ggTutorial | R Documentation |
The dataset was simulated using the simGG
function so that the
pedigree contains a base population comprised of founders and non-founder
immigrants. These data are then used in the main manuscript and tutorials
accompanying Wolak & Reid (2017).
ggTutorial
A data.frame
with 6000 observations on the following 10
variables:
an integer vector specifying the 6000 unique individual identities
an integer vector specifying the unique dam for each individual
an integer vector specifying the unique sire for each individual
a numeric vector of the average autosomal total additive
genetic effects (u
) of each individual's parents
a numeric vector of the Mendelian sampling deviations
from parAvgU
autosomal total additive genetic effects that is
unique to each individual
a numeric vector of the total autosomal additive genetic
effects underlying p
a numeric vector of the residual (environmental) effects
underlying p
a numeric vector of phenotypic values
an integer vector with 0
for individuals born in the
focal population and 1
for individuals born outside of the
focal population, but immigrated
an integer vector specifying the generation in which each individual was born
The dataset was simulated as described in the ‘examples’ section
using the simGG
function. Full details of the function and
dataset can be found in Wolak & Reid (2017).
The data.frame
contains 6000 individuals across 15 generations. In
each generation, the carrying capacity is limited to 400 individuals, the
number of mating pairs limited to 200 pairs, and 40 immigrants per
generation arrive starting in the second generation.
The breeding values of the founders are drawn from a normal distribution with an expected mean of 0 and a variance of 1. The breeding values of all immigrants are drawn from a normal distribution with an expected mean of 3 and variance of 1. Consequently, the expected difference between mean breeding values in the founders and immigrants is 3. All individuals are assigned a residual (environmental) deviation that is drawn from a normal distribution with an expected mean of 0 and variance of 1.
Wolak, M.E. & J.M. 2017. Accounting for genetic differences among unknown parents in microevolutionary studies: how to include genetic groups in quantitative genetic animal models. Journal of Animal Ecology 86:7-20. doi:10.1111/1365-2656.12597
set.seed(102) #<-- seed value used originally
library(nadiv)
# create data using `simGG()`
ggTutorial <- simGG(K = 400, pairs = 200, noff = 4, g = 15,
nimm = 40, nimmG = seq(2, 14, 1), # nimmG default value
VAf = 1, VAi = 1, VRf = 1, VRi = 1, # all default values
mup = 20, muf = 0, mui = 3, murf = 0, muri = 0, # mup and mui non-default values
d_bvf = 0, d_bvi = 0, d_rf = 0, d_ri = 0) # all default values
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