Description Usage Arguments Details Value References See Also Examples

Extracts jackknife confidence intervals for additive genetic, non-additive genetic, and maternal variance components. Also extracts intervals for additional fixed and/or random effects.

1 |

`comp` |
Data frame of jackknife resampling results |

`full` |
A vector of raw observed additive, non-additive, maternal, and total variance component values
for from the full observed data set, i.e. c(additive, non-additive, maternal, total). Followed by
any other components in the order of the vector |

`remain` |
Vector of column names for additional effects |

`level` |
Confidence level, as a percentage. Default is 95. |

`rnd_r` |
Number of decimal places to round the confidence interval of raw values. |

`rnd_p` |
Number of decimal places to round the confidence interval of percentage values. |

`trait` |
Optional label for the phenotypic trait. |

Used for jackknife resampling results produced using *JackLmer3* for normal data or
*JackGlmer3* for non-normal data.
Jackknife confidence intervals, using pseudo-values are described by Efron and Tibshirani (1993).
The standard errors are calculated from the pseudo-values and the Student's *t* distribution is used
to provide the lower and upper confidence values. For delete-*d* jackknife resampling,
*M* degrees of freedom are used for producing the confidence interval (Martin et al. 2004):
*M = N / d*, where *N* is the total number of observations and d is the number of
deleted observations. That is, *M* is the number of row in the jackknife resampling results.
Large values of *M*, such as 1,000, can translate to the delete-d jackknife resampling
method approaching bootstrap resampling expectations (Efron & Tibshirani 1993).

Prints a data frame containing the lower, median, and upper values of the jackknife confidence interval for additive genetic, non-additive genetic, maternal variance components, and any additional fixed effect and/or random effect variance components. Values are presented as raw and percentages of the total variance value within each row.

Efron B, Tibshirani R. 1993. An introduction to the Bootstrap. Chapman and Hall, New York.

Martin, H., Westad, F. & Martens, H. (2004). Imporved Jackknife Variance Estimates of Bilinear Model Parameters. COMPSTAT 2004 – Proceedings in Computational Statistics 16th Symposium Held in Prague, Czech Republic, 2004 (ed J. Antoch), pp. 261-275. Physica-Verlag HD, Heidelberg.

1 2 | ```
data(chinook_jackL) #Chinook salmon offspring length, delete-one jackknife
ciJack3(chinook_jackL,remain=c("tray","Residual"),c(0,0.7192,0.2030,1.0404,0.1077,0.5499))
``` |

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