RRBLUP_SCA2 {AlphaSimR} | R Documentation |

Fits an RR-BLUP model that estimates seperate additive effects for
females and males and a dominance effect. This implementation is meant
for situations where `RRBLUP_SCA`

is too slow. Note that
RRBLUP_SCA2 is only faster in certain situations. Most users should use
`RRBLUP_SCA`

.

```
RRBLUP_SCA2(
pop,
traits = 1,
use = "pheno",
snpChip = 1,
useQtl = FALSE,
maxIter = 10,
VuF = NULL,
VuM = NULL,
VuD = NULL,
Ve = NULL,
useEM = TRUE,
tol = 1e-06,
simParam = NULL,
...
)
```

`pop` |
a |

`traits` |
an integer indicating the trait to model, a trait name, or a function of the traits returning a single value. |

`use` |
train model using phenotypes "pheno", genetic values "gv", estimated breeding values "ebv", breeding values "bv", or randomly "rand" |

`snpChip` |
an integer indicating which SNP chip genotype to use |

`useQtl` |
should QTL genotypes be used instead of a SNP chip. If TRUE, snpChip specifies which trait's QTL to use, and thus these QTL may not match the QTL underlying the phenotype supplied in traits. |

`maxIter` |
maximum number of iterations for convergence. |

`VuF` |
marker effect variance for females. If value is NULL, a reasonable starting point is chosen automatically. |

`VuM` |
marker effect variance for males. If value is NULL, a reasonable starting point is chosen automatically. |

`VuD` |
marker effect variance for dominance. If value is NULL, a reasonable starting point is chosen automatically. |

`Ve` |
error variance. If value is NULL, a reasonable starting point is chosen automatically. |

`useEM` |
use EM to solve variance components. If false, the initial values are considered true. |

`tol` |
tolerance for EM algorithm convergence |

`simParam` |
an object of |

`...` |
additional arguments if using a function for traits |

```
#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=20)
#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)
SP$setVarE(h2=0.5)
SP$addSnpChip(10)
#Create population
pop = newPop(founderPop, simParam=SP)
#Run GS model and set EBV
ans = RRBLUP_SCA2(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)
#Evaluate accuracy
cor(gv(pop), ebv(pop))
```

[Package *AlphaSimR* version 1.3.2 Index]