size_b.three_way {OPDOE}

Three-way analysis of variance – nested and mixed classification A\succ B \succ C and (A\times B)\succ C model III, IV and VII

Description

Returns the optimal number of levels for factor B.

Usage

size_b.three_way_mixed_ab_in_c.model_3_a(alpha, beta, delta, a, c, n, cases)

Arguments

alpha

Risk of 1st kind

beta

Risk of 2nd kind

delta

The minimum difference to be detected

a

Number of levels of fixed factor A

c

Number of levels of fixed factor C

n

Number of replications

cases

Specifies whether the "maximin" or "maximin" sizes are to be determined

Details

see chapter 3 in the referenced book

Value

Integer giving the size.

Note

Better use size.anova which allows a cleaner notation.

Author(s)

Dieter Rasch, Juergen Pilz, L.R. Verdooren, Albrecht Gebhardt, Minghui Wang

References

Dieter Rasch, Juergen Pilz, L.R. Verdooren, Albrecht Gebhardt: Optimal Experimental Design with R, Chapman and Hall/CRC, 2011

See Also

size.anova

Examples

size_b.three_way_mixed_ab_in_c.model_3_a(0.05, 0.1, 0.5, 6, 5, 1, "maximin")
size_b.three_way_mixed_ab_in_c.model_3_a(0.05, 0.1, 0.5, 6, 5, 1, "minimin")
size_b.three_way_mixed_cxbina.model_4_a(0.05, 0.1, 0.5, 6, 4, 1, "maximin")
size_b.three_way_mixed_cxbina.model_4_a(0.05, 0.1, 0.5, 6, 4, 1, "minimin")
size_b.three_way_mixed_cxbina.model_4_c(0.05, 0.1, 0.5, 6, 4, 1, "maximin")
size_b.three_way_mixed_cxbina.model_4_c(0.05, 0.1, 0.5, 6, 4, 1, "minimin")
size_b.three_way_mixed_cxbina.model_4_axc(0.05, 0.1, 0.5, 6, 4, 1, "maximin")
size_b.three_way_mixed_cxbina.model_4_axc(0.05, 0.1, 0.5, 6, 4, 1, "minimin")
size_b.three_way_nested.model_6_a(0.05, 0.1, 0.5, 6, 4, 2, "maximin")
size_b.three_way_nested.model_6_a(0.05, 0.1, 0.5, 6, 4, 2, "minimin")