tidyst_kcde: Tidy and geospatial kernel cumulative distribution and copula...

In eks: Tidy and Geospatial Kernel Smoothing

Tidy and geospatial kernel cumulative distribution and copula estimates

Description

Tidy and geospatial versions of kernel cumulative distribution estimates for 1- and 2-dimensional data, and kernel copula estimates for 2-dimensional data.

Usage

tidy_kcde(data, ...)
tidy_kcopula(data, ...)
st_kcde(x, ...)

Arguments

data	data frame/tibble of data values
x	sf object with point geometry
...	other parameters in ks::kcde, ks::kcopula functions

Details

For details of the computation of the kernel distribution and copula estimates, and of the bandwidth selector procedures, see ?ks::kcde, ?ks::kcopula.

Value

The outputs from *_kcde have the same structure as the kernel density estimate from *_kde, except that estimate indicates the cumulative distribution rather than the density values. Likewise for tidy_kcopula.

Examples

library(ggplot2)
data(crabs, package="MASS")
## tidy 1-d distribution estimate per species
crabs1 <- dplyr::select(crabs, FL, sp)
crabs1 <- dplyr::group_by(crabs1, sp)
t1 <- tidy_kcde(crabs1)
gt1 <- ggplot(t1, aes(x=FL)) 
gt1 + geom_line(colour=1) + geom_rug_ks(colour=4) + facet_wrap(~sp)

## tidy 2-d copula estimate
crabs2 <- dplyr::select(crabs, FL, CW)
t2 <- tidy_kcopula(crabs2)
gt2 <- ggplot(t2, aes(x=FL, y=CW)) 
gt2 + geom_contour_filled_ks(colour=1, cont=seq(10,90,by=10))

## geospatial distribution estimate
data(wa)
data(grevilleasf)
paradoxa <- dplyr::filter(grevilleasf, species=="paradoxa")
paradoxa_crop <- sf::st_crop(paradoxa, xmin=4e5, xmax=8e5, ymin=6.4e6, ymax=6.65e6)
paradoxa_bbox <- sf::st_as_sfc(sf::st_bbox(paradoxa_crop))
xminb <- sf::st_bbox(paradoxa_crop)[1:2]
xmaxb <- sf::st_bbox(paradoxa_crop)[3:4]
s1 <- st_kcde(paradoxa_crop, xmin=xminb, xmax=xmaxb)

## base R filled contour plot
xlim <- c(1.2e5, 1.1e6); ylim <- c(6.1e6, 7.2e6)
plot(wa, xlim=xlim, ylim=ylim)
plot(paradoxa_bbox, add=TRUE, lty=3, lwd=2)
plot(s1, add=TRUE)

## geom_sf filled contour plot
gs1 <- ggplot(s1) + geom_sf(data=wa, fill=NA) + ggthemes::theme_map() 
gs1 + geom_sf(data=paradoxa_bbox, linewidth=1.2, linetype="dotted", fill=NA) +
    geom_sf(data=st_get_contour(s1), aes(fill=label_percent(contlabel))) +
    scale_fill_viridis_d() + coord_sf(xlim=xlim, ylim=ylim)

eks documentation built on May 29, 2024, 1:17 a.m.