XTRA 1 {bWGR} | R Documentation |

Function to solve univariate mixed models with or without the usage of omic information. This function allows single-step modeling of replicated observations with marker information available through the usage of a linkage function to connect to a whole-genome regression method. Genomic estimated values can be optionally deregressed (no shrinkage) while fitting the model.

```
mixed(y,random=NULL,fixed=NULL,data=NULL,X=list(),
alg=emML,maxit=10,Deregress=FALSE,...)
```

`y` |
Response variable from the data frame containg the dataset. |

`random` |
Formula. Right-hand side formula of random effects. |

`fixed` |
Formula. Right-hand side formula of fixed effects. |

`data` |
Data frame containing the response variable, random and fixed terms. |

`X` |
List of omic incidence matrix. Row names of these matrices connect the omic information to the levels of the indicated random terms (eg. |

`alg` |
Function. Whole-genome regression algorithm utilized to solve link functions. These include MCMC ( |

`maxit` |
Integer. Maximum number of iterations. |

`Deregress` |
Logical. Deregress (unshrink) coefficients while fitting the model? |

`...` |
Additional arguments to be passed to the whole-genome regression algorithms especified on |

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

`y = Xb + Zu + Wa + e`

where `y`

is the response variable, `Xb`

corresponds to the fixed effect term, `Zu`

corresponds to one or more random effect terms, `W`

is the incidence matrix of terms with omic information and `a`

is omic values by `a=Mg`

, where `M`

is the genotypic matrix and `g`

are marker effects. Here, `e`

is the residual term. An example is provided using the data from the NAM package with: `demo(mixedmodel)`

.

Alterinative (and updated) implementations have similar syntax:

01) ```
mm(y,random=NULL,fixed=NULL,data=NULL,
M=NULL,bin=FALSE,AM=NULL,it=10,verb=TRUE,
FLM=TRUE,wgtM=TRUE,cntM=TRUE,nPc=3)
```

02) ```
mtmixed = function(resp, random=NULL, fixed=NULL,
data, X=list(), maxit=10, init=10, regVC=FALSE)
```

The function wgr returns a list with Fitness values (`Fitness`

) containing observation `obs`

, fitted values `hat`

, residuals `res`

, and fitted values by model term `fits`

; Estimated variance components (`VarComp`

) containing the variance components per se (`VarComponents`

) and variance explained by each model term (`VarExplained`

), regression coefficients by model term (`Coefficients`

), and the effects of structured terms (`Structure`

) containing the marker effects of each model term where markers were provided.

Alencar Xavier

Xavier, A. (2019). Efficient Estimation of Marker Effects in Plant Breeding. G3: Genes, Genomes, Genetics, DOI: 10.1534/g3.119.400728

```
## Not run:
demo(mixedmodel)
## End(Not run)
```

[Package *bWGR* version 2.2.5 Index]