epsilon.full.SS: Epsilon for ANOVA from F and Sum of Squares

In doomlab/MOTE: Effect Size and Confidence Interval Calculator

Epsilon for ANOVA from F and Sum of Squares

Description

This function displays epsilon squared from ANOVA analyses and its non-central confidence interval based on the F distribution. This formula works for one way and multi way designs with careful focus on the sum of squares total calculation.

Usage

epsilon.full.SS(dfm, dfe, msm, mse, sst, a = 0.05)

Arguments

dfm	degrees of freedom for the model/IV/between
dfe	degrees of freedom for the error/residual/within
msm	mean square for the model/IV/between
mse	mean square for the error/residual/within
sst	sum of squares total
a	significance level

Details

To calculate epsilon, first, the mean square for the error is substracted from the mean square for the model. The difference is multiplied by the degrees of freedom for the model. The product is divided by the sum of squares total.

epsilon^2 = (dfm * (msm - mse)) / (sst)

Learn more on our example page.

Value

Provides the effect size (epsilon) with associated confidence intervals from the F-statistic.

epsilon	effect size
epsilonlow	lower level confidence interval of epsilon
epsilonhigh	upper level confidence interval of epsilon
dfm	degrees of freedom for the model/IV/between
dfe	degrees of freedom for the error/residual/within
F	F-statistic
p	p-value
estimate	the epsilon statistic and confidence interval in APA style for markdown printing
statistic	the F-statistic in APA style for markdown printing

Examples

#The following example is derived from the "bn1_data" dataset, included
#in the MOTE library.

#A health psychologist recorded the number of close inter-personal
#attachments of 45-year-olds who were in excellent, fair, or poor
#health. People in the Excellent Health group had 4, 3, 2, and 3
#close attachments; people in the Fair Health group had 3, 5,
#and 8 close attachments; and people in the Poor Health group
#had 3, 1, 0, and 2 close attachments.

anova_model = lm(formula = friends ~ group, data = bn1_data)
summary.aov(anova_model)

epsilon.full.SS(dfm = 2, dfe = 8, msm = 12.621,
                mse = 2.458, sst = (25.24+19.67), a = .05)

doomlab/MOTE documentation built on April 17, 2022, 2:08 a.m.