Description Usage Arguments Details Note Author(s) References Examples
View source: R/ordiplot.gllvm.R
Plots latent variables and their corresponding coefficients (biplot).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  ## S3 method for class 'gllvm'
ordiplot(
object,
biplot = FALSE,
ind.spp = NULL,
alpha = 0.5,
main = NULL,
which.lvs = c(1, 2),
predict.region = FALSE,
level = 0.95,
jitter = FALSE,
jitter.amount = 0.2,
s.colors = 1,
symbols = FALSE,
cex.spp = 0.7,
spp.colors = "blue",
arrow.scale = 0.8,
arrow.ci = TRUE,
spp.arrows = NULL,
cex.env = 0.7,
lab.dist = 0.1,
lwd.ellips = 0.5,
col.ellips = 4,
lty.ellips = 1,
...
)

object 
an object of class 'gllvm'. 
biplot 

ind.spp 
the number of response variables (usually, species) to include on the biplot. The default is none, or all if 
alpha 
a numeric scalar between 0 and 1 that is used to control the relative scaling of the latent variables and their coefficients, when constructing a biplot. 
main 
main title. 
which.lvs 
indices of two latent variables to be plotted if number of the latent variables is more than 2. A vector with length of two. Defaults to 
predict.region 
logical, if 
level 
level for prediction regions. 
jitter 
if 
jitter.amount 
numeric, positive value indicating an amount of jittering for each point, defaults to 0.2 (jitter range). 
s.colors 
colors for sites 
symbols 
logical, if 
cex.spp 
size of species labels in biplot 
spp.colors 
colors for sites, defaults to 
arrow.scale 
positive value, to scale arrows 
arrow.ci 
represent statistical uncertainty for arrows in constrained ordinatioon using confidence interval? Defaults to 
spp.arrows 
plot species scores as arrows if outside of the range of the plot? Defaults to 
cex.env 
size of labels for arrows in constrianed ordination 
lab.dist 
distance between label and arrow heads. Value between 0 and 1 
lwd.ellips 
line width for prediction ellipses. See graphical parameter lwd. 
col.ellips 
colors for prediction ellipses. 
lty.ellips 
line type for prediction ellipses. See graphical parameter lty. 
... 
additional graphical arguments. 
Function constructs a scatter plot of two latent variables, i.e. an ordination plot. Latent variables are rerotated to their principal direction using singular value decomposition, so that the first plotted latent variable does not have to be the first latent variable in the model. If only one latent variable is in the fitted model, latent variables are plotted against their corresponding row indices. The latent variables are labeled using the row index of the response matrix y.
Coefficients related to latent variables are plotted in the same figure with the latent
variables if biplot = TRUE
. They are labelled using the column names of y. The number
of latent variable coefficients to be plotted can be controlled by ind.spp. An argument alpha
is used to control the relative scaling of the latent variables and their coefficients.
If alpha = 0.5
, the latent variables and their coefficients are on the same scale.
For details for constructing a biplot, see Gabriel (1971).
Latent variable scores are always scaled by their estimated standard deviations, for plotting.
For a quadratic response model, species optima are plotted. Any species scores that are outside the range of the predicted site scores are not directly plotted, but their main direction is indicated with arrows instead. This ensures that the plot remains on a reasonable scale.
Effects of environmental variables in constrained ordination are indicated with arrows. If any of the arrows exceeds the range of the plot, arrows are scaled to 80 but so that the relative contribution of predictors is maintained. If standard errors are available in the provided model, the slopes of environmental variables for which the 95 are slightly less intensely coloured.
 If error is occurred when using ordiplot()
, try full name of the function ordiplot.gllvm()
as functions named 'ordiplot' might be found in other packages as well.
Jenni Niku <jenni.m.e.niku@jyu.fi>, Francis K.C. Hui
Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58, 453467.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  #'# Extract subset of the microbial data to be used as an example
data(microbialdata)
y < microbialdata$Y[, order(colMeans(microbialdata$Y > 0),
decreasing = TRUE)[21:40]]
fit < gllvm(y, family = poisson())
fit$logL
ordiplot(fit, predict.region = TRUE)
## Not run:
#'## Load a dataset from the mvabund package
data(antTraits)
y < as.matrix(antTraits$abund)
fit < gllvm(y, family = poisson())
# Ordination plot:
ordiplot(fit)
# Biplot with 10 species
ordiplot(fit, biplot = TRUE, ind.spp = 10)
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.