DImodelsVis (version 1.0.1)

gradient_change_plot: Visualise change in (predicted) response over diversity gradient

Description

Helper function for plotting the average (predicted) response at each level of a diversity gradient. The output of the gradient_change_data function should be passed here to visualise a scatter-plot of the predicted response (or raw response) over a diversity gradient. The points can be overlaid with `pie-glyphs` to show the relative proportions of the compositional variables. The average change in any user-chosen variable over the chosen diversity gradient can also be shown using the `y_var` parameter.

Usage

gradient_change_plot(
  data,
  prop = NULL,
  pie_data = NULL,
  pie_colours = NULL,
  pie_radius = 0.25,
  points_size = 3,
  average = TRUE,
  y_var = ".Pred",
  facet_var = NULL,
  nrow = 0,
  ncol = 0
)

Value

A ggplot object

Arguments

data

A data-frame which is the output of the `gradient_change_data` function, consisting of the predicted response averaged over a particular diversity gradient.

prop

A vector of column names or indices identifying the columns containing the species proportions in the data. Will be inferred from the data if it is created using the `gradient_change_data` function, but the user also has the flexibility of manually specifying the values.

pie_data

A subset of data-frame specified in `data`, to visualise the individual data-points as pie-glyphs showing the relative proportions of the variables in the data-point.

pie_colours

A character vector specifying the colours for the slices within the pie-glyphs.

pie_radius

A numeric value specifying the radius (in cm) for the pie-glyphs.

points_size

A numeric value specifying the size of points (when pie-glyphs not shown) shown in the plots.

average

A boolean value indicating whether to plot a line indicating the average change in the predicted response with respect to the variable shown on the X-axis. The average is calculated at the median value of any variables not specified.

y_var

A character string indicating the column name of the variable to be shown on the Y-axis. This could be useful for plotting raw data on the Y-axis. By default has a value of ".Pred" referring to the column containing model predictions.

facet_var

A character string or numeric index identifying the column in the data to be used for faceting the plot into multiple panels.

nrow

Number of rows in which to arrange the final plot (when `add_var` is specified).

ncol

Number of columns in which to arrange the final plot (when `add_var` is specified).

Examples

library(DImodels)
library(dplyr)

## Load data
data(sim4)
sim4 <- sim4 %>% filter(treatment %in% c(50, 150))

## Fit model
mod <- glm(response ~ 0 + (p1 + p2 + p3 + p4 + p5 + p6)^2, data = sim4)

## Create data
## By default response would be averaged on the basis of richness
plot_data <- gradient_change_data(data = sim4,
                                  prop = c("p1", "p2", "p3",
                                           "p4", "p5", "p6"),
                                  model = mod)

## Create plot
gradient_change_plot(data = plot_data)

## Average response with respect to evenness
plot_data <- gradient_change_data(data = sim4,
                                  prop = c("p1", "p2", "p3",
                                           "p4", "p5", "p6"),
                                  model = mod,
                                  gradient = "evenness")
gradient_change_plot(data = plot_data)

## Can also manually specify prop variables
## Add grouped proportions to data
plot_data <- group_prop(plot_data,
                       prop = c("p1", "p2", "p3", "p4", "p5", "p6"),
                       FG = c("Gr", "Gr", "Le", "Le", "He", "He"))
## Manually specify prop to show in pie-glyphs
gradient_change_plot(data = plot_data,
                     prop = c("Gr", "Le", "He"))

## Don't show line indicating the average change by using `average = FALSE` and
## Change colours of the pie-slices using `pie_colours`
gradient_change_plot(data = plot_data,
                     average = FALSE,
                     pie_colours = c("darkolivegreen1", "darkolivegreen4",
                                     "orange1", "orange4",
                                     "steelblue1", "steelblue4"))

## Manually specify only specific communities to be shown as pie-chart
## glyphs using `pie_data`.
## Note: It is important for the data specified in
## `pie_data` to have the .Pred and .Gradient columns.
## So the best use case for this parameter is to accept
## a subset of the data specified in `data`.#'
## Also use `facet_var` to facet the plot on an additional variable
gradient_change_plot(data = plot_data,
                     pie_data = plot_data %>% filter(.Richness %in% c(1, 6)),
                     facet_var = "treatment")

## If `add_var` was used during the data preparation step then
## multiple plots will be produced and can be arranged using nrow and ncol
# \donttest{
new_mod <- update(mod, ~. + treatment, data = sim4)
plot_data <- gradient_change_data(data = sim4[c(seq(1, 18, 3), 19:47), -2],
                                  prop = c("p1", "p2", "p3",
                                           "p4", "p5", "p6"),
                                  model = new_mod,
                                  add_var = list("treatment" = c(50, 250)))
## Create plot arranged in 2 columns
gradient_change_plot(data = plot_data,
                     pie_data = plot_data %>% filter(.Richness %in% c(1, 6)),
                     ncol = 2)

## Create plot for raw data instead of predictions
## Create the data for plotting by specifying `prediction = FALSE`
plot_data <- gradient_change_data(data = sim4[sim4$treatment == 50, ],
                                  prop = c("p1", "p2", "p3",
                                           "p4", "p5", "p6"),
                                  prediction = FALSE)
## This data will not have any predictions
head(plot_data)
## Call the plotting function by specifying the variable you we wish to
## plot on the Y-axis by using the argument `y_var`
## Since this data wasn't created using `gradient_change_data`
## `prop` should be manually specified
gradient_change_plot(data = plot_data, y_var = "response",
                     prop = c("p1", "p2", "p3",
                              "p4", "p5", "p6"))
# }