Bayesian version of the CDI plot

The CDI plot presents the coefficients for the variable of interest (top-left panel), the spread of the data (bottom-left panel), and the influence statistic (bottom-right panel).

Usage

plot_bayesian_cdi(
  fit,
  xfocus = "area",
  yfocus = "fishing_year",
  xlab = NULL,
  ylab = NULL,
  hurdle = FALSE,
  sort_coefs = FALSE,
  axis.text.x.bl = TRUE,
  colour = "purple",
  p_margin = 0.05,
  legend = TRUE,
  sum_by = "row",
  ...
)

Arguments

fit

An object of class brmsfit.

xfocus

The column name of the variable to be plotted on the x axis. This column name must match one of the column names in the data.frame that was passed to brm as the data argument.

yfocus

The column name of the variable to be plotted on the y axis. This column name must match one of the column names in the data.frame that was passed to brm as the data argument. This is generally the temporal variable in a generalised linear model (e.g. year).

xlab

The x axis label.

ylab

The y axis label.

hurdle

If a hurdle model then use the hurdle.

sort_coefs

Should the coefficients be sorted from highest to lowest.

axis.text.x.bl

Include the x axis labels on the bottom-left (bl) bubble plot panel.

colour

The colour to use in the plot.

p_margin

The margin between panels on the plot. This is passed to margin within theme.

legend

To show the legend or not.

sum_by

Sum to 1 by row, sum to 1 by column, sum to 1 across all data, or raw. The size of the bubbles will be the same for all and raw, but the legend will change from numbers of records to a proportion.

...

Further arguments passed to nothing.

Value

a ggplot object.

See also

get_coefs, get_influ, plot_bubble

Author

Darcy Webber darcy@quantifish.co.nz