| fullfact-package {fullfact} | R Documentation |
Full Factorial Breeding Analysis
Description
Full factorial breeding designs are useful for quantifying the amount of additive genetic, nonadditive genetic, and maternal variance that explain phenotypic traits. Such variance estimates are important for examining evolutionary potential. Traditionally, full factorial mating designs have been analyzed using a two- way analysis of variance, which may produce negative variance values and is not suited for unbalanced designs. Mixed-effects models do not produce negative variance values and are suited for unbalanced designs. However, extracting the variance components, calculating significance values, and estimating confidence intervals and/or power values for the components are not straightforward using traditional analytic methods.
In this package we address these issues and facilitate the analysis of full factorial mating designs with mixed-effects models. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap confidence intervals, which can then be plotted using a simple function. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses.
The package contains six vignettes containing detailed examples: browseVignettes(package="fullfact").
The paper associated with the package including worked examples is: Houde ALS, Pitcher TE. 2016. fullfact: an R package for the analysis of genetic and maternal variance components from full factorial mating designs. Ecology and evolution 6 (6), 1656-1665. doi: 10.1002/ece3.1943.
Details
The DESCRIPTION file:
| Package: | fullfact |
| Type: | Package |
| Title: | Full Factorial Breeding Analysis |
| Version: | 1.5.2 |
| Date: | 2024-02-04 |
| Author: | Aimee Lee Houde [aut, cre], Trevor Pitcher [aut] |
| Maintainer: | Aimee Lee Houde <aimee.lee.houde@gmail.com> |
| Depends: | R (>= 3.6) |
| Imports: | lme4, afex |
| VignetteBuilder: | knitr |
| Suggests: | knitr, rmarkdown |
| Description: | We facilitate the analysis of full factorial mating designs with mixed-effects models. The package contains six vignettes containing detailed examples. |
| License: | GPL (>=2) |
Index of help topics:
JackGlmer Jackknife components for non-normal data JackGlmer2 Jackknife components for non-normal data 2 JackGlmer3 Jackknife components for non-normal data 3 JackLmer Jackknife components for normal data JackLmer2 Jackknife components for normal data 2 JackLmer3 Jackknife components for normal data 3 barMANA Bargraph of confidence intervals boxMANA Boxplot of resampled results buildBinary Convert to a binary data frame buildMulti Convert to a multinomial frame chinook_bootL Chinook salmon length, bootstrap calculations chinook_bootS Chinook salmon survival, bootstrap data chinook_jackL Chinook salmon length, jackknife data chinook_jackS Chinook salmon survival, jackknife data chinook_length Chinook salmon length, raw data chinook_resampL Chinook salmon length, bootstrap resampled chinook_resampS Chinook salmon survival, bootstrap resampled chinook_survival Chinook salmon survival, raw data ciJack Jackknife confidence intervals ciJack2 Jackknife confidence intervals 2 ciJack3 Jackknife confidence intervals 3 ciMANA Bootstrap confidence intervals ciMANA2 Bootstrap confidence intervals 2 ciMANA3 Bootstrap confidence intervals 3 fullfact-package Full Factorial Breeding Analysis observGlmer Variance components for non-normal data observGlmer2 Variance components for non-normal data 2 observGlmer3 Variance components for non-normal data 3 observLmer Variance components for normal data observLmer2 Variance components for normal data 2 observLmer3 Variance components for normal data 3 powerGlmer Power analysis for non-normal data powerGlmer2 Power analysis for non-normal data 2 powerGlmer3 Power analysis for non-normal data 3 powerLmer Power analysis for normal data powerLmer2 Power analysis for normal data 2 powerLmer3 Power analysis for normal data 3 resampFamily Bootstrap resample within families resampGlmer Bootstrap components for non-normal data resampGlmer2 Bootstrap components for non-normal data 2 resampGlmer3 Bootstrap components for non-normal data 3 resampLmer Bootstrap components for normal data resampLmer2 Bootstrap components for normal data 2 resampLmer3 Bootstrap components for normal data 3 resampRepli Bootstrap resample within replicates
Author(s)
Aimee Lee Houde [aut, cre], Trevor Pitcher [aut]
Maintainer: Aimee Lee Houde <aimee.lee.houde@gmail.com>
References
Traditional full factorial breeding design analysis:
Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sinauer Associates, Massachusetts.
Residual variance component values for generalized linear mixed-effects models:
Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews 85(4): 935-956. DOI: 10.1111/j.1469-185X.2010.00141.x
Fixed effect variance component values for mixed-effects models:
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
Confidence intervals (bootstrap resampling, bias and acceleration correction, jackknife resampling):
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.
Data sources:
Pitcher TE, Neff BD. 2007. Genetic quality and offspring performance in Chinook salmon: implications for supportive breeding. Conservation Genetics 8(3):607-616. DOI: 10.1007/s10592-006-9204-z
Examples
data(chinook_length) #Chinook salmon offspring length
## Standard additive genetic, non-additive genetic, and maternal variance analysis
length_mod1<- observLmer(observ=chinook_length,dam="dam",sire="sire",response="length")
length_mod1
## Confidence intervals
##Bootstrap resampling of data: replicates within family
## Not run: resampRepli(dat=chinook_length,copy=c(3:8),family="family",replicate="repli",
iter=1000)
## End(Not run)
#saves the files in working directory: one for each replicate and
#one final (combined) file "resamp_datR.csv"
##Import file
#length_datR<- read.csv("resamp_datR.csv")
data(chinook_resampL) #same as length_datR, 5 iterations
##Models for the resampled data: standard analysis
## Not run: length_rcomp<- resampLmer(resamp=length_datR,dam="dam",sire="sire",
response="length",start=1,end=1000)
## End(Not run)
## 1. Uncorrected Bootstrap 95% confidence interval
#ciMANA(comp=length_rcomp)
data(chinook_bootL) #similar to length_rcomp, but 1,000 models
ciMANA(comp=chinook_bootL)
## 2. Bias and accelerated corrected Bootstrap 95% confidence interval
##Jackknife resampling of data, delete-one: for acceleration estimate
## Not run: length_jack<- JackLmer(observ=chinook_length,dam="dam",sire="sire",
response="length")
## End(Not run)
#ciMANA(comp=length_rcomp,bias=c(0,0.7192,0.2030),accel=length_jack)
data(chinook_jackL) #similar to length_jack, but all observations
ciMANA(comp=chinook_bootL,bias=c(0,0.7192,0.2030),accel=chinook_jackL)
##3. Jackknife 95% confidence interval
#ciJack(comp=length_jack,full=c(0,0.7192,0.2030,1.0404))
ciJack(comp=chinook_jackL,full=c(0,0.7192,0.2030,1.0404))