z.test: One- and Two-Sample z-Test

In tadaatoolbox: Helpers for Data Analysis and Presentation Focused on Undergrad Psychology

Description

Since the "standard" z-test is not available in R as in most real-world scenarios you're only ever going to use a t-test, this function fills that gap for teaching purposes. The function is basically a carbon-copy of stats::t.test, but with user-supplied variances for x and y and p-value and related calculations use a standard normal distribution.

Usage

z.test(
  x,
  y = NULL,
  alternative = c("two.sided", "less", "greater"),
  mu = 0,
  sigma_x,
  sigma_y = NULL,
  paired = FALSE,
  conf.level = 0.95
)

Arguments

x	A (non-empty) numeric vector of data values
y	An optional (non-empty) numeric vector of data values. If omitted, a one-sample test is conducted.
alternative	A character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.
mu	A number indicating the true value of the mean (or difference in means if you are performing a two sample test).
sigma_x, sigma_y	The assumed known variance of x and y. Must be numeric.
paired	A logical indicating whether you want a paired t-test.
conf.level	Confidence level of the interval.

Value

An object of class htest, see stats::t.test

Source

stats::t.test

Examples

x <- rnorm(10, 5, 1)
y <- 1:10 + rnorm(10, 3, 1.5)

# Two sample
z.test(x, y, sigma_x = 1, sigma_y = 1.5)

# One sample
z.test(x, sigma_x = 1, mu = 5)

tadaatoolbox documentation built on July 2, 2020, 2:30 a.m.