calcEffectSizeConfidenceIntervals {reproducer}

calcEffectSizeConfidenceIntervals

Description

This function provides single-sided and two-sided confidence interval of an effect size (assuming that the null hypothesis value is zero).

Usage

calcEffectSizeConfidenceIntervals(
  effectsize,
  effectsize.variance,
  effectsize.df = 0,
  alpha = 0.05,
  alternative = "two.sided",
  UpperValue = Inf,
  LowerValue = -Inf
)

Arguments

effectsize

The effect size

effectsize.variance

The effect size variance

effectsize.df

The degrees of freedom for confidence intervals based on the t- distribution. If df=0 (default), the confidence interval is based on the normal distribution

alpha

The significance level of the confidence interval (default 0.05).

alternative

This defines whether a one-sided test or a two-sided (default) test is required. For a one-sided test use parameter values greater' or 'less' to define whether the d-value should be greater or less than zero.

UpperValue

The maximum legal value of the effect size (default Inf). Used to ensure that confidence intervals of effect sizes such as correlation coefficients are restricted to sensible values.

LowerValue

The minimum legal value of the effect size (default -Inf). Used to ensure that confidence intervals of effect sizes such as correlation coefficients are restricted to sensible values

Value

The value of the test statistic, the p.value of test statistic, the upper and lower confidence interval of the effect size, a logical value specifying whether the effect size is significantly different from zero based on the confidence interval and the lower and upper confidence interval bounds.

Author(s)

Barbara Kitchenham and Lech Madeyski

Examples

reproducer:::calcEffectSizeConfidenceIntervals(
  effectsize=0.37,effectsize.variance=0.00847,effectsize.df=11.1,
  alpha=0.05,alternative='two.sided',UpperValue=0.5,LowerValue=-0.5)
# A tibble: 1 x 5
#  ES.test ES.pvalue ES.sig ES.ci.lower ES.ci.upper
#    <dbl>     <dbl> <lgl>        <dbl>       <dbl>
#1    4.02   0.00198 TRUE         0.168         0.5