Package 'ggord'

Ordination plots with ggplot2

Description

Create an ordination biplot using ggplot2 including options for selecting axes, group color aesthetics, and selection of variables to plot.

Usage

ggord(...)

## Default S3 method:
ggord(
  obs,
  vecs,
  axes = c("1", "2"),
  grp_in = NULL,
  cols = NULL,
  facet = FALSE,
  nfac = NULL,
  addpts = NULL,
  obslab = FALSE,
  ptslab = FALSE,
  ellipse = TRUE,
  ellipse_pro = 0.95,
  poly = TRUE,
  polylntyp = "solid",
  hull = FALSE,
  arrow = 0.4,
  labcol = "black",
  veccol = "black",
  vectyp = "solid",
  veclsz = 0.5,
  ext = 1.2,
  repel = FALSE,
  vec_ext = 1,
  vec_lab = NULL,
  size = 4,
  sizelab = NULL,
  addsize = size/2,
  addcol = "blue",
  addpch = 19,
  txt = 4,
  alpha = 1,
  alpha_el = 0.4,
  xlims = NULL,
  ylims = NULL,
  var_sub = NULL,
  coord_fix = TRUE,
  parse = TRUE,
  grp_title = "Groups",
  force = 1,
  max.overlaps = 10,
  exp = c(0, 0),
  ...
)

## S3 method for class 'PCA'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'MCA'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'mca'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'acm'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'prcomp'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'princomp'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'metaMDS'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'lda'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'pca'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'coa'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'ca'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'ppca'
ggord(ord_in, grp_in = NULL, axes = NULL, ...)

## S3 method for class 'rda'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'capscale'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'dbrda'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'cca'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

## S3 method for class 'dpcoa'
ggord(ord_in, grp_in = NULL, axes = c("1", "2"), ...)

Arguments

...	arguments passed to or from other methods
obs	matrix or data frame of axis scores for each observation
vecs	matrix or data frame of axis scores for each variable
axes	chr string indicating which axes to plot
grp_in	vector of grouping objects for the biplot, must have the same number of observations as the original matrix used for the ordination
cols	chr string of optional colors for grp_in
facet	logical indicating if plot is faceted by groups in grp_in
nfac	numeric indicating number of columns if facet = TRUE
addpts	optional matrix or data.frame of additional points if constrained ordination is used (e.g., species locations in cca, rda)
obslab	logical if the row names for the observations in obs are plotted rather than points
ptslab	logical if the row names for the additional points (addpts) in constrained ordination are plotted as text
ellipse	logical if confidence ellipses are shown for each group, method from the ggbiplot package, at least one group must have more than two observations
ellipse_pro	numeric indicating confidence value for the ellipses
poly	logical if confidence ellipses are filled polygons, otherwise they are shown as empty ellipses
polylntyp	chr string for line type of polygon outlines if poly = FALSE, options are twodash, solid, longdash, dotted, dotdash, dashed, blank, or alternatively the grouping vector from grp_in can be used
hull	logical if convex hull is drawn around points or groups if provided
arrow	numeric indicating length of the arrow heads on the vectors, use NULL to suppress arrows
labcol	chr string for color of text labels on vectors
veccol	chr string for color of vectors
vectyp	chr string for line type of vectors, options are twodash, solid, longdash, dotted, dotdash, dashed, blank
veclsz	numeric for line size on vectors
ext	numeric indicating scalar distance of the labels from the arrow ends
repel	logical if overlapping text labels on vectors use geom_text_repel from the ggrepel package
vec_ext	numeric indicating a scalar extension for the ordination vectors
vec_lab	list of optional labels for vectors, defaults to names from input data. The input list must be named using the existing variables in the input data. Each element of the list will have the desired name change.
size	numeric indicating size of the observation points or a numeric vector equal in length to the rows in the input data
sizelab	chr string indicating an alternative legend title for size
addsize	numeric indicating size of the species points if addpts is not NULL
addcol	numeric indicating color of the species points if addpts is not NULL
addpch	numeric indicating point type of the species points if addpts is not NULL
txt	numeric indicating size of the text labels for the vectors, use NULL to suppress labels
alpha	numeric transparency of points and ellipses from 0 to 1
alpha_el	numeric transparency for confidence ellipses, also applies to filled convex hulls
xlims	two numeric values indicating x-axis limits
ylims	two numeric values indicating y-axis limits
var_sub	chr string indcating which labels to show. Regular expression matching is used.
coord_fix	logical indicating fixed, equal scaling for axes
parse	logical indicating if text labels are parsed
grp_title	chr string for legend title
force	numeric passed to force argument in geom_text_repel from the ggrepel package
max.overlaps	numeric passed to max.overlaps argument in geom_text_repel from the ggrepel package
exp	numeric of length two for expanding x and y axes, passed to scale_y_continuous and scale_y_continuous
ord_in	input ordination object

Details

Explained variance of axes for triplots are constrained values.

Value

A ggplot object that can be further modified

See Also

ggplot

Examples

library(ggplot2)

# principal components analysis with the iris data set
# prcomp
ord <- prcomp(iris[, 1:4])

p <- ggord(ord, iris$Species)
p

p <- ggord(ord, iris$Species, cols = c('purple', 'orange', 'blue'))
p

p + scale_shape_manual('Groups', values = c(1, 2, 3))
p + theme_classic()
p + theme(legend.position = 'top')

# change the vector labels with vec_lab
new_lab <- list(Sepal.Length = 'SL', Sepal.Width = 'SW', Petal.Width = 'PW',
 Petal.Length = 'PL')
p <- ggord(ord, iris$Species, vec_lab = new_lab)
p

# faceted by group
p <- ggord(ord, iris$Species, facet = TRUE, nfac = 3)
p

# principal components analysis with the iris dataset
# princomp
ord <- princomp(iris[, 1:4])

ggord(ord, iris$Species)

# principal components analysis with the iris dataset
# PCA
library(FactoMineR)

ord <- PCA(iris[, 1:4], graph = FALSE)

ggord(ord, iris$Species)

# principal components analysis with the iris dataset
# dudi.pca
library(ade4)

ord <- dudi.pca(iris[, 1:4], scannf = FALSE, nf = 4)

ggord(ord, iris$Species)

# multiple correspondence analysis with the tea dataset
# MCA
data(tea, package = 'FactoMineR')
tea <- tea[, c('Tea', 'sugar', 'price', 'age_Q', 'sex')]

ord <- MCA(tea[, -1], graph = FALSE)

ggord(ord, tea$Tea, parse = FALSE) # use parse = FALSE for labels with non alphanumeric characters

# multiple correspondence analysis with the tea dataset
# mca
library(MASS)

ord <- mca(tea[, -1])

ggord(ord, tea$Tea, parse = FALSE) # use parse = FALSE for labels with non alphanumeric characters

# multiple correspondence analysis with the tea dataset
# acm
ord <- dudi.acm(tea[, -1], scannf = FALSE)

ggord(ord, tea$Tea, parse = FALSE) # use parse = FALSE for labels with non alphanumeric characters

# nonmetric multidimensional scaling with the iris dataset
# metaMDS
library(vegan)
ord <- metaMDS(iris[, 1:4])

ggord(ord, iris$Species)

# linear discriminant analysis
# example from lda in MASS package
ord <- lda(Species ~ ., iris, prior = rep(1, 3)/3)

ggord(ord, iris$Species)

# correspondence analysis
# dudi.coa
ord <- dudi.coa(iris[, 1:4], scannf = FALSE, nf = 4)

ggord(ord, iris$Species)

# correspondence analysis
library(ca)
ord <- ca(iris[, 1:4])

ggord(ord, iris$Species)

# double principle coordinate analysis (DPCoA)
library(ade4)
data(ecomor)
grp <- rep(c("Bu", "Ca", "Ch", "Pr"), each = 4)    # sample groups
dtaxo <- dist.taxo(ecomor$taxo)                    # taxonomic distance between species
ord <- dpcoa(data.frame(t(ecomor$habitat)), dtaxo, scan = FALSE, nf = 2)

ggord(ord, grp_in = grp, ellipse = FALSE, arrow = 0.2, txt = 3)

# phylogenetic PCA
# ppca

library(adephylo)
library(phylobase)
library(ape)

data(lizards)

# example from help file, adephylo::ppca
# original example from JOMBART ET AL 2010

# build a tree and phylo4d object
liz.tre <- read.tree(tex=lizards$hprA)
liz.4d <- phylobase::phylo4d(liz.tre, lizards$traits)

# remove duplicated populations
liz.4d <- phylobase::prune(liz.4d, c(7,14))

# correct labels
lab <- c("Pa", "Ph", "Ll", "Lmca", "Lmcy", "Phha", "Pha",
         "Pb", "Pm", "Ae", "Tt", "Ts", "Lviv", "La", "Ls", "Lvir")
tipLabels(liz.4d) <- lab

# remove size effect
dat <- tdata(liz.4d, type="tip")
dat <- log(dat)
newdat <- data.frame(lapply(dat, function(v) residuals(lm(v~dat$mean.L))))
rownames(newdat) <- rownames(dat)
tdata(liz.4d, type="tip") <- newdat[,-1] # replace data in the phylo4d object

# create ppca
liz.ppca <- ppca(liz.4d,scale=FALSE,scannf=FALSE,nfposi=1,nfnega=1, method="Abouheif")

# plot
ggord(liz.ppca)

# distance-based redundancy analysis
# dbrda from vegan
data(varespec)
data(varechem)

ord <- dbrda(varespec ~ N + P + K + Condition(Al), varechem, dist = "bray")

ggord(ord)

######
# triplots

# redundancy analysis
# rda from vegan
ord <- rda(varespec, varechem)

ggord(ord)

# distance-based redundancy analysis
# capscale from vegan
ord <- capscale(varespec ~ N + P + K + Condition(Al), varechem, dist = "bray")

ggord(ord)

# canonical correspondence analysis
# cca from vegan
ord <- cca(varespec, varechem)

ggord(ord)

# species points as text
# suppress site points
ggord(ord, ptslab = TRUE, size = NA, addsize = 5, parse = TRUE)

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