confinterpret: Descriptive interpretations of confidence intervals.
In confinterpret: Descriptive Interpretations of Confidence Intervals

Description Usage Arguments Details Value Examples

Produces descriptive interpretations of confidence intervals, depending on the type of test specified by an interpretation_set.

1 2	confinterpret(ci, interpretation_set, boundaries, comparison_labels = NULL, low_to_high = TRUE)

`ci`	A single row from a matrix of the type returned by `confint()`, containing the confidence interval for the parameter estimate. The two columns provide the lower and upper confidence limits.
`interpretation_set`	List-based object that specifies the boundaries between regions that each of the confidence limits can fall in, and the interpretations to be returned in each of the cases.
`boundaries`	Vector of numbers specifying the values for each of the boundaries defined in the `interpretation_set`. Normally provided in low-to-high order, but see the `low_to_high` parameter for options.
`comparison_labels`	Character vector specifying the labels to be used within the interpretation to describe the comparison. Required if the `interpretation_set` includes a $placeholders entry. Null otherwise.
`low_to_high`	Are the boundaries ordered low-to-high (TRUE) or high-to-low (FALSE)? This can be used to reverse the assessment, including in the cases where only one boundary is supplied. See Details.

Helpful wrapper functions are provided for some commonly used types of test:

Superiority tests: interpret_superiority
Non-inferiority tests: interpret_noninferiority
Equivalence tests: interpret_equivalence

The low_to_high parameter can be set to FALSE to facilitate the situation where the boundaries are ordered high-to-low. This enables the same interpretation_set object to be used for both beneficial and harmful outcomes. For an interpretation_set that has been defined as if higher numbers are better (for example, proportion of participants recovering from a particular illness after treatment) then the inferiority interpretations will be listed first and the superiority ones last. To use this with a negative outcome (for example, proportion of participants catching an illness after a preventative measure), provide the boundaries in high-to-low order and use low_to_high = FALSE. This will also work where a single boundary is specified, and will act to 'reverse' the interpretations.

The use of low_to_high only affects the order of the boundaries (and the regions these implicitly define). It does not affect the ordering of the confidence interval: the numerically lower confidence limit should be listed first either way.

Plotting functions are provided to display the results of confinterpret. To plot a single result see plot.interpretation_result. To plot multiple results on one chart see plot_interpretation_result_list.

A list object of class interpretation_result with elements stating the interpretation in different formats, plus the parameters used to generate the interpretation.

# Establish a test confidence interval
ci_test <- matrix(c(-0.1,0.1),
                  nrow = 1, dimnames = list("estimate",
                                            c("2.5 %","97.5 %")))
confinterpret(ci_test, interpretations_superiority, 0,
  comparison_labels = c(comparison_intervention = "Treatment as usual",
                        tested_intervention = "New treatment"))