eks (version 1.1.1)

tidyst_kde_boundary: Tidy and geospatial kernel density estimates with boundary and truncated kernels

Description

Tidy and geospatial versions of kernel density estimates with boundary and truncated kernels for 1- and 2-dimensional data.

Usage

tidy_kde_boundary(data, ...)
tidy_kde_truncate(data, boundary, ...)
st_kde_boundary(x, ...)
st_kde_truncate(x, boundary, ...)

Value

The outputs from *_kde_boundary, *_kde_truncate have the same structure as the standard kernel density estimate from *_kde.

Arguments

data

data frame/tibble of data values

x

sf object with point geometry

boundary

data frame/sf point geometry of boundary

...

other parameters in ks::kde.boundary function

Details

A boundary kernel density estimate is a modification of the standard density estimate for bounded data. There are two main types: beta kernels (boundary.kernel="beta") and linear kernels (boundary.kernel="linear"). For details of the computation of the boundary kernel estimates and of the bandwidth selector procedure, see ks::kde.boundary. Currently only rectangular boundaries are supported, as defined by xmin x xmax.

A truncated kernel density estimate is a modification of the standard density estimate for bounded data. All the probability mass outside of boundary is set to zero and re-assigned over the regions inside in the boundary. The boundary can be any polygon. For further details of the computation of the truncated kernel estimate, see ks::kde.truncate.

For details of the computation of the boundary kernel estimates and the truncated kernel density estimates, and of the bandwidth selector procedure, see ks::kde.boundary, ks::kde.truncate.

Examples

## tidy boundary density estimates
library(ggplot2)
data(worldbank, package="ks")
worldbank <- dplyr::as_tibble(worldbank)
## percentage data is bounded on [0,100] x [0,100]
wb2 <- na.omit(worldbank[,c("internet", "ag.value")])
xmin <- c(0,0); xmax <- c(100,100)
rectb <- data.frame(x=c(0,100,100,0,0), y=c(0,0,100,100,0))

## standard density estimate
t1 <- tidy_kde(wb2)
## tidy truncated density estimate
t2 <- tidy_kde_truncate(wb2, boundary=rectb)
tt <- rbind(t1, t2, labels=c("Standard KDE", "Truncated KDE"))
tb <- contour_breaks(tt, group=FALSE)

## standard estimate exceeds boundary but not truncated estimate
gr <- geom_polygon(data=rectb, aes(x=x,y=y), inherit.aes=FALSE, fill=NA, 
    colour=1, linetype="dashed")
gt <- ggplot(tt, aes(x=internet, y=ag.value)) 
gt + geom_contour_filled_ks(colour=1, breaks=tb) + gr + facet_wrap(~group)

## linear boundary density estimate
## beta boundary density estimate
t3 <- tidy_kde_boundary(wb2, boundary.kernel="beta", xmin=xmin, xmax=xmax)
t4 <- tidy_kde_boundary(wb2, boundary.kernel="linear", xmin=xmin, xmax=xmax)
t5 <- rbind(t1, t2, t3, t4, labels=c("Standard KDE", "Truncated KDE", 
    "Beta bd KDE", "Linear bd KDE"))
tb <- contour_breaks(t5, group=FALSE)
## standard estimate exceeds boundary but not boundary or truncated estimates
gt + geom_contour_filled_ks(data=t5, colour=1, breaks=tb) + gr + facet_wrap(~group)

## geospatial boundary density estimates
data(wa)
data(grevilleasf)
hakeoides <- dplyr::filter(grevilleasf, species=="hakeoides")
hakeoides_bbox <- sf::st_bbox(c(xmin=4e5, xmax=5.7e5, ymin=6.47e6, ymax=7e6), 
    crs=sf::st_crs(hakeoides))
xminb <- hakeoides_bbox[1:2]; xmaxb <- hakeoides_bbox[3:4]
hakeoides_bbox <- sf::st_as_sfc(hakeoides_bbox)
hakeoides_crop <- sf::st_filter(hakeoides, hakeoides_bbox)

s1 <- st_kde(hakeoides_crop)
s2 <- st_kde_boundary(hakeoides_crop, boundary.kernel="beta", 
    xmin=xminb, xmax=xmaxb)
s3 <- st_kde_boundary(hakeoides_crop, boundary.kernel="linear", 
    xmin=xminb, xmax=xmaxb)
## geospatial truncated density estimate    
s4 <- st_kde_truncate(x=hakeoides_crop, boundary=hakeoides_bbox)
s5 <- rbind(s1, s2, s3, s4, labels=c("Standard KDE", "Beta bd KDE", 
    "Linear bd KDE", "Truncated KDE"))

## base R plots
xlim <- c(1.2e5, 1.1e6); ylim <- c(6.1e6, 7.2e6)
cols <- colorspace::qualitative_hcl(n=4, palette="Set2")

layout(matrix(c(1,3,2,4), ncol=2))
plot(wa, xlim=xlim, ylim=ylim)
plot(hakeoides_bbox, add=TRUE, lty=3, lwd=2)
plot(s1, add=TRUE, border=cols[1], col=NA, legend=FALSE)

plot(wa, xlim=xlim, ylim=ylim)
plot(hakeoides_bbox, add=TRUE, lty=3, lwd=2)
plot(s2, add=TRUE, border=cols[2], col=NA, legend=FALSE)

plot(wa, xlim=xlim, ylim=ylim)
plot(hakeoides_bbox, add=TRUE, lty=3, lwd=2)
plot(s3, add=TRUE, border=cols[3], col=NA, legend=FALSE)
mapsf::mf_legend(type="symb", val=c("Standard KDE","Beta bd KDE", 
    "Linear bd KDE", "Truncated KDE"), pal=cols, cex=rep(3,4), 
    pch=rep("-",4), title="Density",  pos="bottomleft")

plot(wa, xlim=xlim, ylim=ylim)
plot(hakeoides_bbox, add=TRUE, lty=3, lwd=2)
plot(s4, add=TRUE, border=cols[4], col=NA, legend=FALSE)
layout(1)

## geom_sf plots
gs <- ggplot() + geom_sf(data=wa, fill=NA) + 
    geom_sf(data=hakeoides_bbox, linetype="dotted", fill=NA) + 
    ggthemes::theme_map() +
    colorspace::scale_colour_discrete_qualitative(palette="Set2")
gs + geom_sf(data=st_get_contour(s5), aes(colour=group), fill=NA) + 
    coord_sf(xlim=xlim, ylim=ylim) + 
    guides(colour=guide_legend(title="Density")) + facet_wrap(~group)