Size.Block {BDEsize} | R Documentation |
This function computes the number of blocks for randomized complete block design to detect a certain standardized effect size with power at the significance level.
Size.Block(factor.lev, interaction = FALSE, delta_type = 1, delta = c(1, 0, 1), alpha = 0.05, beta = 0.2, maxsize = 1000)
factor.lev |
vector of the numbers of levels for each factor. |
interaction |
specifies whether two-way interaction effects are included in a model with the main effects. When |
delta_type |
specifies the type of standardized effect size: 1 for standard deviation type and 2 for range type. |
delta |
vector of effect sizes: |
alpha |
Type I error. |
beta |
Type II error. |
maxsize |
tolerance for the number of blocks. |
In a randomized complete block design (without replications), the optimal number of blocks need to be determined.
This function computes the number of blocks for randomized complete block design to detect a certain standardized effect size delta
with power 1-beta
at the significance level alpha
.
model |
a character vector expressing a model. The main effects are expressed by the upper-case letters
of the Roman alphabet, and two-way interaction effects are denoted by * operator for pairs of the main effects.
The block factor is denoted by |
n |
optimal the number of blocks. |
Delta |
a vector of minimal detectable standardized effect sizes. |
R. V. Lenth (2006-9). Java Applets for Power and Sample Size[Computer software]. Retrieved March 27, 2018 from https://homepage.divms.uiowa.edu/~rlenth/Power/.
Y. B. Lim (1998). Study on the Size of Minimal Standardized Detectable Difference in Balanced Design of Experiments. Journal of the Korean society for Quality Management, 26(4), 239–249.
M. A. Kastenbaum, D. G. Hoel and K. O. Bowman (1970) Sample size requirements : one-way analysis of variance, Biometrika, 57(2), 421–430.
D. C. Montgomery (2013) Design and analysis of experiments. John Wiley & Sons.
Size.Full
, Size.2levFr
, Size.Split
.
# only main effects model1 <- Size.Block(factor.lev=c(2, 2), interaction=FALSE, delta_type=1, delta=c(1, 0, 1), alpha=0.05, beta=0.2) model1$model model1$n model1$Delta # including two-way interaction effects model2 <- Size.Block(factor.lev=c(2, 2), interaction=TRUE, delta_type=1, delta=c(1, 1, 1), alpha=0.05, beta=0.2) model2