plots.Split {BDEsize}

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.

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.

Value

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

See Also

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

Examples

# 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)