Description Usage Arguments Details Examples

View source: R/pop_predict_quick.R

Calculates the expected mean, genetic variance, and superior progeny mean of a bi-parental population based on genomewide marker effects.

1 2 3 | ```
pop_predict_quick(G.in, y.in, map.in, crossing.table, parents, tail.p = 0.1,
model = c("RRBLUP"), map.function = c("haldane", "kosambi", "cf",
"morgan"))
``` |

`y.in` |
A |

`map.in` |
A |

`crossing.table` |
A |

`parents` |
If |

`tail.p` |
The proportion of the distribution of genotypic values from which to define the superior progeny mean. Defaults to 0.1. |

`model` |
The statistical model from which to predict marker effects. |

`map.function` |
The map function to use when converting genetic map distance to recombination rate. |

`pheno` |
A |

`geno` |
A |

This functions executes the same predictions as the function `pop.predict`

, but uses
the expectation of the genetic variance in a biparental population, as opposed to simulating biparental
populations. The approach using the expectation is about 250 times faster than using simulated biparental population
on the default settings.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
# Load example data
library(dplyr)
data("genos")
data("phenos")
data("map")
# Create a crossing table
crossing.table <- combn(x = row.names(genos), m = 2) %>%
t() %>%
as.data.frame() %>%
structure(names = c("parent1", "parent2")) %>%
sample_n(100)
pp_quick_out <- pop_predict_quick(G.in = genos, y.in = phenos, map.in = map,
crossing.table = crossing.table)
``` |

neyhartj/gws documentation built on Nov. 9, 2017, 8:35 p.m.

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