Introducing the vector-based statistics.
Now we can extract the centroid data and plot the vector data.
# extract the centroids from the fitted model object
centroids <- siberCentroids(ellipses.posterior)
# calculate pairwise polar vectors among all groups
# this is not actually used in this example
angles_distances <- allCentroidVectors(centroids, do.plot = FALSE)The posterior distributions of the angles can then be visualise as a polar histograms, since the data wrap between \(-\pi\) and \(\pi\). For some reason, I cant get the gridline associate with \(\pi\) to appear.
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
# function to do the histograms on each group
my.hist <- function(df){
test <- hist(df$angles,
breaks = seq(from = -pi, to = pi, length = 60),
plot = FALSE)
X <- data.frame(counts = test$counts, mids = test$mids, dens = test$density,
counts.stdzd = test$counts / max(test$counts))
return(X)
}
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
# calculate the points for each group's ellipse
hist.by.groups <- angles_distances %>% group_by(comparison) %>%
do(my.hist(.))
all.roses <- ggplot(data = hist.by.groups, aes(x = mids, y = counts.stdzd)) +
geom_bar(stat = "identity") +
coord_polar(start = pi / 2, direction = -1) +
facet_wrap( ~ comparison) +
theme(axis.ticks.y = element_blank(),
axis.text.y = element_blank()) +
scale_x_continuous( breaks = c(-pi, -pi/2, 0, pi/2, pi),
labels = c("","-\u03C0/2","0","\u03C0/2", "\u03C0"))
print(all.roses)
