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 `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` for main effects, `delta` for two-way interaction effects, and `delta` for standard deviation of noise. When `interaction=FALSE`, `delta` is 0. `deltao` the minimal detectable standardized effect size for power vs the number of blocks 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 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`.

`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)
```

[Package BDEsize version 1.6 Index]