Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
eval = FALSE,
comment = "#>"
)
## ----setup, echo=FALSE, warning=FALSE, message=FALSE--------------------------
# library(kernstadapt)
# library(ggplot2)
# library(stpp)
# library(sparr)
## -----------------------------------------------------------------------------
# # Number of points coming from a Poisson distribution with mean 1000
# N <- rpois(1, 1000)
## -----------------------------------------------------------------------------
# logGaussianPP <- rlgcp(npoints = N, nx = 128, ny = 128, nt = 64,
# separable = F, model = "gneiting", scale = c(0.5, 0.8),
# param = c(1, 1, .1, 0.1, 1, 2), var.grf = 2, mean.grf = 1)
## ---- fig.height = 4.5, fig.align="center"------------------------------------
# # Spatstat format
# XX <- ppp(x = logGaussianPP$xyt[, 1], y = logGaussianPP$xyt[, 2],
# marks = logGaussianPP$xyt[, 3], window = owin())
#
# # Set the color scheme
# colmap <- colourmap(rainbow(512), range = range(marks(XX)))
# sy <- symbolmap(pch = 21, bg = colmap, range = range(marks(XX)))
#
# # Plotting
# plot(XX, symap = sy, 'log-Gaussian Cox point pattern')
## -----------------------------------------------------------------------------
# # Global bandwidths
# bwS0 <- OS(XX) # The spatial bandwidth will be the oversmoothing version
# bwt0 <- bw.SJ(XX$marks) # We employ Sheather & Jones's bandwidth for time
#
# # Spatial and temporal bandwidths based on pilot estimations
# bwS <- bw.abram(unmark(XX), h0 = bwS0)
# bwt <- bw.abram.temp(t = XX$marks, h0 = bwt0)
## -----------------------------------------------------------------------------
# # Fixed bandwidth estimate (non-separable)
# classic.dens <- spattemp.density(pp = unmark(XX), tt = XX$marks,
# sres = 128, tres = 64,
# lambda = bwt0, h = bwS0,
# sedge = "uniform")
## -----------------------------------------------------------------------------
# # In this case we use 8 groups for space and 4 for time
# adapt.dens <- dens.par(X = XX,
# dimt = 64,
# bw.xy = bwS, bw.t = bwt,
# ngroups.xy = 8, ngroups.t = 4,
# at = "bins")
## ---- fig.align="center"------------------------------------------------------
# # We select some fixed times for visualisation
# I <- c(13, 17, 21, 31, 50)
#
# # We subset the lists
# CN <- as.imlist(classic.dens$z[I])
# AN <- as.imlist(adapt.dens[I])
#
# # We generate the plots
# plot.imlist(CN, ncols = 5, main = 'Classic fixed-bandwidth estimate')
# plot.imlist(AN, ncols = 5, main = 'Adaptive non-separable estimate')
## -----------------------------------------------------------------------------
# # Loading dataset
# data("amazon")
## ---- fig.height = 4.5, fig.align="center"------------------------------------
# # Extract a sample of 5000 data points
# AmazonReduced <- amazon[sample.int(amazon$n, 5000)]
#
# # Set the color scheme
# colmap <- colourmap(rainbow(512), range = range(marks(AmazonReduced)))
# sy <- symbolmap(pch = 21, bg = colmap, range = range(marks(AmazonReduced)))
#
# # Plotting
# plot(AmazonReduced, symap = sy, 'Sample of Amazonia fires')
## -----------------------------------------------------------------------------
# separability.test(X = amazon, nperm = 1500)
## -----------------------------------------------------------------------------
# # Global bandwidths
# bwS0 <- OS(amazon) # The spatial bandwidth will be the oversmoothing version
# bwt0 <- bw.nrd(amazon$marks) # We employ Scott bandwidth for time
#
# # Spatial and temporal bandwidths based on pilot estimations
# bwS <- bw.abram(amazon, h0 = bwS0)
# bwt <- bw.abram.temp(t = amazon$marks, h0 = bwt0)
## -----------------------------------------------------------------------------
# # Fixed bandwidth estimate (non-separable)
# # This could be time consuming
# classic.dens <- spattemp.density(pp = unmark(amazon), h = bwS0, tt = amazon$marks,
# sres = 64, tres = 64,
# lambda = bwt0,
# sedge = "uniform")
## -----------------------------------------------------------------------------
# # In this case we let the algorithm to choose the numbers of groups
# # It could be time consuming
# adapt.dens <- dens.par(X = amazon,
# dimyx = 64, dimt = 64,
# bw.xy = bwS, bw.t = bwt,
# at = "bins")
## ---- fig.align="center"------------------------------------------------------
# # We select some fixed times for visualisation
# I <- c(12, 18, 23, 31, 55)
#
# # We subset the lists
# CN <- as.imlist(classic.dens$z[I])
# AN <- as.imlist(adapt.dens[I])
#
# # We generate the plots
# plot.imlist(CN, ncols = 5, main = 'Classic fixed-bandwidth estimate')
# plot.imlist(AN, ncols = 5, main = 'Adaptive non-separable estimate')
## ---- eval = FALSE------------------------------------------------------------
# animation::saveVideo(
# for(i in 1:length(adapt.dens)){
# plot(adapt.dens[[i]], main = paste("Time",i))
# },
# video.name="amazon.mp4", other.opts = '-b:v 1M -pix_fmt yuv420p',
# ani.width = 640, ani.height = 640, interval = 1 / 12)
#
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.