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 |

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

`delta` |
vector of effect sizes: |

`deltao` |
the minimal detectable standardized effect size for power vs the sample size plot when |

`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)
```

*BDEsize*version 1.6 Index]