plot_interpretation_result_list {confinterpret}

Plotting function for collection of interpretation_result objects

Description

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.

Usage

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, ...)

Arguments

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.

Details

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.

Examples

# 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")