knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.width = 6, fig.height = 5) library(viridis) palette(viridis(3))

We will use the same demo data as per the main introductory vignette. We plot it just to remind us.

# remove previously loaded items from the current environment and remove previous graphics. rm(list=ls()) graphics.off() # Here, I set the seed each time so that the results are comparable. # This is useful as it means that anyone that runs your code, *should* # get the same results as you, although random number generators change # from time to time. set.seed(1) library(SIBER) library(ggplot2) library(magrittr) # to enable piping library(dplyr) # load in the included demonstration dataset data("demo.siber.data") # # create the siber object siber.example <- createSiberObject(demo.siber.data) # Create lists of plotting arguments to be passed onwards to each # of the three plotting functions. community.hulls.args <- list(col = 1, lty = 1, lwd = 1) group.ellipses.args <- list(n = 100, p.interval = 0.95, lty = 1, lwd = 2) group.hull.args <- list(lty = 2, col = "grey20") par(mfrow=c(1,1)) plotSiberObject(siber.example, ax.pad = 2, hulls = F, community.hulls.args, ellipses = T, group.ellipses.args, group.hulls = T, group.hull.args, bty = "L", iso.order = c(1,2), xlab = expression({delta}^13*C~'\u2030'), ylab = expression({delta}^15*N~'\u2030') )

As before, we fit the Bayesian model describing the ellipses.

# options for running jags parms <- list() parms$n.iter <- 2 * 10^4 # number of iterations to run the model for parms$n.burnin <- 1 * 10^3 # discard the first set of values parms$n.thin <- 10 # thin the posterior by this many parms$n.chains <- 2 # run this many chains # define the priors priors <- list() priors$R <- 1 * diag(2) priors$k <- 2 priors$tau.mu <- 1.0E-3 # fit the ellipses which uses an Inverse Wishart prior # on the covariance matrix Sigma, and a vague normal prior on the # means. Fitting is via the JAGS method. ellipses.posterior <- siberMVN(siber.example, parms, priors)

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)

Try some fancy 2-d density plots. These boxes represent the density of the tips of the polar vectors describing the relative position between pairs of individuals. The median vector is shown as an arrow, with the 2-dimensional heatmaps showing where the tip is likely to be. To do this, we have to convert our polar coordinates to cartesian space using the formulae: $(x,y) = (r\cos(\theta),r\sin(\theta))$.

The names for each comparison are not the prettiest or most informative. Here the "X" is superfluous and has appeared via the dplyr code above. The panel label 'X1.1.1.2' reads as community 1 group 1, compared with community 1 group 2. You could relabel the groups in the field `cart_positions$comparison`

below.

median_vectors <- dplyr::summarise(group_by(angles_distances, comparison), medAngle = median(angles), medDist = median(distances)) origins <- data.frame(comparison = median_vectors$comparison, x = 0, y = 0) ends <- with(median_vectors, data.frame(comparison = comparison, x = medDist * cos(medAngle), y = medDist * sin(medAngle))) # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # generate the start and end points of the medians for the arrows for_arrows <- dplyr::bind_rows(origins, ends) # rename the comparison label for nice plot labels below # aa <- unlist(strsplit(as.character(for_arrows$comparison), "[.]")) # aa <- aa[seq(3,length(aa),5)] # for_arrows$comparison2 <- factor(aa) # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # create the cartesian points for the estimated tips of the arrows cart_positions <- with(angles_distances, data.frame(x = distances * cos(angles), y = distances * sin(angles), comparison = comparison )) # rename as above # bb <- unlist(strsplit(as.character(angles_distances$comparison), "[.]")) # bb <- bb[seq(3,length(bb),5)] # cart_positions$comparison2 <- factor(bb) # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # plot it ggplot(cart_positions, aes(x,y) ) + geom_bin2d(bins = 20) + scale_fill_gradient(low = "white", high = "black") + coord_cartesian(xlim = c(-20, +20), ylim = c(-20, +20)) + facet_wrap( ~ comparison, scales = "fixed") + theme_classic() + geom_path(data = for_arrows, arrow = arrow(type = "open", length = unit(0.2, "cm")), col = "red", alpha = 0.6) + ylab(expression(paste(delta^{15}, "N (\u2030)"))) + xlab(expression(paste(delta^{13}, "C (\u2030)"))) + theme(text = element_text(size=15))

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.