# Tidy and geospatial kernel feature significance Tidy and geospatial versions of kernel feature significance for 1- and 2-dimensional data. ```r tidy_kfs(data, ...) st_kfs(x, ...) ``` ## Arguments - `data`: data frame/tibble of data values - `x`: sf object with point geometry - `...`: other parameters in `ks::kfs` function ## Returns The output from `tidy_kfs` has the same structure as the kernel density estimate from `tidy_kde`, except that all values of `estimate` outside of the significant curvature regions are set to zero, and the `label` indicates whether the corresponding `x,y` point is inside a significant curvature region. The output from `st_kfs` has a single contour, with `contlabel=50`, as a multipolygon which delimits significant curvature regions. ## Details A significant kernel curvature region consist of all points whose density curvature value is significantly different less than zero (i.e. forms a bump surrounding a local maximum). A Hochberg procedure is employed to control the significance level for multiple significance tests. For details of the computation of the significant kernel curvature regions, see `?ks::kfs`. The bandwidth matrix of smoothing parameters is computed as in `ks::kdde(deriv_order=2)`. ## Examples ```r ## tidy significant curvature regions library(ggplot2) data(hsct, package="ks") hsct <- dplyr::as_tibble(hsct) hsct <- dplyr::filter(hsct, PE.Ly65Mac1>0 & APC.CD45.2>0) hsct12 <- dplyr::filter(hsct, subject==12) hsct12 <- dplyr::select(hsct12, PE.Ly65Mac1, APC.CD45.2) t1 <- tidy_kde(hsct12) t2 <- tidy_kfs(hsct12) gt <- ggplot(t2, aes(x=PE.Ly65Mac1, y=APC.CD45.2)) + geom_contour_ks(data=t1, colour="grey50", cont=seq(10,90,by=10)) gt + geom_contour_filled_ks(aes(fill=after_stat(contregion)), colour=1) gt + geom_contour_ks(aes(colour=after_stat(contregion))) ## geospatial significant curvature regions data(wa) data(grevilleasf) hakeoides <- dplyr::filter(grevilleasf, species=="hakeoides") s1 <- st_kfs(hakeoides) ## base R plot xlim <- c(1.2e5, 1.1e6); ylim <- c(6.1e6, 7.2e6) plot(wa, xlim=xlim, ylim=ylim) plot(s1, add=TRUE) ## geom_sf plot gs <- ggplot(s1) + geom_sf(data=wa, fill=NA) + ggthemes::theme_map() gs + geom_sf(data=st_get_contour(s1), aes(fill=contregion)) + coord_sf(xlim=xlim, ylim=ylim) ```

tidyst_kfs function