concurve (version 2.0)

curve_mean: Computes consonance intervals for mean differences

Description

Computes thousands of consonance (confidence) intervals for the chosen parameter in a statistical test that compares means and places the interval limits for each interval level into a data frame along with the corresponding p-values and s-values.

Usage

curve_mean(x, y, data, paired = F, method = "default",
replicates = 1000, steps = 10000)

Arguments

x

Variable that contains the data for the first group being compared.

y

Variable that contains the data for the second group being compared.

data

Data frame from which the variables are being extracted from.

paired

Indicates whether the statistical test is a paired difference test. By default, it is set to "F", which means the function will be an unpaired statistical test comparing two independent groups. Inserting "paired" will change the test to a paired difference test.

method

By default this is turned off (set to "default"), but allows for bootstrapping if "boot" is inserted into the function call.

replicates

Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method.

steps

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.

References

Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

Examples

# NOT RUN {
# Simulate random data

GroupA <- runif(100, min = 0, max = 100)
GroupB <- runif(100, min = 0, max = 100)

RandomData <- data.frame(GroupA, GroupB)

bob <- curve_mean(GroupA, GroupB, RandomData)

tibble::tibble(bob)

# }