# PSO Search

### Description

PSO based method for computing main effects of selected SNPs and interaction effects of selected SNP-combinations within the maximum order.

### Usage

1 2 |

### Arguments

`pts` |
matrix. SNP data. Each row represents a sample. Each column represents a SNP. For the element, 1 -> AA, 2 -> Aa, 3 -> aa. |

`class` |
matrix. Class labels of samples. It only has one row. Each column represents a class label. For the element, 1 -> case, 2-> control. |

`MaxOrder` |
numeric. The considered maximum order of epistatic interactions. It must be setted as 1, 2, 3, 4, or 5. |

`Population` |
numeric. The number of particles. For example, Population=1000. |

`Iteration` |
numeric. The number of iterations. For example, Iteration=100. |

`c1` |
numeric. The acceleration factor of individual experience. For example, c1=2. |

`c2` |
numeric. The acceleration factor of global experience. For example, c2=2. |

`TopSNP` |
numeric. The selected SNPs with top indexes. For example, TopSNP=10. |

`measure` |
numeric. The used evaluation measure. 1 -> the classic co-information measure; 2 -> the normalized co-information measure; 3 -> TingHu's co-informationn measure. |

`alpha` |
numeric. The lower threshold of effects, either main effects or interaction effects, which must be higher or equal to 0, By default, alpha <- 0. |

### Value

`SingleEffect` |
matrix. main Effects of SNPs. There are 2 columns. The first column saves all SNPs, and the second column saves their corresponding effects. Ddescending save according to their effects. |

`TwoEffect` |
matrix. interaction Effects of two-SNP combinations. There are three columns. The first two columns save all two-SNP combinations, and the last column saves their corresponding effects. Descending save according to their interaction effects. |

`ThreeEffect` |
matrix. interaction Effects of three-SNP combinations. There are four columns. The first three columns save all three-SNP combinations, and the last column saves their corresponding effects. Descending save according to their interaction effects. |

`FourEffect` |
matrix. interaction Effects of four-SNP combinations. There are five columns. The first four columns save all four-SNP combinations, and the last column saves their corresponding effects. Descending save according to their interaction effects. |

`FiveEffect` |
matrix. interaction Effects of five-SNP combinations. There are six columns. The first five columns save all five-SNP combinations, and the last column saves their corresponding effects. Descending save according to their interaction effects. |

### Author(s)

Junliang Shang shangjunliang110@163.com

### References

None

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
data(pts)
data(class)
MaxOrder <- 2
Pop <- 10
Iter <- 10
c1 <- 2
c2 <- 2
TopSNP <- 10
measure <- 1
alpha <- 0
Effect <- PSOSearch(pts, class, MaxOrder, Pop, Iter, c1, c2, TopSNP, measure, alpha)
SingleEffect <- Effect$SingleEffect
TwoEffect <- Effect$TwoEffect
ThreeEffect <- Effect$ThreeEffect
FourEffect <- Effect$FourEffect
FiveEffect <- Effect$FiveEffect
``` |