plots.Split {BDEsize} R Documentation

## 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.

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)


[Package BDEsize version 1.6 Index]