gg_varpart: Plot variance components as estimated by calc.varpart
In mbedward/ggboral: View BORAL model results with ggplot

Description Usage Arguments Details Value See Also Examples

View source: R/variance_plots.R

Plots estimates of the proportion of variance in each response accounted for by explanatory variables, latent variables and any row effects, as estimated by the boral function calc.varpart.

1	gg_varpart(model, as.percent = FALSE, label.means = FALSE)

`model`	A boral model fitted with one or more latent variables.
`as.percent`	If `TRUE`, format variance axis tick marks and label as percentages. If `FALSE` (default), format as proportions.
`label.means`	If `TRUE`, append the mean value for each variance component to the legend label. Default is `FALSE`.

Proceed with caution! Read the caveats and warnings detailed in the help page for calc.varpart when interpreting the plot drawn by this function. Also keep in mind that the results can be influenced by data artefacts, e.g. when modelling species occurrence data with a binomial model, the estimate of variance explained by predictor variables will tend to be higher for species with few presences than for more common species.

A ggplot object.

calc.varpart gg_varpart_data

library(boral)
library(ggboral)

data(spider, package = "mvabund")
y <- spider$abun
X <- scale(spider$x)

# Warning - these settings are only to make the example run quickly.
# Don't use them for a real analysis!
example.control <- list(n.burnin = 10, n.iteration = 100, n.thin = 1)

# Note that we specify save.model = TRUE when calling boral
#
spiderfit_nb <- boral(y, X,
                      family = "negative.binomial",
                      lv.control = list(num.lv = 2),
                      row.eff = "fixed",
                      mcmc.control = example.control,
                      save.model = TRUE)

gg_varpart(spiderfit_nb,
           as.percent = TRUE,
           label.means = TRUE) + theme_bw()