plot.interpretation_result: Plot an interpretation_result, as returned by confinterpret()
In jimvine/confinterpret: Descriptive Interpretations of Confidence Intervals

Description Usage Arguments Details Examples

Produces a diagram that illustrates the confidence interval that was interpreted using confinterpret against a background illustrating the interpretation_set that it was the basis for the interpretation.

## S3 method for class 'interpretation_result'
plot(x, extra_boundaries = NULL,
  estimate = 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", interval_value_labels = TRUE,
  estimate_value_labels = TRUE, plot_estimate_marks = TRUE,
  estimate_mark_points = c(0, 0.05, 0, -0.05), ...)

`x`	An `interpretation_result` object, of the type returned by `confinterpret`.
`extra_boundaries`	A vector of numerical values specifying the position for displaying additional boundaries, not specified in the `interpretation_set`. May optionally be named values; if named, the names will be labelled on the plot axis.
`estimate`	Estimate value that the interval relates to. If not specified, a default of the central point between the two ends of the 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.
`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.
`estimate_mark_points`	y positions of the ends of the estimate marks as a numeric vector of length 4. Values are, in order: start (relative to centre), end (relative to box top), start (relative to centre), end (relative to box bottom).
`...`	Further arguments passed to and from methods.

Additional boundaries can be displayed using the extra_boundaries parameter. This can be helpful if you want to show a position that is of some practical relevance, but is not defined as a boundary for the purposes of the interpretation_set.

If you wish to plot multiple interpretation_result objects on one chart, see plot_interpretation_result_list.

Plots use the current R Graphics Palette, so you may wish to set that to something attractive before plotting. See ?palette.

# Set a nice colour scheme
grDevices::palette(c("#FF671F99", "#F2A90099", "#0085CA99"))
# Set up a confidence interval to interpret
ci_test <- matrix(c(-0.03, 0.05),
                  nrow = 1,
                  dimnames = list("estimate", c("2.5 %", "97.5 %")))
noninf <- interpret_noninferiority(ci_test, 0, 0.05, c("Treatment as usual",
                                                       "New treatment"))
plot(noninf)