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knitr::opts_chunk$set( collapse = TRUE, comment = "#>", eval = FALSE )
library(devtools) #devtools::install_github('jagm03/kernstadapt', force = T) library(kernstadapt) library(ggplot2)
# Setting a simulation of temporal point pattern with a hotspot # intensity for Nt points Nt <- 2000 y1 <- rnorm(Nt, 10, 0.1) # fixed bandwidth estimate classic.dens <- density.default(y1, from = min(y1), to = max(y1)) classic.dens$y <- classic.dens$y * Nt adapt.dens <- dens.par.temp(y1, at = "bins", dimt = 512) true.dens <- Nt * dnorm(classic.dens$x, 10, 0.1) ##ISE ISE.adapt <- (sum(adapt.dens$y - true.dens) ^ 2) * diff(adapt.dens$x[1:2]) ISE.classic <- (sum(classic.dens$y - true.dens) ^ 2) * diff(adapt.dens$x[1:2]) PD <- data.frame(x = rep(adapt.dens$x, 3), intensity = c(classic.dens$y, adapt.dens$y, true.dens), estimator = factor(rep(1:3, rep(512,3)), levels = 1:3, labels = c("classical", "adaptive", "true"))) ggplot(data = PD, aes(x = x, y = intensity, group = estimator, colour = estimator)) + geom_path() + theme(axis.title.x = element_blank())
# Setting a simulation of a high clustered temporal point pattern # Probability density function fdens.x <- function(x) (dbeta(x %% 4, 2, 2)) # intensity for Nt points Nt <- 2000 x <- runif(Nt, 0, 10) # temporal point pattern y <- sample(x, replace = T, prob = fdens.x(x)) # fixed bandwidth estimate classic.dens <- density.default(y, from = min(y), to = max(y)) classic.dens$y <- classic.dens$y * Nt # Global bandwidth (we give such a refined one because of the high clustering) bw0 <- bw.nrd0(y) / 4 # Abram's bandwidth bw1 <- bw.abram.temp(y, h0 = bw0, trim = 2) # Adaptive intensity adapt.dens <- dens.par.temp(y, bw = bw1, at = "bins", dimt = 512) true.dens <- Nt * fdens.x(adapt.dens$x) ##ISE ISE.adapt <- (sum(adapt.dens$y - true.dens) ^ 2) * diff(adapt.dens$x[1:2]) ISE.classic <- (sum(classic.dens$y - true.dens) ^ 2) * diff(adapt.dens$x[1:2]) PD <- data.frame(x = rep(adapt.dens$x, 3), intensity = c(classic.dens$y, adapt.dens$y, true.dens), estimator = factor(rep(1:3, rep(512,3)), levels = 1:3, labels = c("classical", "adaptive", "true"))) ggplot(data = PD, aes(x = x, y = intensity, group = estimator, colour = estimator)) + geom_line() + theme(axis.title.x = element_blank())
# Load aegiss data-set and plotting data(aegiss) plot(aegiss, bg = rainbow(512), pch = 21, cex = 1, main = "Gastrointestinal desease cases in Hampshire")
# Fixed bandwidth estimate ti <- aegiss$marks Nt <- aegiss$n # Classical estimate classic.dens <- density.default(ti, from = min(ti), to = max(ti)) classic.dens$y <- classic.dens$y * Nt # Adaptive estimate adapt.dens <- dens.par.temp(ti, at = "bins", dimt = 512) aegissD <- data.frame(x = rep(adapt.dens$x, 2), intensity = c(classic.dens$y, adapt.dens$y), estimator = factor(rep(1:2, rep(512,2)), levels = 1:2, labels = c("classical", "adaptive"))) ggplot(data = aegissD, aes(x = x, y = intensity, group = estimator, colour = estimator)) + geom_line() + theme(axis.title.x = element_blank())
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