plots.Block {BDEsize} | R Documentation |

## Diagnosis Graphs for the number of Blocks of Randomized Complete Block Design

### Description

This function produces graphs between the sample size, power and the detectable standardized effect size of randomized complete block design.

### Usage

```
plots.Block(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 number of blocks plot when |

`alpha` |
Type I error. |

`beta` |
Type II error. |

`type` |
graph type: 1 for Power vs Delta plot, 2 for Delta vs the Number of Blocks plot, and 3 for Power vs the Number of Blocks plot. |

`maxsize` |
tolerance for the number of blocks. |

### Details

In a randomized complete block design (without replications), the optimal number of blocks need to be determined.
This function produces graph between Number of Block, power 1-`beta`

and the detectable standardized effect size `delta`

of randomized complete block design.
According to `type`

, it displays plot of Power vs Delta, Delta vs Number of Blocks, or Power vs Number of Blocks.

### Value

plot of Power vs Delta, Delta vs Number of Blocks, or Power vs Number of Blocks according to `type`

.

### See Also

`plots.Full`

, `plots.2levFr`

, `plots.Split`

.

### Examples

```
# plot of Power vs Delta for randomized complete block design
# with 2 factors without the interaction effects
plots.Block(factor.lev=c(2, 2), interaction=FALSE,
delta_type=1, delta=c(1, 0, 1), alpha=0.05, beta=0.2, type=1)
# plot of Power vs Number of Blocks for randomized complete block design
# with 2 factors with the interaction effects
plots.Block(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]