sp.plot {agricolae} | R Documentation |

## Splip-Plot analysis

### Description

The variance analysis of a split plot design is divided into two parts: the plot-factor analysis and the sub-plot factor analysis.

### Usage

```
sp.plot(block, pplot, splot, Y)
```

### Arguments

`block` |
replications |

`pplot` |
main-plot Factor |

`splot` |
sub-plot Factor |

`Y` |
Variable, response |

### Details

The split-plot design is specifically suited for a two-factor experiment on of the factors is assigned to main plot (main-plot factor), the second factor, called the subplot factor, is assigned into subplots. The model is mixed, the blocks are random and the study factors are fixed applied according to the design.

### Value

ANOVA: Splip plot analysis

### Author(s)

Felipe de Mendiburu

### References

Statistical procedures for agricultural research. Kwanchai A. Gomez, Arturo A. Gomez. Second Edition. 1984.

### See Also

`ssp.plot`

, `strip.plot`

, `design.split`

,
`design.strip `

### Examples

```
library(agricolae)
data(plots)
model<-with(plots,sp.plot(block,A,B,yield))
# with aov
plots[,1]<-as.factor(plots[,1])
AOV <- aov(yield ~ block + A*B + Error(block/A),data=plots)
summary(AOV)
```

[Package

*agricolae*version 1.3-7 Index]