Description Usage Arguments Details Value Note Author(s) References Examples
This function provides access to several functions returning the optimal number of levels and / or observations in different types of One-Way, Two-Way and Three-Way ANOVA.
1 2 |
model |
A character string describing the model, allowed characters are
Examples: One-Way fixed: |
hypothesis |
Character string describiung Null hypothesis, can be omitted in
most cases if it is clear that a
test for no effects of factor A is performed, Other possibilities: |
assumption |
Character string. A few functions need an assumption on sigma, like
|
a |
Number of levels of fixed factor A |
b |
Number of levels of fixed factor B |
c |
Number of levels of fixed factor C |
n |
Number of Observations |
alpha |
Risk of 1st kind |
beta |
Risk of 2nd kind |
delta |
The minimum difference to be detected |
cases |
Specifies whether the |
see chapter 3 in the referenced book
named integer giving the desired size(s)
Depending on the selected model and hypothesis omit one or two of the
sizes a
, b
, c
, n
. The function then tries
to get its optimal value.
Dieter Rasch, Juergen Pilz, L.R. Verdooren, Albrecht Gebhardt, Minghui Wang
Dieter Rasch, Juergen Pilz, L.R. Verdooren, Albrecht Gebhardt: Optimal Experimental Design with R, Chapman and Hall/CRC, 2011
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | size.anova(model="a",a=4,
alpha=0.05,beta=0.1, delta=2, case="maximin")
size.anova(model="a",a=4,
alpha=0.05,beta=0.1, delta=2, case="minimin")
size.anova(model="axb", hypothesis="a", a=6, b=4,
alpha=0.05,beta=0.1, delta=1, cases="maximin")
size.anova(model="axb", hypothesis="a", a=6, b=4,
alpha=0.05,beta=0.1, delta=1, cases="maximin")
size.anova(model="axb", hypothesis="axb", a=6, b=4,
alpha=0.05,beta=0.1, delta=1, cases="minimin")
size.anova(model="axb", hypothesis="axb", a=6, b=4,
alpha=0.05,beta=0.1, delta=1, cases="minimin")
size.anova(model="axBxC",hypothesis="a",
assumption="sigma_AC=0,b=c",a=6,n=2,
alpha=0.05, beta=0.1, delta=0.5, cases="maximin")
size.anova(model="axBxC",hypothesis="a",
assumption="sigma_AC=0,b=c",a=6,n=2,
alpha=0.05, beta=0.1, delta=0.5, cases="minimin")
size.anova(model="a>B>c", hypothesis="c",a=6, b=2, c=4,
alpha=0.05, beta=0.1, delta=0.5, case="maximin")
size.anova(model="a>B>c", hypothesis="c",a=6, b=20, c=4,
alpha=0.05, beta=0.1, delta=0.5, case="maximin")
size.anova(model="a>B>c", hypothesis="c",a=6, b=NA, c=4,
alpha=0.05, beta=0.1, delta=0.5, case="maximin")
size.anova(model="(axb)>c", hypothesis="a",a=6, b=5, c=4,
alpha=0.05, beta=0.1, delta=0.5, case="maximin")
size.anova(model="(axb)>c", hypothesis="a",a=6, b=5, c=4,
alpha=0.05, beta=0.1, delta=0.5, case="minimin")
size.anova(model="(axb)>c", hypothesis="a",a=6, b=5, c=4,
alpha=0.05, beta=0.1, delta=0.5, case="maximin")
size.anova(model="(axb)>c", hypothesis="a",a=6, b=5, c=4,
alpha=0.05, beta=0.1, delta=0.5, case="minimin")
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