compare_p: Compare traditional, max, and second generation P-values...

In LucyMcGowan/sgpvalue: Functions and examples to understand Second Generation P-values

Description

Compare traditional, max, and second generation P-values across studies

Usage

compare_p(data, delta_lb, delta_ub, h0, plot = TRUE)

Arguments

data	Data frame with two variables xbar and se.
delta_lb	Numeric. Lower bound of the indifference zone
delta_ub	Numeric. Upper bound of the indifference zone
h0	Numeric. Value of the null hypothesis
plot	Logical. Whether to produce a plot of the study values with the indifference zone (default = TRUE).

Value

data frame with the following columns:

• xbar: Mean corresponding to the input xbar value

• se: Standard error corresponding to the input se value

• lb: Lower bound of the effect (95

• ub: Upper bound of the effect (95

• p_old: Traditional P-value

• p_max: Maximum P-value over the interval null

• p_new: Second Generation P-value

If plot = TRUE (default), will also output a plot of the studies with the indifference zone.

Examples

## Figure 2 from the second generation P-value paper
data <- data.frame(
  xbar = c(146, 145.5, 145, 146, 144, 143.5, 142, 141),
  se = c(0.5, 0.25, 1.25, 2.25, 1, 0.5, 1, 0.5)
  )
compare_p(data, delta_lb = 144, delta_ub = 148, h0 = 146)

LucyMcGowan/sgpvalue documentation built on May 3, 2019, 4:30 p.m.