| quasiF.fnc {languageR} | R Documentation |
The textbook Quasi-F test for a split-plot design with subjects, items, and a single factorial predictor. Included for educational purposes for this specific design only.
quasiF.fnc(ms1, ms2, ms3, ms4, df1, df2, df3, df4)
ms1 |
Mean squares Factor |
ms2 |
Mean squares Item:Subject |
ms3 |
Mean squares Factor:Subject |
ms4 |
Mean squares Item |
df1 |
Degrees of freedom Factor |
df2 |
Degrees of freedom Item:Subject |
df3 |
Degrees of freedom Factor:Subject |
df4 |
Degrees of freedom Item |
A list with components
F |
Quasi-F value. |
df1 |
degrees of freedom numerator. |
df2 |
degrees of freedom denominator. |
p |
p-value. |
R. H. Baayen
~~objects to See Also as quasiF.split.plot.fnc.
data(splitplot)
splitplot.lm = lm(RT ~ SOA + Item + Subject +
SOA:Subject + Item:Subject, data = splitplot)
splitplot.aov = anova(splitplot.lm)
quasiF.fnc(splitplot.aov["SOA","Mean Sq"],
splitplot.aov["Item:Subject", "Mean Sq"],
splitplot.aov["SOA:Subject", "Mean Sq"],
splitplot.aov["Item", "Mean Sq"],
splitplot.aov["SOA","Df"],
splitplot.aov["Item:Subject", "Df"],
splitplot.aov["SOA:Subject", "Df"],
splitplot.aov["Item", "Df"])
# much simpler is
quasiF.split.plot.fnc(splitplot)$quasiF