mapping_excursions
mapping_excursions.Rmd
library(actR)
library(lipdR)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(purrr)
#Get some data from the 4.2 paper
googlesheets4::gs4_deauth()
sites <- googlesheets4::read_sheet("1ReehV_sHCOwMalaFE_5NJyANEPih_nT0tBIQO1IeHak")
#> ✔ Reading from North Atlantic 8.2 datasets.
#> ✔ Range tp.
#download the corresponding data
northAtlanticData <- readLipd(sites$datasetId)
#> Getting version information from lipdverse
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#create a timeseries tibble
nats <- as.lipdTsTibble(northAtlanticData)
#filter down to just the data we want
filtNats <- nats |>
filter(paleoData_TSid %in% sites$paleoData_TSid)
#look for the 8.2 ka event at these sites:
tp <- split(filtNats,seq(nrow(filtNats)))
test <- detectExcursion(tp[[1]],
event.yr = 8200,
event.window = rnorm(50,400,100),
ref.window = rnorm(50,400,100),
exc.type = "negative",
n.ens = 50,
sig.num = rnorm(50,2,.1),
n.consecutive = sample(c(2,3,4),size = 50,replace = TRUE),
null.hypothesis.n = 50,
simulate.time.uncertainty = FALSE,
simulate.paleo.uncertainty = TRUE,
paleo.uncertainty = 0.2,
min.vals = 4,
paleo.ar1 = 0.8)
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events8.2 <- purrr::map(tp,
detectExcursion,
event.yr = 8200,
event.window = rnorm(50,400,100),
ref.window = rnorm(50,400,100),
exc.type = "negative",
n.ens = 50,
sig.num = rnorm(50,2,.1),
n.consecutive = sample(c(2,3,4),size = 50,replace = TRUE),
null.hypothesis.n = 50,
simulate.time.uncertainty = FALSE,
simulate.paleo.uncertainty = TRUE,
paleo.uncertainty = 0.2,
min.vals = 4,
paleo.ar1 = 0.8,
.progress = TRUE) |>
purrr::list_rbind() |>
dplyr::filter(!is.na(pvalue))
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lon.range <- range(events8.2$geo_longitude) + c(-5,5)
lat.range <- range(events8.2$geo_latitude) + c(-5,5)
dist.grid <- distanceGrid(events8.2,lon.range = lon.range,lat.range = lat.range,grid.resolution = 3)
pval.grid.weight.temp <- map(dist.grid,
spatialSigTest,
events=events8.2,
agg.method="robustNull",
use.weights = TRUE,
distance.cutoff = 1000,
.progress = TRUE)
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figoutTemp <- plotSignificance(pval.grid.weight.temp,
sigTestResults = events8.2,
which.test = "pvalNeg",
color.breaks = c(0.01,0.05,0.1,0.2),
restrict.sites = TRUE,
alpha.by.weight = TRUE,
cutoff.distance = 1000,
x.lim = lon.range,
y.lim = lat.range)
figoutTemp$plot