concurve (version 2.0)

plot_concurve: Plots the P- (Consonance) and S-Value (Surprisal) Functions using base R graphics.

Description

Takes the dataframe produced by the interval functions and plots the p-values, s-values, consonance (confidence) levels, and the interval estimates to produce p- and s-value functions using base R graphics.

Usage

plot_concurve(type, data, title, xlab, ylab1, ylab2)

Arguments

type

Choose whether to plot a "consonance" function or a "surprisal" function. The default option is set to "consonance". The type must be set in quotes, for example plot_concurve(type = "surprisal") or plot_concurve(type = "consonance").

data

Dataframe where the results from a curve_ function is stored.

title

The title for the graph. By default, it is set to "Consonance Function". In order to set a title, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, title = "Custom Title").

xlab

The label for the x-axis. By default, it is set to "Theta.". In order to set a label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, xlab = "Custom Caption").

ylab1

A label for the y-axis on the left side of the graph. By default, it is set to "P-value." In order to set a custom y-axis label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, ylab1= "Custom y-axis title").

ylab2

A label for the y-axis on the right side of the graph. By default, it is set to "Confidence Level."" In order to set a custom y-axis label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, ylab2= "Custom y-axis title").

Value

Plot with intervals at every consonance level graphed with their corresponding p- and s-values.

References

Amrhein V, Trafimow D, Greenland S. Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis If We Don<U+2019>t Expect Replication. Am Stat; 2018.

Greenland S. Valid P-values behave exactly as they should: Some misleading criticisms of P-values and their resolution with S-values. Am Stat. 2018;18(136).

Greenland S. The unconditional information in P-values, and its refutational interpretation via S-values. 2018.

Shannon CE. A Mathematical Theory of Communication. Bell System Technical Journal. 1948;27(3):379-423. doi:10.1002/j.1538-7305.1948.tb01338.x

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 <- rnorm(50)
GroupB <- rnorm(50)

RandomData <- data.frame(GroupA, GroupB)
RandomModel <- lm(GroupA ~ GroupB, data = RandomData)

intervalsdf <- curve_gen(RandomModel, "GroupB")

s <- plot_concurve(type = "consonance", data = intervalsdf)
s
# }