coefplot.gllvm: Plot covariate coefficients and confidence intervals

In gllvm: Generalized Linear Latent Variable Models

Plot covariate coefficients and confidence intervals

Description

Plots covariate coefficients and their confidence intervals.

Usage

## S3 method for class '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,
  ...
)

Arguments

object	an object of class 'gllvm'.
y.label	logical, if TRUE (default) colnames of y with respect to coefficients are added to plot.
which.Xcoef	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.
order	logical, whether or not coefficients are ordered, defaults to TRUE.
cex.ylab	the magnification to be used for axis annotation relative to the current setting of cex.
cex.xlab	the magnification to be used for axis annotation.
mfrow	same as mfrow in par. If NULL (default) it is determined automatically.
mar	vector of length 4, which defines the margin sizes: c(bottom, left, top, right). Defaults to c(4,5,2,1).
xlim.list	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.

Author(s)

Jenni Niku <[e-mail]>, Francis K.C. Hui, Sara Taskinen, Bert van der Veen

Examples

# Extract subset of the microbial data to be used as an example
data(microbialdata)
X <- microbialdata$Xenv
y <- microbialdata$Y[, order(colMeans(microbialdata$Y > 0), 
                     decreasing = TRUE)[21:40]]
fit <- gllvm(y, X, formula = ~ pH + Phosp, family = poisson())
coefplot(fit)
## Not run: 
## Load a dataset from the mvabund package
data(antTraits)
y <- as.matrix(antTraits$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)

## End(Not run)

gllvm documentation built on Sept. 18, 2023, 5:22 p.m.