ES.t.one: Calculating effect size (Cohen's d) of one-sample t test

In powerAnalysis: Power Analysis in Experimental Design

Description

Calculating effect size (Cohen's d) of one-sample t test

Usage

ES.t.one(m = NULL, sd = NULL, n = NULL, t = NULL, se = NULL,
  df = NULL, mu = NULL, alternative = c("two.sided", "one.sided"))

Arguments

m	mean of sample
sd	standard deviation of sample
n	number of observations
t	t statistic
se	standard error of sample 1
df	degree of freedom
mu	population mean
alternative	The test is two sided or one sided

See Also

ES.t.two

ES.t.paired

Examples

## mean, sd and mu -> d
ES.t.one(m=-0.0938268,sd=0.9836668,mu=0)

## mean, se, n and mu -> d
ES.t.one(m=-0.0938268,se=0.1391115,n=50,mu=0)

## t and df -> d (df=n-1)
ES.t.one(t = -0.6745,df = 49)

## t and n -> d ((df=n-1))
ES.t.one(t = -0.6745,n = 50)

Example output

     effect size (Cohen's d) of one-sample t test 

              d = 0.09538474
    alternative = two.sided

NOTE: The alternative hypothesis is m != mu
small effect size:  d = 0.2
medium effect size: d = 0.5
large effect size:  d = 0.8


     effect size (Cohen's d) of one-sample t test 

              d = 0.09538473
    alternative = two.sided

NOTE: The alternative hypothesis is m != mu
small effect size:  d = 0.2
medium effect size: d = 0.5
large effect size:  d = 0.8


     effect size (Cohen's d) of one-sample t test 

              d = 0.09635714
    alternative = two.sided

NOTE: The alternative hypothesis is m != mu
small effect size:  d = 0.2
medium effect size: d = 0.5
large effect size:  d = 0.8


     effect size (Cohen's d) of one-sample t test 

              d = 0.09635714
    alternative = two.sided

NOTE: The alternative hypothesis is m != mu
small effect size:  d = 0.2
medium effect size: d = 0.5
large effect size:  d = 0.8

powerAnalysis documentation built on May 2, 2019, 12:40 p.m.