RRBLUP2 {AlphaSimR} | R Documentation |

Fits an RR-BLUP model for genomic predictions. This implementation is
meant for situations where `RRBLUP`

is too slow. Note that
RRBLUP2 is only faster in certain situations, see details below. Most
users should use `RRBLUP`

.

```
RRBLUP2(
pop,
traits = 1,
use = "pheno",
snpChip = 1,
useQtl = FALSE,
maxIter = 10,
Vu = 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. Unlike |

`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. |

`Vu` |
marker effect variance. 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 |

The RRBLUP2 function works best when the number of markers is not
too large. This is because it solves the RR-BLUP problem by setting
up and solving Henderson's mixed model equations. Solving these equations
involves a square matrix with dimensions equal to the number of fixed
effects plus the number of random effects (markers). Whereas the `RRBLUP`

function solves the RR-BLUP problem using the EMMA approach. This approach involves
a square matrix with dimensions equal to the number of phenotypic records. This means
that the RRBLUP2 function uses less memory than RRBLUP when the number of markers
is approximately equal to or smaller than the number of phenotypic records.

The RRBLUP2 function is not recommend for cases where the variance components are
unknown. This is uses the EM algorithm to solve for unknown variance components,
which is generally considerably slower than the EMMA approach of `RRBLUP`

.
The number of iterations for the EM algorithm is set by maxIter. The default value
is typically too small for convergence. When the algorithm fails to converge a
warning is displayed, but results are given for the last iteration. These results may
be "good enough". However we make no claim to this effect, because we can not generalize
to all possible use cases.

The RRBLUP2 function can quickly solve the mixed model equations without estimating variance
components. The variance components are set by defining Vu and Ve. Estimation of components
is suppressed by setting useEM to false. This may be useful if the model is being retrained
multiple times during the simulation. You could run `RRBLUP`

function the first
time the model is trained, and then use the variance components from this output for all
future runs with the RRBLUP2 functions. Again, we can make no claim to the general robustness
of this approach.

```
#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 = RRBLUP2(pop, simParam=SP)
pop = setEBV(pop, ans, simParam=SP)
#Evaluate accuracy
cor(gv(pop), ebv(pop))
```

[Package *AlphaSimR* version 1.3.2 Index]