plot_interpretation_result_list: Plotting function for collection of interpretation_result...
In jimvine/confinterpret: Descriptive Interpretations of Confidence Intervals

Description Usage Arguments Details Examples

Produces a plot presenting a collection of interpretation_result objects on a single chart. If the interpretation_result objects are named then the names will be used for labelling the relevant intervals on the chart.

plot_interpretation_result_list(x, extra_boundaries = NULL,
  estimates = NULL, boundary_values = TRUE, boundary_label_pos = "below",
  interpretation_label_pos = "right", x_axis_pos = "below",
  y_axis_pos = "none", inner_margin = c(-0.1, 0.05, -0.1, 0.05),
  edge_margin = c(0, 0.02, 0, 0.02), edge_type = "gradient",
  interval_type = "norm", y_scale = 0.75, interval_value_labels = TRUE,
  estimate_value_labels = TRUE, plot_estimate_marks = TRUE, ...)

`x`	A list of `interpretation_result` objects, length at least 2. The objects may optionally be named. See Details.
`extra_boundaries`	Names optional.
`estimates`	Estimate values that the intervals assessed in each `interpretation_result` object relate to. If not specified, a default of the central point between the two ends of each interval will be assumed.
`boundary_values`	A logical value indicating whether the values should be appended to the boundaries' names.
`boundary_label_pos`	Where to put the boundary labels. Options are `c("below", "above", "on top", "none")`. If you are planning to plot values on the canvas and want the boundary labels on top then you may want to choose "none" and make a call to `label_ontop_boundaries()` after plotting values.
`interpretation_label_pos`	Options are `c("right", "left", "none")`
`x_axis_pos`	Location of a numerical x axis. Options are `c("none", "below", "above")`.
`y_axis_pos`	Location of a numerical y axis. Default "none" will almost always be right. Options are c`("none", "left", "right")`.
`inner_margin`	Numerical vector of the form `c(bottom, left, top, right)`, which gives the amount of inner margin to be added, expressed as a proportion of the plotted area. This is space designed to be past any plotted objects but before the edging (defined separately via `edge_margin`). See Details.
`edge_margin`	Numerical vector of the form `c(bottom, left, top, right)`, which gives the amount of 'edge margin' to be added, expressed as a proportion of the plotted width. This is the space designed to be occupied by plot edges (e.g. a gradient fading out). Currently only implemented for left and right; top and bottom values are ignored. See Details.
`edge_type`	What style of edge to draw at the sides of the plot. Currently supported options are `"gradient"` (the default) and `"zigzag"`.
`interval_type`	Set the way the interval is presented. Current options are `c("norm", "unif")` for a normal distribution-based curve and a box, respectively.
`y_scale`	How tall the interval plots are to be drawn
`interval_value_labels`	Logical value specifying whether interval value labels are to be added.
`estimate_value_labels`	Logical value specifying whether estimate value labels are to be added.
`plot_estimate_marks`	Whether to plot marks at the x location of the estimates.
`...`	Further arguments passed to and from methods.

For a single interpretation_result object a plot() method is provided; see plot.interpretation_result.

To be a valid group of interpretation_result objects, each of the items in x must be a valid interpretation_result, and they must all share some characteristics. Each of the component objects must have been generated using the same interpretation_set, with the same boundaries, and the low_to_high parameter must be the same. This enables them to be meaningfully plotted on the same canvas.

# Set up some intervals to test:
ci_stage_1 <- matrix(c(0.023, 0.131), nrow = 1,
                     dimnames = list("estimate", c("2.5 %", "97.5 %")))
ci_stage_2 <- matrix(c(-0.016, 0.096), nrow = 1,
                     dimnames = list("estimate", c("2.5 %", "97.5 %")))
# Conduct the interpretations:
interp_stage_1 <-  interpret_noninferiority(ci_stage_1, actual_null = 0,
                                           ni_margin = 0.05,
                                           groups = c("Business as usual",
                                                      "New approach"))
interp_stage_2 <-  interpret_noninferiority(ci_stage_2, actual_null = 0,
                                           ni_margin = 0.05,
                                           groups = c("Business as usual",
                                                      "New approach"))

# Assemble the list object:
interp_1_and_2 <- list("Stage 1" = interp_stage_1,
                       "Stage 2" = interp_stage_2)
# Set a nice colour scheme
grDevices::palette(c("#FF671F99", "#F2A90099", "#0085CA99"))
plot_interpretation_result_list(interp_1_and_2,
                                boundary_label_pos = "on top")