sample2indp.pow: Independent normal data

In stats4teaching: Simulate Pedagogical Statistical Data

Independent normal data

Description

Generates two normal independent samples with desired power and cohen's effect.

Usage

sample2indp.pow(n1, mean = 0, s1= 1, d.cohen, power,
   alternative = c("two.sided", "less", "greater"), delta = 1,
   conf.level = 0.95, dec = 2)

Arguments

n1	first sample size.
mean	vector of sample means.
s1	standard deviation for first sample.
d.cohen	Cohen's effect.
power	power of the test.
alternative	a character string specifying the alternative hypothesis for T-Test. Must be one of “two.sided“ (default), “greater“ or “less“. Can be specified just the initial letter.
delta	true value of the difference in means.
conf.level	confidence level of the interval.
dec	number of decimals for observations.

Details

Pooled standard deviation= sp = sqrt((n1 - 1) sigma1^2 +(n2 - 1) sigma2^2) / (n1 + n2 - 2)

d.cohen = |mean1 - mean2| / sqrt(sp)

Value

A list containing the following components:

• Data: a data frame containing the samples created.

• Size: size of each sample.

• T.test: a t-test of the samples.

Examples

sample2indp.pow(n1 = 30, mean = c(2,3), s1= 0.5, d.cohen = 0.8, power = 0.85, delta = 1)
sample2indp.pow(n1 = 50, mean = c(15.5,16), s1=2 , d.cohen = 0.3, power = 0.33, delta = 0.5)

stats4teaching documentation built on Oct. 4, 2022, 9:06 a.m.