Using the confidence limits and point estimates from a dataset, one can use these estimates to compute thousands of consonance intervals and graph the intervals to form a consonance, surprisal, and likelihood functions. The intervals are calculated from the approximated normal distribution, however, users should be cautious as this this function is currently designed for similar situations (involving ratios and normal approximations), nevertheless the function also works for means but should be used skeptically, as it can break down in many situations and give implausible numbers. Computations of likelihood functions for means is currently not supported.

curve_rev(point, LL = NULL, UL = NULL, se = NULL, conf.level = 0.95,
type = "c", measure = "ratio", steps = 10000,
cores = getOption("mc.cores", 1L), table = TRUE)

## Arguments

point The point estimate from an analysis. Ex: 1.20 The lower confidence limit from an analysis Ex: 1.0 The upper confidence limit from an analysis Ex: 1.4 The standard error of the point estimate. Ex: 0.05 Confidence level of the interval estimate. Indicates whether the produced result should be a consonance function or a likelihood function. The default is "c" for consonance and likelihood can be set via "l". The type of data being used. If they involve mean differences, then the "mean" option should be used. If the data are ratios, then the "ratio" option should be used. "ratio" is currently the default option. Currently, this function is designed to be used with ratios and normal approximations rather than means. Indicates how many consonance intervals are to be calculated at various levels. For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended as it will take longer to produce all the intervals and store them into a dataframe. Select the number of cores to use in order to compute the intervals The default is 1 core. Indicates whether or not a table output with some relevant statistics should be generated. The default is TRUE and generates a table which is included in the list object.

## Value

A list with 3 items where the dataframe of values is in the first object, the values needed to calculate the density function in the second, and the table for the values in the third if table = TRUE.

ggcurve()

curve_compare()

plot_compare()

## Examples

if (FALSE) {
# From a real published study. Point estimate of the result was hazard ratio of 1.61 and
# lower bound of the interval is 0.997 while upper bound of the interval is 2.59.
#
df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio")
}