fullfact (version 1.0)

ciJack3: Jackknife confidence intervals 3

Description

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

Usage

ciJack3(comp, full, remain = NULL, level = 95, rnd_r = 3, rnd_p = 1, trait = NULL)

Arguments

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, i.e. c(additive, non-additive, maternal, total, component1, component2, ...).

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.

Value

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 random effect variance components. Values are presented as raw and percentages of the total variance value within each row.

Details

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).

References

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.

See Also

ciJack, ciJack2

Examples

data(chinook_jackL) #Chinook salmon offspring length, delete-one jackknife
ciJack3(chinook_jackL,c(0.0000000,0.7192253,0.2029684,1.0404425,0.1077423,0.5499255),
remain=c("tray","Residual"))