Plots covariate coefficients and their confidence intervals.
# S3 method for gllvm
coefplot(
object,
y.label = TRUE,
which.Xcoef = NULL,
order = TRUE,
cex.ylab = 0.5,
cex.xlab = 1.3,
mfrow = NULL,
mar = c(4, 6, 2, 1),
xlim.list = NULL,
...
)
an object of class 'gllvm'.
logical, if TRUE
(default) colnames of y with respect to coefficients are added to plot.
vector indicating which covariate coefficients will be plotted. Can be vector of covariate names or numbers. Default is NULL
when all covariate coefficients are plotted.
logical, whether or not coefficients are ordered, defaults to TRUE
.
the magnification to be used for axis annotation relative to the current setting of cex.
the magnification to be used for axis annotation.
same as mfrow
in par
. If NULL
(default) it is determined automatically.
vector of length 4, which defines the margin sizes: c(bottom, left, top, right)
. Defaults to c(4,5,2,1)
.
list of vectors with length of two to define the intervals for an x axis in each covariate plot. Defaults to NULL when the interval is defined by the range of point estimates and confidence intervals
additional graphical arguments.
# Extract subset of the microbial data to be used as an example
data(microbialdata)
X <- microbialdata$Xenv
y <- microbialdata$Y[, order(colMeans(microbialdata$Y > 0),
decreasing = TRUE)[21:40]]
fit <- gllvm(y, X, formula = ~ pH + Phosp, family = poisson())
coefplot(fit)
if (FALSE) {
## Load a dataset from the mvabund package
data(antTraits)
y <- as.matrix(antTraits$abund)
X <- as.matrix(antTraits$env)
# Fit model with environmental covariates
fit <- gllvm(y, X, formula = ~ Bare.ground + Shrub.cover,
family = poisson())
coefplot.gllvm(fit)
# Fit model with all environmental covariates
fitx <- gllvm(y, X, family = "negative.binomial")
coefplot(fitx, mfrow = c(3,2))
coefplot(fitx, which.Xcoef = 1:2)
# Fit gllvm model with environmental and trait covariates
TR <- antTraits$traits
fitT <- gllvm(y = y, X = X, TR = TR, family = "negative.binomial")
coefplot(fitT)
# Fit gllvm model with environmental covariances and reduced rank
fitRR <- gllvm(y = y, X = X, num.RR = 2, family = "negative.binomial")
coefplot(fitRR)
}