tidyst_kdde: Tidy and geospatial kernel density derivative estimates

In eks: Tidy and Geospatial Kernel Smoothing

Tidy and geospatial kernel density derivative estimates

Description

Tidy and geospatial versions of kernel density derivative estimates for 1- and 2-dimensional data.

Usage

tidy_kdde(data, deriv_order=1, ...)
st_kdde(x, deriv_order=1, ...)

Arguments

data	data frame/tibble of data values
deriv_order	derivative order. Default is 1.
x	sf object with point geometry
...	other parameters in ks::kdde function

Details

The output from *_kdde have the same structure as the kernel density estimate from *_kde, except that estimate is the kernel density derivative values at the grid points, and the additional derived grouping variable deriv_group is the index of the partial derivative, e.g. "deriv (1,0)" and "deriv (0,1)" for a first order derivative for 2-d data. The output is a grouped tibble, grouped by the input grouping variable (if it exists) and by deriv_group.

For details of the computation of the kernel density derivative estimate and the bandwidth selector procedure, see ?ks::kdde.

In geom_contour_filled_ks and st_get_contour, then the 100% contour is set to the bounding box of the data, and it is plotted in a transparent (if data is not missing) or neutral colour (if data is missing). It is difficult to avoid displaying the boundary of this bounding box in geom_contour_filled_ks, though for geom_sf, using aes(linetype=contline) avoids displaying the boundary box. If data is not missing, it is transparent. See *_kde for details on the aesthetics based on contperc, contregion, estimate.

Value

–For tidy_kdde, the output is an object of class tidy_ks, which is a tibble with columns:

x	evaluation points in x-axis (name is taken from 1st input variable in data)
y	evaluation points in y-axis (2-d) (name is taken from 2nd input variable in data)
estimate	kernel density derivative estimate values
deriv_order	derivative order (same as input)
deriv_ind	index of partial derivative
ks	first row (within each group) contains the untidy kernel estimate from ks::kde
tks	short object class label derived from the ks object class
label	long object class label
group	grouping variable (if grouped input) (name is taken from grouping variable in data)
deriv_group	additional derived grouping variable on partial derivative indices.

–For st_kdde, the output is an object of class st_ks, which is a list with fields:

tidy_ks	tibble of simplified output (deriv_ind, ks, tks, label, group, deriv_group) from tidy_kdde
grid	sf object of grid of kernel density derivative estimate values, as polygons, with attributes estimate, deriv_ind, group, deriv_group copied from the tidy_ks object
sf	sf object of 5%, 10%, ..., 95% contour regions of the kernel density derivative estimate, as multipolygons, with attributes estimate, contlabel, contperc, contregion, contline derived from the contour level, and deriv_ind, group, deriv_group copied from the tidy_ks object.

Examples

library(ggplot2)
theme_set(theme_bw())
data(crabs, package="MASS")
## 1-d density curvature estimate
crabs1 <- dplyr::select(crabs, FL)
t1 <- tidy_kdde(crabs1, deriv_order=2)
gt1 <- ggplot(t1, aes(x=FL))
gt1 + geom_line(colour=1) 

## 2-d density gradient estimate
crabs2 <- dplyr::select(crabs, FL, CW)
t2 <- tidy_kdde(crabs2, deriv_order=1)
tb1 <- contour_breaks(t2, group=FALSE)
tb2 <- contour_breaks(t2, group=TRUE)
gt2 <- ggplot(t2, aes(x=FL, y=CW)) 

## overall set of contour breaks suitable for both derivatives
## filled contour regions with density derivative labels w/o breaks
## displays bounding box by default
gt2 + geom_contour_filled_ks(data=t2, aes(fill=after_stat(contregion)), 
    colour=1, breaks=tb1) + facet_wrap(~deriv_group)
## contour lines with density derivative labels with breaks
gt2 + geom_contour_ks(data=t2, aes(group=deriv_group, 
    colour=after_stat(estimate)), breaks=tb1) + facet_wrap(~deriv_group)

## second partial derivative f^(0,1) only
t22 <- dplyr::filter(t2, deriv_ind==2)
tb22 <- dplyr::filter(tb2, deriv_ind==2)
## filled contour regions with density derivative labels with breaks
## aes(linetype=after_stat(contline)) suppresses displaying bounding box 
gt2 + geom_contour_filled_ks(data=t22, aes(fill=after_stat(contregion),
    linetype=after_stat(contline)), colour=1, breaks=tb22)
## contour lines with density derivative labels with breaks
gt2 + geom_contour_ks(data=t22, aes(colour=after_stat(estimate)), 
     breaks=tb22) 

## geospatial density derivative estimate
data(wa)
data(grevilleasf)
hakeoides <- dplyr::filter(grevilleasf, species=="hakeoides")
s1 <- st_kdde(hakeoides, deriv_order=1)
## overall set of contour breaks suitable for both derivatives
sb1 <- contour_breaks(s1, group=FALSE)
s1_cont1 <- st_get_contour(s1, breaks=sb1)
## set of contour breaks per derivative
sb2 <- contour_breaks(s1, group=TRUE)
sb2 <- dplyr::filter(sb2, deriv_ind==2)
s1_cont2 <- st_get_contour(s1)
s1_cont2 <- dplyr::filter(s1_cont2, deriv_ind==2)

## base R plot
## filled contour regions with density derivative labels with breaks
xlim <- c(1.2e5, 1.1e6); ylim <- c(6.1e6, 7.2e6)
plot(wa, xlim=xlim, ylim=ylim)
plot(s1, add=TRUE, which_deriv_ind=2, breaks=sb2)

## geom_sf plot
glim <- coord_sf(xlim=xlim, ylim=ylim)
gs <- ggplot(s1) + geom_sf(data=wa, fill=NA) + ggthemes::theme_map()
## filled contours with density derivative "percentage" labels w/o breaks
## aes(linetype=contline) suppresses displaying bounding box 
gs + geom_sf(data=s1_cont2, aes(fill=contregion, linetype=contline), 
    colour=1) + glim 
   
## facet wrapped geom_sf plot for each partial derivative
## filled contours with density derivative labels with breaks
gs + geom_sf(data=s1_cont1, aes(fill=contregion, linetype=contline)) + 
    glim + facet_wrap(~deriv_group)

eks documentation built on June 8, 2025, 1:29 p.m.