one_mean_test: Simulation-based hypothesis test for a single mean

In greenwood-stat/catstats: Statistics for Montana State University Bobcats

Simulation-based hypothesis test for a single mean

Description

This function will run a simulation-based hypothesis test for a single mean or median of a quantitative variable.

Usage

one_mean_test(
  data,
  null_value = 0,
  summary_measure = "mean",
  shift = 0,
  as_extreme_as,
  direction = c("greater", "less", "two-sided"),
  number_repetitions = 1,
  add_normal = FALSE
)

Arguments

data	Vector of quantitative data values.
summary_measure	Name of summary measure to return from simulations. Allowed values are "mean" or "median". Defaults to "mean".
shift	Amount to shift data values for bootstrapping of null distribution.
as_extreme_as	Value of observed mean.
direction	Direction of alternative hypothesis. Allowed values are "greater", "less", or "two-sided".
number_repetitions	Number of simulated samples.
add_normal	Logical value indicating whether to superimpose a normal curve on the histogram. Defaults to FALSE.

Value

Returns plot of distribution of simulated statistics, with values as or more extreme than specified value highlighted, and reports proportion of simulations as or more extreme than specified as subtitle on plot.

Examples

set.seed(117)
x <- rnorm(25)
m <- mean(x)
one_mean_test(x,
              summary_measure = "mean",
              shift = -m,
              as_extreme_as = m,
              direction = "two-sided",
              number_repetitions = 100
)

greenwood-stat/catstats documentation built on Dec. 14, 2024, 6:25 p.m.