simple_htest: One, two, and paired samples hypothesis tests

Description

Performs one or two sample t-tests or Wilcoxon-Mann-Whitney rank-based tests with expanded options compared to t.test, brunner_munzel, or wilcox.test.

Usage

simple_htest(
  x,
  ...,
  paired = FALSE,
  alternative = c("two.sided", "less", "greater", "equivalence", "minimal.effect"),
  mu = NULL,
  alpha = 0.05
)
# S3 method for default
simple_htest(
  x,
  y = NULL,
  test = c("t.test", "wilcox.test", "brunner_munzel"),
  paired = FALSE,
  alternative = c("two.sided", "less", "greater", "equivalence", "minimal.effect"),
  mu = NULL,
  alpha = 0.05,
  ...
)
# S3 method for formula
simple_htest(formula, data, subset, na.action, ...)

Value

A list with class "htest" containing the following components:

statistic: the value of the t-statistic.
parameter: the degrees of freedom for the t-statistic.
p.value: the p-value for the test.
conf.int: a confidence interval for the mean appropriate to the specified alternative hypothesis.
estimate: the estimated mean or difference in means depending on whether it was a one-sample test or a two-sample test.
null.value: the specified hypothesized value of the mean or mean difference. May be 2 values.
stderr: the standard error of the mean (difference), used as denominator in the t-statistic formula.
alternative: a character string describing the alternative hypothesis.
method: a character string indicating what type of t-test was performed.
data.name: a character string giving the name(s) of the data.

Arguments

x: a (non-empty) numeric vector of data values.
...: further arguments to be passed to or from methods.
paired: a logical indicating whether you want a paired t-test.
alternative: a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater", "less", "equivalence" (TOST), or "minimal.effect" (TOST). You can specify just the initial letter.
mu: a number specifying an optional parameter used to form the null hypothesis. See ‘Details’.
alpha: alpha level (default = 0.05)
y: an optional (non-empty) numeric vector of data values.
test: a character string specifying what type of hypothesis test to use. Options are limited to "wilcox.test", "t.test", or "brunner_munzel". You can specify just the initial letter.
formula: a formula of the form lhs ~ rhs where lhs is a numeric variable giving the data values and rhs either 1 for a one-sample or paired test or a factor with two levels giving the corresponding groups. If lhs is of class "Pair" and rhs is 1, a paired test is done.
data: an optional matrix or data frame (or similar: see model.frame) containing the variables in the formula formula. By default the variables are taken from environment(formula).
subset: an optional vector specifying a subset of observations to be used.
na.action: a function which indicates what should happen when the data contain NAs. Defaults to getOption("na.action").

Details

The type of test, t-test/Wilcoxon-Mann-Whitney/Brunner-Munzel, can be selected with the "test" argument.

alternative = "greater" is the alternative that x is larger than y (on average). If alternative = "equivalence" then the alternative is that the difference between x and y is between the two null values mu.. If alternative = "minimal.effect" then the alternative is that the difference between x and y is less than the lowest null value or greater than the highest.

For more details on each possible test (brunner_munzel, stats::t.test, or stats::wilcox.test), please read their individual documentation.

Examples

Run this code

data(mtcars)
simple_htest(mpg ~ am,
data = mtcars,
alternative = "e",
mu = 3)