# Uses bootstrap sampling over a vector of LD LASSO constraint parameters, s2, to compute a vector of cp estimates.

### Description

The vector of cp estimates is used to identify the cp-optimal solution.

### Usage

1 | ```
get.cp(s2low, s2high, s2.vec.length, block.obj, Xa = NA, Y = NA, s1, r2.cut, block.cood, B = 20)
``` |

### Arguments

`s2low` |
The lower limit for the s2 vector. |

`s2high` |
The upper limit for the s2 vector. |

`s2.vec.length` |
The number of exponentially spaced values in the s2 vector. |

`block.obj` |
An object of class gwaa.data from GenABEL. |

`Xa` |
If block.obj is NA then a genotype matrix must be provided. Xa is a matrix of genotype values codes as 0, 1 or 2 for homozygous major, heterozygous, or homozygous minor, respectively. |

`Y` |
If block.obj is NA then a phenotype vector Y must be provided. Y is a vector of diagnoses, where 0 is non-diseased and 1 is diseased. |

`s1` |
The LASSO parameter |

`r2.cut` |
Only SNP pairs with correlation greater than r2.cut are bounded by the LD LASSO constraint. |

`block.cood` |
A vector of length p+1, where p is the number of SNPs. block.cood is an indicator vector that indicates block boundaries at all p+1 SNP bounded intervals. Use find.bounds to create this vector. |

`B` |
Number of bootstrap samples |

### Value

`s2.vec` |
A vector of s2 values |

`cp.vec` |
A vector of cp estimates |

`beta0.mat` |
A matrix of LD LASSO estimates |

`s1` |
The LASSO parameter |

### Author(s)

Samuel G. Younkin

### See Also

ld_lasso_method

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