the {qgen} | R Documentation |
For theoretical investigations we need a function that takes the
chosen causal genetic parameters and transforms them into observational
parameters. Additionally the form of the object needs to be adjusted
to fit the form of an object of class paraDATA
. This function
handles the following causal variance components: additive; dominance,
maternal, micro-environmental; and the following sources of observable
variance components: sire, dam, and individuals.
the(partitioning = "all", chN = 1, # number of characters enN = 1, # number of environments fbN = 1, # number of fixed blocks rbN = 1, # number of random blocks siN = 100, # number sires (within each block) daN = 6, # number of dams (within each sire) idN = 3, # number of individuals (within each dam) randomblockCor = matrix(0.5, chN*enN, chN*enN), randomblockVar = rep(100, chN*enN), additiveCor = matrix(0.5, chN*enN, chN*enN), additiveVar = rep(100, chN*enN), dominanceCor = matrix(0.5, chN*enN, chN*enN), dominanceVar = rep(100, chN*enN), maternalCor = matrix(0.5, chN*enN, chN*enN), maternalVar = rep(100, chN*enN), environmentalCor = matrix(0, chN*enN, chN*enN), environmentalVar = rep(100, chN*enN), ch.names = paste("ch",1:chN,sep=""), en.names = paste("en",1:enN,sep=""), fb.names = paste("fb",1:fbN,sep=""), fixe = array(0, dim=c(enN, chN, fbN)), miss = array(0, dim=c(enN, chN, fbN)), file=TRUE, path="~/qgen/")
partitioning |
character string: The method of variance
partitioning used throughout the analysis
|
chN |
number of characters |
enN |
number of environments |
fbN |
number of fixed blocks |
rbN |
number of random blocks, a random factor crossed with sire |
siN |
number of sires |
daN |
number of dams within sires |
idN |
number of individuals within dams |
randomblockCor |
for the random block effects |
additiveCor |
for the additive genetic effects |
dominanceCor |
for the dominance genetic effects |
maternalCor |
for the maternal effects |
environmentalCor |
for the microenvironmental effects |
randomblockVar |
for the random blocks |
additiveVar |
for the additive genetic effects |
dominanceVar |
for the dominance genetic effects |
maternalVar |
for the maternal effects |
environmentalVar |
for the microenvironmental effects |
fixe |
array with the mean of every fixedblock–environment–character combinations (sequence: character in environments in fixedblock, ex. fb1: en1ch1, en1ch2...en2ch1...enxchx fb2: en1ch1,...) |
miss |
vector with the proportion of randomly missing values per environment–character combination; if scalar all are assumed to be equal |
en.names |
vector of character strings: names for the environments |
ch.names |
vector of character strings: names for the traits |
fb.names |
vector of character strings: names for the fixed blocks |
file |
logical flag: Should the object be written to a file called "the.rda"? |
path |
character vector: containing a single path name |
This function transforms the causal sources of variance (additive genetic effects, dominance genetic effects, common (maternal) environmental effects, and individual (special) environmental effects) into the three observable variance components from a nested full–sib, half–sib mating design (North-Carolina Design I). Assumptions: no sources of epistatic variance; see Lynch & Walsh 1997, p.572.
a paraDATA
object: with the individual slots
orig |
fully specified |
supl |
fully specified |
para |
fully specified |
data |
empty |
spec |
empty |
name: theoretical investigations on chosen biological parameters
Lynch, M. and Walsh, B. (1997) Genetics and analysis of quantitative traits. Sinauer.
## create a paraDATA-object ## (with a full para section and an empty DATA section) the(file=FALSE)