Some test code for checking that the community (and group) labels as strings works.
library(SIBER) library(viridis) palette(viridis(5)) mydata <- read.csv("../inst/extdata/test.community.names.csv", header = TRUE) test <- createSiberObject(mydata) community.hulls.args <- list(col = 1, lty = 1, lwd = 1) group.ellipses.args <- list(n = 100, p.interval = 0.40, lty = 1, lwd = 2) group.hull.args <- list(lty = 2, col = "grey20") par(mfrow=c(1,1)) plotSiberObject(test, ax.pad = 2, hulls = T, 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~'permille'), ylab = expression({delta}^15*N~'permille') ) # Calculate sumamry statistics for each group: TA, SEA and SEAc group.ML <- groupMetricsML(test) print(group.ML) # Calculate the various Layman metrics on each of the communities. community.ML <- communityMetricsML(test) print(community.ML) ## ------------------------------------------------------------------------ # 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(test, parms, priors) ## ------------------------------------------------------------------------ # The posterior estimates of the ellipses for each group can be used to # calculate the SEA.B for each group. SEA.B <- siberEllipses(ellipses.posterior) siberDensityPlot(SEA.B, xticklabels = colnames(group.ML), xlab = c("Community | Group"), ylab = expression("Standard Ellipse Area " ('permille' ^2) ), bty = "L", las = 1, main = "SIBER ellipses on each group", ylims = c(0, 70) ) # Add red x's for the ML estimated SEA-c points(1:ncol(SEA.B), group.ML[3,], col="red", pch = "x", lwd = 2)
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