BDEsize (version 1.6)

plots.Split: Diagnosis Graphs for Sample Size of Split-Plot Design

Description

This function produces graphs between the sample size, power and the detectable standardized effect size of split-plot design.

Usage

plots.Split(whole.factor.lev, split.factor.lev, interaction = FALSE, 
    delta_type = 1, delta = c(1, 0, 1, 1), deltao = NULL, alpha = 0.05, beta = 0.2, 
    type = 1, maxsize = 1000)

Arguments

whole.factor.lev

vector of the numbers of levels for each whole factor.

split.factor.lev

vector of the numbers of levels for each split factor.

interaction

specifies whether two-way interaction effects are included in a model with the main effects. When interaction = TRUE, two-way interaction effects are include in a model.

delta_type

specifies the type of standardized effect size: 1 for standard deviation type and 2 for range type.

delta

vector of effect sizes: delta[1] for main effects, delta[2] for two-way interaction effects, and delta[3] and delta[4] for standard deviation of whole-plot noise and subplot noise, respectively. When interaction=FALSE, delta[2] is 0.

deltao

the minimal detectable standardized effect size for power vs the sample size plot when type = 3.

alpha

Type I error.

beta

Type II error.

type

graph type: 1 for Power vs Delta plot, 2 for Delta vs Sample size plot, and 3 for Power vs Sample size plot.

maxsize

tolerance for sample size.

Value

plot of Power vs Delta, Delta vs Sample size, or Power vs Sample size according to type.

Details

This function produces graph between the sample size, power 1-beta and the detectable standardized effect size delta of split-plot design. According to type, it displays plot of Power vs Delta, Delta vs Sample size, or Power vs Sample size. The number of whole-plot factors and split plot factors are up to 2 in the current package version.

See Also

plots.Full, plots.2levFr, plots.Block.

Examples

# NOT RUN {
# plot of Power vs Delta for split-plot design 
# without the interaction effects
plots.Split(whole.factor.lev=2, split.factor.lev=2, interaction=FALSE,
    delta_type=1, delta=c(1, 0, 1, 1), alpha=0.05, beta=0.2, type=1)
  
# plot of Power vs Sample size for split-plot design 
# with the interaction effects
plots.Split(whole.factor.lev=2, split.factor.lev=2, interaction=TRUE,
    delta_type=1, delta=c(1, 1, 1, 1), deltao=1, alpha=0.05, beta=0.2, type=3)
# }