## Interactive sites selection:
# ID=pfInteractive()
library(devtools)
install_github("paleofire/paleofire")
library(paleofire)
## Site selection using criterions
data(paleofiresites)
names(paleofiresites)
ID=pfSiteSel(id_land_desc!="MARI" , id_site_type!="FLUV" & id_site_type!="LFLU")
plot(ID)
ID=pfSiteSel(lat>0 & rf99==6)
plot(ID)
ID=pfSiteSel(lat>0, rf99==6 | l12==1)
plot(ID)
ID=pfSiteSel(lat>0, rf99==6 | l12==1)
plot(ID)
ID=pfSiteSel(lat>0, rf99==6 | l12==1, date_int<=2000 & num_samp>30)
plot(ID)
ID=pfSiteSel(lat>0, rf99==6 | l12==1, elev<=1000)
plot(ID)
## Associated plots
plot(ID,zoom="sites")
## Simple test for transforming data
# Select site 1 (Cygnet Lake)
ID1=pfSiteSel(id_site==1)
plot(ID1)
# Transformation of data
TR=pfTransform(ID1,method=c("MinMax", "Box-Cox", "Z-Score"))
# Plot Transformed and raw data
# First retrieve raw data for Cygnet using pfExtract
RAW=pfExtract(ID1)
dev.off()
par(mfrow=(c(2,1)))
plot(RAW[,3],RAW[,4],type="l")
plot(TR$Age,TR$TransData,type="l")
## Transforming and Compositing
## Example 1: Usage as in Power et al. 2008
## Data transformation
TR1=pfTransform(ID, method=c("MinMax","Box-Cox","Z-Score"),BasePeriod=c(200,2000),MethodType="SIEV")
TR1=pfTransform(ID, method=c("MinMax","Box-Cox","Z-Score"),BasePeriod=c(200,2000))
## Diagnostic pdf file with transformed series:
# pfDiagnostic(ID, method=c("MinMax","Box-Cox","Z-Score"),BasePeriod=c(200,2000),
# FileName = "Diagnostic.pdf")
## Compositing: basic binning procedure
COMP=pfComposite(TR1, binning=TRUE, bins=seq(0,8000,500))
plot(COMP)
## The result matrix can be saved
# write.csv(COMP$Result,file="temp.csv")
## Compositing: Using the locfit package equivalent procedure to Daniau et al. 2012
COMP2=pfCompositeLF(TR1, tarAge=seq(-50,8000,20), binhw=20, hw=500,nboot=100)
plot(COMP2)
## Composite charcoal record for western North America:
ID=pfSiteSel(id_region=="WNA0" & l12==1)
plot(ID, zoom="world" )
## Transform data
res3=pfTransform(ID,method=c("MinMax","Box-Cox","Z-Score"),BasePeriod=c(200,4000),Interpolate=FALSE)
## Composite
comp=pfComposite(res3,bins=seq(from=-500,to=12500,by=1000))
plot(comp)
## Kruskal Wallis Anova
comparison=pfKruskal(comp,alpha=0.05, p.adj="none")
plot(comparison,trend=TRUE)
## Kruskal Wallis Anova
comparison=pfKruskal(res3,alpha=0.05,bins=seq(from=-500,to=12500,by=1000))
plot(comparison,ylim=c(-5,7))
## Example: Obtaining the number of sites at each binned interval from pfComposite:
# Select some sites:
ID=pfSiteSel(lat>0, rf99==6 | l12==1, elev<=1000, id_region=="EURO")
# Transform
TR=pfTransform(ID,method=c("MinMax", "Box-Cox", "Z-Score"))
# Compositing
COMP=pfComposite(TR, binning=TRUE, bins=seq(0,12000,500))
plot(COMP)
# Count number of Non NA values in binned data (i.e. number of sites at each bin)
n=rowSums(!is.na(COMP$BinnedData))
# Plot it
plot(COMP$BinCentres,n,type="l")
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