Description Usage Arguments Details Value References See Also

`BayesCpi`

is an implementation of Bayes Cpi to extend Bayes A and B for estimating direct SNP effects in
high dimensional data problems (*p >> N*). `BayesCpi`

treats the prior probability, `pi`

= *P(SNP has zero effect)*, as unknown.
The C Function `cBaysCpi`

is utilized for for speed ..

1 | ```
BayesCpi(ga, numiter = 5000, Pi = .9, y)
``` |

`ga` |
A matrix of genotypes with a number of rows identical to the number of genotyped individuals and a number of columns identical to the number of SNPs. Values in the matrix are 0, 1, 2, & 5 for homozygous, heterozygous, other homozygous, & unknown genotypes, respectively. |

`numiter` |
Number of iterations |

`Pi` |
Proportion of SNP loci with 0 effect for Bayes C |

`y` |
Trait phenotypes or conventional breeding values |

This function runs Bayes C and Cpi to estimate direct SNP effects and the
proportion of loci with nonzero effects based on a matrix of genotypes,
`ga`

and a vector of adjusted phenotypes, `y`

, (Habier et al., 2011; BMC Bioinformatics 12:186).
As in other bayesian alphabet, Bayes Cpi is essential in high dimensional data problems with highly
overparameterized models (*p >> N*). It extends Bayes A and B to estimate the proportion of loci
with nonzero effect.

Other data management functions in `gdmp`

can be used to construct the integer matrix of genotypes,
`ga`

, to use as input to `BayesCpi`

.

A list object with a vector of SNP estimates `meanb`

and a vector of genomic values for individuals,
`aHat`

are returned. It is also possible to extract the estimated number of SNP loci in `nLoci`

.

Habier et al. (2011).
Extension of the bayesian alphabet for genomic selection.
*BMC Bioinformatics*, **12**, 186.

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