MOTE (version 1.0.2)

epsilon.full.SS: 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

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

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.

Examples

# NOT RUN {
#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)
# }