fullfact (version 1.5.2)

JackLmer3: Jackknife components for normal data 3

Description

Extracts additive genetic, non-additive genetic, and maternal variance components from a linear mixed-effect model using the lmer function of the lme4 package. Model random effects are dam, sire, dam by sire, and any additional fixed and/or random effects.

Usage

JackLmer3(observ, dam, sire, response, remain, ml = F, size = 1, first = NULL)

Value

A data frame with columns containing the raw variance components for dam, sire, dam by sire, residual, total, additive genetic, non-additive genetic, and maternal. Also columns containing the raw variance components for remaining formula components. The number of rows in the data frame matches the total number of observations (N) for delete-one jackknife resampling or M groups for delete-d jackknife resampling to the lowest integer. Each row represents a deleted single observation or deleted d observations group.

Arguments

observ

Data frame of observed data

dam

Column name containing dam (female) parent identity information.

sire

Column name containing sire (male) parent identity information.

response

Column name containing the offspring (response) phenotype values.

remain

Remaining formula using lme4 package format.

ml

Default is FALSE for restricted maximum likelihood. Change to TRUE for maximum likelihood.

size

Default is 1 for delete-one jackknife resampling. If size > 1, delete-d jackknife resampling occurs removing a block d equal to size.

first

Number of initial sub-samples to run. Useful for examing if there is variation among sub-samples before jackknife resampling the entire data set. There can be little variation for delete-one jackknife resampling with large data sets, and delete-d jackknife resampling should be considered.

Details

Uses delete-one jackknife resampling (Efron & Tibshirani 1993, p. 141-145). For the option of delete-d jackknife resampling, the rows of the observed data frame are shuffled and a block of observations of size d is deleted sequentially. Extracts the dam, sire, dam, dam by sire, and residual variance components. Extracts any additional fixed effect and random effect variance components. The fixed-effect variance component is as a single group using the method described by Nakagawa and Schielzeth (2013). Calculates the total variance component. Calculates the additive genetic, non-additive genetic, and maternal variance components (see Lynch and Walsh 1998, p. 603).

References

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

Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.

Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x

See Also

JackLmer, JackLmer2

Examples

data(chinook_length) #Chinook salmon offspring length

#Delete-one
#length_jack3<- JackLmer3(observ=chinook_length,dam="dam",sire="sire",response="length",
#remain="egg_size + (1|tray)")
length_jack3<- JackLmer3(observ=chinook_length,dam="dam",sire="sire",response="length",
remain="egg_size + (1|tray)",first=2) #first 2

#Delete-d, d=5
#length_jack3.2<- JackLmer3(observ=chinook_length,dam="dam",sire="sire",response="length",
#remain="egg_size + (1|tray)",size=5)
length_jack3.2<- JackLmer3(observ=chinook_length,dam="dam",sire="sire",response="length",
remain="egg_size + (1|tray)",size=5,first=2) #first 2