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smooth-curve_07_code.Rmd
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smooth-curve_07_code.Rmd
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---
title: smooth-curve.R
output:
html_document:
collapsed: no
fig_caption: yes
highlight: haddock
number_sections: yes
theme: united
toc: yes
toc_float: no
---
```{r eval=FALSE}
# composite curve via the locfit package
# bootstrap-by-site confidence intervals
# names
queryname <- "v3i"
basename <- "zt21k"
binname <- "bw20"
# paths for input and output .csv files -- modify as appropriate
datapath <- "/Projects/GPWG/GPWGv3/GCDv3Data/v3plus/"
sitelistpath <- "/Projects/GPWG/GPWGv3/GCDv3Data/v3plus/v3plus_sitelists/"
sitelist <- "v3i_nsa_globe"
outpath <- "e:/Projects/GPWG/GPWGv3/data/v3plus/v3plus_curves/"
# presampled/binned files
csvpath <- "/Projects/GPWG/GPWGv3/GCDv3Data/v3plus/v3plus_presamp_csv/"
csvname <- paste("_presamp_influx_",basename,"_",binname,".csv", sep="")
library(locfit)
# locfit (half) window-width parameter
hw <- 25 # bandwidth (smoothing parameter)
# number of bootstrap samples/replications
nreps <- 1000
# target ages for fitted values
targbeg <- -60
targend <- 22000
targstep <- 20
# array sizes
maxrecs <- 2000
maxreps <- 1000
# plot output
plotout <- "screen" # "pdf"
# no changes below here
# site list file
sitelistfile <- paste(sitelistpath, sitelist, ".csv", sep="")
sitelistfile
# curve (output) path and file
curvecsvpath <- paste(datapath,queryname,"_curves/",sep="")
# if output folder does not exist, create it
dir.create(file.path(datapath, paste(queryname,"_curves/",sep="")), showWarnings=FALSE)
curvefile <- paste(sitelist,"_locfit_",basename,"_",binname,"_",as.character(hw),"_",
as.character(nreps), ".csv", sep="")
print(curvecsvpath)
print(curvefile)
# .pdf plot of bootstrap iterations
if (plotout == "pdf") {
pdffile <- paste(sitelist,"_locfit_",basename,"_",binname,"_",as.character(hw),"_",
as.character(nreps),".pdf", sep="")
print(pdffile)
}
# read the list of sites
sites <- read.csv(sitelistfile)
head(sites)
ns <- length(sites[,1]) #length(sites$ID_SITE)
ns
# arrays for data and fitted values
age <- matrix(NA, ncol=ns, nrow=maxrecs)
influx <- matrix(NA, ncol=ns, nrow=maxrecs)
nsamples <- rep(0, maxrecs)
targage <- seq(targbeg,targend,targstep)
targage.df <- data.frame(x=targage)
lowage <- targage - hw; highage <- targage + hw
ntarg <- length(targage)
yfit <- matrix(NA, nrow=length(targage.df$x), ncol=maxreps)
# arrays for sample number and effective window span tracking
ndec <- matrix(0, ncol=ntarg, nrow=ns)
ndec_tot <- rep(0, ntarg)
xspan <- rep(0, ntarg)
ninwin <- matrix(0, ncol=ntarg, nrow=ns)
ninwin_tot <- rep(0, ntarg)
# read and store the presample (binned) files as matrices of ages and influx values
ii <- 0
for (i in 1:ns) {
#i <- 1
sitenum <- sites[i,1] # sites$ID_SITE[i]
print(sitenum)
siteidchar <- as.character(sitenum)
if (sitenum >= 1) siteid <- paste("000", siteidchar, sep="")
if (sitenum >= 10) siteid <- paste("00", siteidchar, sep="")
if (sitenum >= 100) siteid <- paste("0", siteidchar, sep="")
if (sitenum >= 1000) siteid <- paste( siteidchar, sep="")
inputfile <- paste(csvpath, siteid, csvname, sep="")
print(inputfile)
if (file.exists(inputfile)) {
indata <- read.csv(inputfile)
nsamp <- length(indata$age) #
if (nsamp > 0) {
ii <- ii+1
age[1:nsamp,ii] <- indata$age #
influx[1:nsamp,ii] <- indata$zt #
nsamples[ii] <- nsamp
}
}
}
nsites <- ii
# number of sites with data
nsites
# trim samples to age range
influx[age >= targend+hw] <- NA
age[age >= targend+hw] <- NA
# censor abs(influx) values > 10
influx[abs(influx) >= 10] <- NA
age[abs(influx) >= 10] <- NA
# count number of sites that contributed to each fitted value
ptm <- proc.time()
for (i in 1:ntarg) {
agemax <- -1e32; agemin <- 1e32
for (j in 1:nsites) {
for (k in 1:nsamples[j]) {
if (!is.na(age[k,j])) {
ii <- (age[k,j]-targage[1])/targstep + 1
#print (c(i,j,k,ii))
if (ii > 0 && ii <= ntarg) {ndec[j,ii] = 1}
if (age[k,j] >= targage[i]-hw && age[k,j] <= targage[i]+hw) {
ninwin[j,i] = 1
if (agemax < age[k,j]) {agemax <- age[k,j]}
if (agemin > age[k,j]) {agemin <- age[k,j]}
}
}
}
}
ndec_tot[i] <- sum(ndec[,i])
ninwin_tot[i] <- sum(ninwin[,i])
xspan[i] <- agemax - agemin
}
proc.time() - ptm
head(cbind(targage,ndec_tot,xspan,ninwin_tot))
ptm <- proc.time()
# 1. reshape matrices into vectors
x <- as.vector(age)
y <- as.vector(influx)
lfdata <- data.frame(x,y)
lfdata <- na.omit(lfdata)
x <- lfdata$x; y <- lfdata$y
# 2. locfit
# initial fit, unresampled (i.e. all) data
loc01 <- locfit(y ~ lp(x, deg=1, h=hw), maxk=800, family="qrgauss")
summary(loc01)
# 3. get fitted values
pred01 <- predict(loc01, newdata=targage.df, se.fit=TRUE)
loc01_fit <- data.frame(targage.df$x, pred01$fit)
fitname <- paste("locfit_",as.character(hw), sep="")
colnames(loc01_fit) <- c("age", fitname)
head(loc01_fit)
proc.time() - ptm
ptm <- proc.time()
# Bootstrap samples
# Step 1 -- Set up to plot individual replications
if (plotout == "pdf") {pdf(file=paste(curvecsvpath,pdffile,sep=""))}
plot(x, y, xlab="Age (BP 1950)", ylab=fitname, xlim=c(22000,-100), xaxp = c(22000, 0, 22), ylim=c(-1.5,0.5), type="n")
# Step 2 -- Do the bootstrap iterations, and plot each composite curve
set.seed(10) # do this to get the same sequence of random samples for each run
for (i in 1:nreps) {
print(i)
randsitenum <- sample(seq(1:nsites), nsites, replace=TRUE)
# print(head(randsitenum))
x <- as.vector(age[,randsitenum])
y <- as.vector(influx[,randsitenum])
lfdata <- data.frame(x,y)
lfdata <- na.omit(lfdata)
x <- lfdata$x; y <- lfdata$y
locboot <- locfit(y ~ lp(x, deg=1, h=hw), maxk=800, maxit=20, family="qrgauss")
predboot <- predict(locboot, newdata=targage.df, se.fit=TRUE)
yfit[,i] <- predboot$fit
# note plotting lines is slowww
lines(targage.df$x, yfit[,i], lwd=2, col=rgb(0.5,0.5,0.5,0.10))
if (i %% 10 == 0) {print(i)}
}
# Step 3 -- Plot the unresampled (initial) fit
fitname <- paste("locfit_",as.character(hw), sep="")
colnames(loc01_fit) <- c("age", fitname)
lines(loc01_fit[,1], loc01_fit[,2], lwd=2, col="red")
# Step 4 -- Find and add bootstrap CIs
yfit95 <- apply(yfit, 1, function(x) quantile(x,prob=0.975, na.rm=T))
yfit05 <- apply(yfit, 1, function(x) quantile(x,prob=0.025, na.rm=T))
lines(targage.df$x, yfit95, lwd=1, col="red")
lines(targage.df$x, yfit05, lwd=1, col="red")
if (plotout == "pdf") {dev.off()}
curveout <- data.frame(cbind(targage.df$x, pred01$fit, yfit95, yfit05, ndec_tot, xspan, ninwin_tot))
colnames(curveout) <- c("age", "locfit", "cu95", "cl95", "nsites", "window", "ninwin")
outputfile <- paste(curvecsvpath, curvefile, sep="")
write.table(curveout, outputfile, col.names=TRUE, row.names=FALSE, sep=",")
proc.time() - ptm
```