R Dataset / Package gap / meyer

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<iframe src="https://embed.picostat.com/r-dataset-package-gap-meyer.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-86471.csv 4.77 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the meyer data set which pertains to A pedigree data on 282 animals deriving from two generations. The meyer data set is found in the gap R package. You can load the meyer data set in R by issuing the following command at the console data("meyer"). This will load the data into a variable called meyer. If R says the meyer data set is not found, you can try installing the package by issuing this command install.packages("gap") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the meyer R data set. The size of this file is about 4,884 bytes.

Documentation

A pedigree data on 282 animals deriving from two generations

Description

A data frame attributed to Meyer (1989).

“The pedigrees for each of these 282 animals derive from an additional 24 base population (Generation 0) animals that do not have records of their own but, nevertheless, are of interest with respect to the inference on their own additive genetic values. Furthermore, it is presumed that these original 24 base animals are not related to each other. Therefore, the row dimension of u is 306 (282+24).” (Templeman \& Rosa 2004)

Usage

data(meyer)

Format

A data frame containing 306 records

Source

Meyer K (1989). Restricted maximum likelihood to estimate variance components for animal models with several random effects using a derivative-free algorithm. Genetics, Selection, Evolution 21:317-340.

Tempelman RJ, Rosa GJM. Empirical Bayes Approaches to Mixed Model Inference in Quantitative Genetics. in Saxton AM (Ed). Genetic Analysis of Complex Traits Using SAS, chapter 7. SAS Institute Inc., Cary, NC, USA, 2004

Examples

## Not run: 
library(gap)
meyer <- within(meyer,{
   g1 <- ifelse(generation==1,1,0)
   g2 <- ifelse(generation==2,1,0)
})
lm(y~-1+g1+g2,data=meyer)
library(MCMCglmm)
m <-MCMCglmm(y~-1+g1+g2,random=animal~1,pedigree=meyer[,1:3],data=meyer,verbose=FALSE)
summary(m)
plot(m)   meyer <- within(meyer,{
   id <- animal
   animal <- ifelse(!is.na(animal),animal,0)
   dam <- ifelse(!is.na(dam),dam,0)
   sire <- ifelse(!is.na(sire),sire,0)
})
# library(kinship)
# A <- with(meyer,kinship(animal,sire,dam))*2A <- kin.morgan(meyer)$kin.matrix*2library(regress)
regress(y~-1+g1+g2,~A,data=meyer)
prior <- list(R=list(V=1, nu=0.002), G=list(G1=list(V=1, nu=0.002)))
m2 <- MCMCgrm(y~-1+g1+g2,prior,meyer,A,singular.ok=TRUE,verbose=FALSE)
summary(m2)
plot(m2)   ## End(Not run)
--

Dataset imported from https://www.r-project.org.

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