library("mvarVis") library("vegan") library("ggplot2")
data(coldf) sample_data <- data.frame(Color = rownames(coldf), Depth = rowSums(coldf), row.names = rownames(coldf)) X <- list(fmla = formula("coldf ~ ."), data = sample_data) coldf_cca <- ordi(X, rows_annot = sample_data, method = "vegan_cca") # compare with usual cca plot(vegan::cca(coldf ~ ., data = sample_data)) plot_mvar(coldf_cca, "text", col = "Color", size = 6) + scale_colour_manual(name = "Color", values = rownames(coldf)) coldf_cca@table$species <- NULL plot_mvar(coldf_cca, "text", col = "Color", size = 6) + scale_colour_manual(name = "Color", values = rownames(coldf))
data(BCI) dis <- vegdist(BCI, method = "euclidean") bci_isomap <- ordi(dis, method = "isomap", k = 10) plot_mvar(bci_isomap, "text") # or, we could have just passed in the data frame and given a distance bci_isomap_2 <- ordi(BCI, method = "isomap", dist_method = "bray", k = 10) plot_mvar(bci_isomap_2, "text")
data(varespec) data(varechem) cca_fmla <- formula("varespec ~ Al + P * (K + Baresoil)") X <- list(fmla = cca_fmla, data = varechem) vare_cca <- ordi(X, method = "vegan_cca", rows_annot = varespec[, 1:10]) plot_mvar(vare_cca, col = "Callvulg")
data(dune) data(dune.env) X <- list(fmla = formula("dune ~ Manure"), data = dune.env) dune_rda <- ordi(X, method = "rda") plot_mvar(dune_rda, "text")
vare_decorana <- ordi(varespec, method = "decorana") plot_mvar(vare_decorana)
# Taken from the vegan ccora help page data(mite) group.1 <- c(1,2,4:8,10:15,17,19:22,24,26:30) group.2 <- c(3,9,16,18,23,25,31:35) # Separate Hellinger transformations of the two groups of species mite.hel.1 <- decostand(mite[,group.1], "hel") mite.hel.2 <- decostand(mite[,group.2], "hel") rownames(mite.hel.1) = paste("S",1:nrow(mite),sep="") rownames(mite.hel.2) = paste("S",1:nrow(mite),sep="") mite_ccora <- ordi(list(mite.hel.1, mite.hel.2), method = "CCorA", cols_annot = list(t(mite.hel.2), t(mite.hel.1))) plot_mvar(mite_ccora, col = "S68")
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