(This article was first published on spatialRecology - r, and kindly contributed to R-bloggers)
Overview
Introduction
Lately, I was plotting, comparing and trying to make a sense out of a lot of spatial statistic functions.
To facilitate the procedure I wrote the following function to create a plot for the spatstat class ‘fv’, combined with ‘Quantum Plots’ from:
Esser, D. S., Leveau, J. H. J., Meyer, K. M., & Wiegand, K. (2014). Spatial scales of interactions among bacteria and between bacteria and the leaf surface. FEMS Microbiology Ecology, 91(3), fiu034. http://doi.org/10.1093/femsec/fiu034
The colored bands at the bottom of the plot highlight the spatial scales at which the summary statistics deviate from the simulation envelopes.
Function
quantumPlot <- function(x,colour=c("#d73027", "#ffffbf", "#91bfdb")){
# load Packages
require(ggplot2)
require(ggthemes)
# convert fv to dataframe
env.data <- as.data.frame(tree.data)
env.data <- env.data[-1,]
# plot it
gg_quantomPlot <- ggplot(env.data, aes(r, obs))+
# plot observed value
geom_line(colour=c("#4d4d4d"))+
# plot simulation envelopes
geom_ribbon(aes(ymin=lo,ymax=hi),alpha=0.1, colour=c("#e0e0e0")) +
# axes names and limits
ylim(min(env.data$obs)-1, max(env.data$obs)+2) +
xlab("Distance r (m)") +
ylab("summary statistic") +
# plot expected value, according to null model
geom_hline(yintercept=1, linetype = "dashed", colour=c("#999999")) +
# plot 'Quantums'
geom_rug(data=env.data[env.data$obs > env.data$hi,], sides="b", colour=colour[1]) +
geom_rug(data=env.data[env.data$obs < env.data$lo,], sides="b", colour=colour[2]) +
geom_rug(data=env.data[env.data$obs >= env.data$lo & env.data$obs <= env.data$hi,], sides="b", color=colour[3]) +
# make it look beautiful
theme_tufte()
return(gg_quantomPlot)
}https://gist.github.com/marcosci/e261a586f82d8575a843
Now you can give the result your envelope-fv object and plot it:
attach(redwood)
redwood.pcf <- envelope(redwood, fun=pcf)
quantumPlot(redwood.pcf)
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