plots.Full {BDEsize} | R Documentation |

## Diagnosis Graphs for Sample Size of Full Factorial Design

### Description

This function produces graphs between the sample size, power and the detectable standardized effect size of full factorial design.

### Usage

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

### Arguments

`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: |

`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 full factorial design.
According to `type`

, it displays plot of Power vs Delta, Delta vs Sample size, or Power vs Sample size.

### Value

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

.

### See Also

`plots.2levFr`

, `plots.Split`

, `plots.Block`

.

### Examples

```
# plot of Power vs Delta for full factorial design
# with 2 factors without the interaction effects
plots.Full(factor.lev=c(2, 3), interaction=FALSE,
delta_type=1, delta=c(1, 0, 1), alpha=0.05, beta=0.2, type=1)
# plot of Power vs Sample size for full factorial design
# with 2 factors with the interaction effects
plots.Full(factor.lev=c(2, 3), interaction=TRUE,
delta_type=1, delta=c(1, 1, 1), deltao=1.5, alpha=0.05, beta=0.2, type=3)
```

*BDEsize*version 1.6 Index]