fullfact (version 1.0)

fullfact-package: \Sexpr[results=rd,stage=build]{tools:::Rd_package_title("#1")}fullfactFull Factorial Breeding Analysis

Description

Analysis of full factorial breeding designs. The package incorporates mixed-effects models suited for unbalanced experimental designs and non-normal data types. Extracts variance components and significance values for random and fixed effects. Calculates the amount of additive genetic, non-additive genetic, and maternal variance explaining phenotypic traits. Produces confidence values using a bootstrap resampling routine and can apply bias and acceleration correction. Also includes an option for producing jackknife resampling confidence intervals. Calculates the power values of random and fixed effects.

Details

The DESCRIPTION file: \Sexpr[results=rd,stage=build]{tools:::Rd_package_DESCRIPTION("#1")}fullfactThis package was not yet installed at build time.

\Sexpr[results=rd,stage=build]{tools:::Rd_package_indices("#1")}fullfact Index: This package was not yet installed at build time.

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(0.0000000,0.7192253,0.2029684),accel=length_jack)
data(chinook_jackL)  #similar to length_jack, but all observations
ciMANA(comp=chinook_bootL,bias=c(0.0000000,0.7192253,0.2029684),accel=chinook_jackL)

##3. Jackknife 95% confidence interval

#ciJack(comp=length_jack,full=c(0.0000000,0.7192253,0.2029684,1.0404425))
ciJack(comp=chinook_jackL,full=c(0.0000000,0.7192253,0.2029684,1.0404425))