WGR2 (EM) {bWGR} | R Documentation |

Univariate models to find breeding values through regression fitted via expectation-maximization implemented in C++.

```
emRR(y, gen, df = 10, R2 = 0.5)
emBA(y, gen, df = 10, R2 = 0.5)
emBB(y, gen, df = 10, R2 = 0.5, Pi = 0.75)
emBC(y, gen, df = 10, R2 = 0.5, Pi = 0.75)
emBL(y, gen, R2 = 0.5, alpha = 0.02)
emEN(y, gen, R2 = 0.5, alpha = 0.02)
emDE(y, gen, R2 = 0.5)
emML(y, gen, D = NULL)
emCV(y, gen, k = 5, n = 5, Pi = 0.75, alpha = 0.02,
df = 10, R2 = 0.5, avg=TRUE, llo=NULL, tbv=NULL, ReturnGebv = FALSE)
```

`y` |
Numeric vector of response variable ( |

`gen` |
Numeric matrix containing the genotypic data. A matrix with |

`df` |
Hyperprior degrees of freedom of variance components. |

`R2` |
Expected R2, used to calculate the prior shape (de los Campos et al. 2013). |

`Pi` |
Value between 0 and 1. Expected probability pi of having null effect (or 1-Pi if Pi>0.5). |

`alpha` |
Value between 0 and 1. Intensity of L1 variable selection. |

`D` |
NULL or numeric vector with length p. Vector of weights for markers. |

`k` |
Integer. Folding of a k-fold cross-validation. |

`n` |
Integer. Number of cross-validation to perform. |

`avg` |
Logical. Return average across CV, or correlations within CV. |

`llo` |
NULL or a vector (numeric or factor) with the same length as y. If provided, the cross-validations are performed as Leave a Level Out (LLO). This argument allows the user to predefine the splits. This argument overrides |

`tbv` |
NULL or numeric vector of 'true breeding values' ( |

`ReturnGebv` |
Logical. If TRUE, it returns a list with the average marker values and fitted values across all cross-validations, in addition to the regular output. |

The model for the whole-genome regression is as follows:

`y = mu + Xb + e`

where `y`

is the response variable, `mu`

is the intercept, `X`

is the genotypic matrix, `b`

is the effect of an allele substitution (or regression coefficient) and `e`

is the residual term. A k-fold cross-validation for model evaluation is provided by `emCV`

.

The EM functions returns a list with the intercept (`mu`

), the regression coefficient (`b`

), the fitted value (`hat`

), and the estimated intraclass-correlation (`h2`

).

The function emCV returns the predictive ability of each model, that is, the correlation between the predicted and observed values from `k`

-fold cross-validations repeated `n`

times.

Alencar Xavier

```
## Not run:
data(tpod)
emCV(y,gen,3,3)
## End(Not run)
```

[Package *bWGR* version 2.2.5 Index]