simplex_path_plot {DImodelsVis}

Visualising the change in a response variable between two points in the simplex space

Description

The helper function for plotting the change in a response variable over a straight line between two points across the simplex space. The output of the simplex_path_data function (with any desired modifications) should be passed here. The generated plot will show individual curves indicating the variation in the response between the points. 'Pie-glyphs' are used to highlight the compositions of the starting, ending and midpoint of the straight line between the two points.

Usage

simplex_path_plot(
  data,
  prop = NULL,
  pie_positions = c(0, 0.5, 1),
  pie_radius = 0.3,
  pie_colours = NULL,
  se = FALSE,
  facet_var = NULL,
  nrow = 0,
  ncol = 0
)

Arguments

data

A data frame created using the simplex_path_data function.

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 'simplex_path_data' function, but the user also has the flexibility of manually specifying the values.

pie_positions

A numeric vector with values between 0 and 1 (both inclusive) indicating the positions along the X-axis at which to show pie-glyphs for each curve. Default is c(0, 0.5, 1) meaning that pie-glyphs with be shown at the start, midpoint and end of each curve.

pie_radius

A numeric value specifying the radius (in cm) for the pie-glyphs. Default is 0.3 cm.

pie_colours

A character vector indicating the colours for the slices in the pie-glyphs.
If left NULL, the colour blind friendly colours will be for the pie-glyph slices.

se

A boolean variable indicating whether to plot confidence intervals associated with the effect of species increase or decrease

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).

Value

A ggmultiplot (ggplot if single plot is returned) class object or data-frame (if 'plot = FALSE')

Examples

library(DImodels)

## Load data
data(sim2)

## Fit model
mod <- glm(response ~ (p1 + p2 + p3 + p4)^2 + 0, data = sim2)

## Visualise change as we move from the centroid community to each monoculture
plot_data <- simplex_path_data(starts = sim2[c(19, 20, 19, 20), ],
                               ends = sim2[c(47, 52, 55, 60), ],
                               prop = c("p1", "p2", "p3", "p4"),
                               model = mod)
## prop will be inferred from data
simplex_path_plot(data = plot_data)

## Show specific curves
simplex_path_plot(data = plot_data[plot_data$.Group %in% c(1, 4), ])

## Show uncertainty using `se = TRUE`
simplex_path_plot(data = plot_data[plot_data$.Group %in% c(1, 4), ],
                  se = TRUE)

## Change colours of pie-glyphs using `pie_colours`
simplex_path_plot(data = plot_data[plot_data$.Group %in% c(1, 4), ],
                  se = TRUE,
                  pie_colours = c("steelblue1", "steelblue4", "orange1", "orange4"))

## Show pie-glyphs at different points along the curve using `pie_positions`
simplex_path_plot(data = plot_data[plot_data$.Group %in% c(1, 4), ],
                  se = TRUE,
                  pie_positions = c(0, 0.25, 0.5, 0.75, 1),
                  pie_colours = c("steelblue1", "steelblue4", "orange1", "orange4"))

## Facet plot based on specific variables
simplex_path_plot(data = plot_data,
                  se = TRUE,
                  facet_var = "block",
                  pie_colours = c("steelblue1", "steelblue4", "orange1", "orange4"))

## Simulataneously create multiple plots for additional variables
sim2$block <- as.numeric(sim2$block)
new_mod <- update(mod, ~ . + block, data = sim2)
plot_data <- simplex_path_data(starts = sim2[c(18), 3:6],
                       ends = sim2[c(48, 60), 3:6],
                       prop = c("p1", "p2", "p3", "p4"),
                       model = new_mod, conf.level = 0.95,
                       add_var = list("block" = c(1, 2)))

simplex_path_plot(data = plot_data,
                  pie_colours = c("steelblue1", "steelblue4",
                                  "orange1", "orange4"),
                  nrow = 1, ncol = 2)