ibdBootstrap | R Documentation |

This function produces (parametric or nonparametric) bootstrap estimates of
the IBD coefficients between two individuals. Both kappa and delta
coefficients are supported (see `ibdEstimate()`

).

```
ibdBootstrap(
x = NULL,
ids = NULL,
param = NULL,
kappa = NULL,
delta = NULL,
N,
method = "parametric",
freqList = NULL,
plot = TRUE,
seed = NULL
)
```

`x` |
A |

`ids` |
A pair of ID labels. |

`param` |
Either NULL (default), "kappa" or "delta". (See below.) |

`kappa, delta` |
Probability vectors of length 3 (kappa) or 9 (delta).
Exactly one of |

`N` |
The number of simulations. |

`method` |
Either "parametric" (default) or "nonparametric". Abbreviations are allowed. see Details for more information about each method. |

`freqList` |
A list of probability vectors: The allele frequencies for each marker. |

`plot` |
A logical, only relevant for bootstraps of kappa. If TRUE, the bootstrap estimates are plotted in the IBD triangle. |

`seed` |
An integer seed for the random number generator (optional). |

The parameter `method`

controls how bootstrap estimates are obtained in each
replication.

If `method = "parametric"`

, new profiles for two individuals are simulated
from the input coefficients, followed by a re-estimation of the coefficients.

If `method = "nonparametric"`

, the original markers are sampled with
replacement, before the coefficients are re-estimated.

A data frame with `N`

rows containing the bootstrap estimates. The
last column (`dist`

) gives the euclidean distance to the original
coefficients, viewed as a point in R^3 (kappa) or R^9 (delta).

`ibdEstimate()`

```
# Frequency list of 15 standard STR markers
freqList = NorwegianFrequencies[1:15]
# Number of bootstrap simulations (increase!)
N = 5
# Bootstrap estimates for kappa of full siblings
boot1 = ibdBootstrap(kappa = c(0.25, .5, .25), N = N, freqList = freqList)
boot1
# Mean deviation
mean(boot1$dist)
# Same, but with the 9 identity coefficients.
delta = c(0, 0, 0, 0, 0, 0, .25, .5, .25)
boot2 = ibdBootstrap(delta = delta, N = N, freqList = freqList)
# Mean deviation
mean(boot2$dist)
#### Non-parametric bootstrap.
# Requires `x` and `ids` to be provided
x = nuclearPed(2)
x = markerSim(x, ids = 3:4, N = 50, alleles = 1:10, seed = 123)
bootNP = ibdBootstrap(x, ids = 3:4, param = "kappa", method = "non", N = N)
# Parametric bootstrap can also be done with this syntax
bootP = ibdBootstrap(x, ids = 3:4, param = "kappa", method = "par", N = N)
```

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