_2", "MAM_2", "JJA_2", "SON_1", "DJF_1", "MAM_1", "JJA_1", "SON", "DJF", "MAM", "JJA")

Climate.name

name of the climate variable for which correlation functions are being calculated

Subset.name

names given to each of the subsets.

Value

corr.site.1

The correlations between the climate variable and the site chronology for the 1st subset.

corr.site.2

The correlations between the climate variable and the site chronology for the 12st subset.

percentile.ci.1

The percentile confidence intervals for the 1st subset.

percentile.ci.2

The percentile confidence intervals for the 2st subset.

Other summary varibles

summary.ci.1

summary.ci.2

t.mean

Test for correlation equal zero

t.meanequal

Test for correlations from the two subsets are equal

percentile.ci

Examples

## Not run: 
# period.RF<-c(1900,1990)
# col.names.season <- list("SON_2", "DJF_2", "MAM_2", "JJA_2", "SON_1", "DJF_1", "MAM_1","JJA_1",
#          "SON", "DJF", "MAM", "JJA")
# ##  Full dataset
# site.full <- site.chron(spline200.sub0.2000.n$sub.series.stand, aver.by.tree=F)
# site.chron.data <- cbind(site.full$aver.site, site.full$aver.site)
# site.boot.full <- ts(boot.full$boot.series.mean, start=tsp(site.full$aver.site)[1] )
# site.boot.data<-list(site.boot.full, site.boot.full)
# 
# corr.SOI.full<-correlation.function(SOI.anom.season.data, site.chron.data,site.boot.data,
#       period.RF, col.names.season,
#       Climate.name="SOI", Subset.name=c("0-20cm","20-200cm" ) )
# corr.prec.full<-correlation.function(prec.anom.season.data, site.chron.data,site.boot.data,
#      period.RF, col.names.season,
#      Climate.name="SOI", Subset.name=c("0-20cm","20-200cm" ) )
# corr.temp.full<-correlation.function(temp.anom.season.data, site.chron.data,site.boot.data,
#      period.RF, col.names.season,
#      Climate.name="SOI", Subset.name=c("0-20cm","20-200cm" ) )
# 
# ##	Near vs Far
# site.0.20  <- site.chron(spline200.sub0.20.n$sub.series.stand, aver.by.tree=F)
# site.20.200 <- site.chron(spline200.sub20.2000.n$sub.series.stand, aver.by.tree=F)
# site.chron.data <- cbind(site.0.20$aver.site, site.20.200$aver.site)
# 
# site.boot.0.20 <- ts(boot.0.20$boot.series.mean, start=tsp(site.0.20$aver.site)[1] )
# site.boot.20.200 <- ts(boot.20.2000$boot.series.mean, start=tsp(site.20.200$aver.site)[1] )
# site.boot.data<-list(site.boot.0.20, site.boot.20.200)
# 
# corr.SOI<-correlation.function(SOI.anom.season.data, site.chron.data, site.boot.data,
#    period.RF, col.names.season,
#    Climate.name="SOI",Subset.name=c("0-20cm","20-200cm" ) )
# corr.prec<-correlation.function(prec.anom.season.data, site.chron.data,          site.boot.data,
#    period.RF, col.names.season,
#    Climate.name="SOI", Subset.name=c("0-20cm","20-200cm" ) )
# corr.temp<-correlation.function(temp.anom.season.data, site.chron.data, site.boot.data,
#    period.RF, col.names.season,
#    Climate.name="SOI", Subset.name=c("0-20cm","20-200cm" ) )
# ## End(Not run)