noffspring: Calculates Optimum Numbers of Offspring
In optiSel: Optimum Contribution Selection and Population Genetics
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Calculates the optimum numbers of offspring from optimum contributions of selection candidates.
Usage
1
noffspring(cand, N, random=TRUE)
Arguments
cand
Data frame with optimum contributions (column oc), sexes (column Sex), and IDs (column Indiv) of the selection candidates.
N
Desired number of individuals in the offspring population.
random
Logical. If 2*N*oc[i] is not an integer value (say 2*N*oc[i]=11.4) then individual i will have either 11 or 12 offspring. The actual number is either determined randomly or not.
Details
The function calculates the optimum numbers of offspring of the selection candidates from the optimum contributions cand$oc and the size N of the offspring population.
Value
Data frame with column Indiv containing the individual IDs and column nOff containing the optimum numbers of offspring.
Column nOff is approximately 2*N*cand$oc with sum(noff[cand$Sex=="male"])=N and sum(noff[cand$Sex=="female"])=N.
Author(s)
Robin Wellmann
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
set.seed(1)
data(PedigWithErrors)
Pedig <- prePed(PedigWithErrors, thisBreed="Hinterwaelder")
use <- Pedig$Born %in% (1998:2008) & Pedig$Breed=="Hinterwaelder"
Population <- sampleIndiv(Pedig[use, ], each=50)
pKin <- pedIBD(Pedig, keep.only=Population)
Phen <- Pedig[Population, ]
Phen$isCandidate <- Phen$Born %in% (2003:2008)
cont <- agecont(Pedig, Population)
cand <- candes(phen=Phen, fA=pedIBD(Pedig, keep.only=Phen$Indiv), cont=cont)
con <- list(ub.fA=0.0175, uniform="female")
Offspring <- opticont("max.BV", cand, con, trace = FALSE)
N <- 250
Candidate <- Offspring$parent
Candidate$nOff <- noffspring(Candidate, N)$nOff
sum(Candidate$nOff[Candidate$Sex=="male"])
#[1] 250
sum(Candidate$nOff[Candidate$Sex=="female"])
#[1] 250
round(2*N*Candidate$oc-Candidate$nOff, 2)
Example output
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl.init' failed, running with 'rgl.useNULL = TRUE'.
Pedigree loops were detected. We recommend to correct them manually before
using prePed(). The parents of the following individuals are set to unknown
to remove the loops.
Sire Dam
276000802875148 276000803622423 276000890878480
276000811476506 276000810087663 276000811476506
The sex of the following animals was not compatible with the pedigree, so
it was modified:
Sire Dam
276000810087663 276000802940621 276000802925028
The population is evaluated at time 2008
Mean values of the parameters are: Value
for trait 'BV' in Hinterwaelder: 0.7062
for kinship 'fA' in Hinterwaelder: 0.0164
Available objective functions and constraints:
for trait 'BV' in Hinterwaelder: min.BV, max.BV, lb.BV, eq.BV, ub.BV
for kinship 'fA' in Hinterwaelder: min.fA, ub.fA
ub lb uniform
Using solver 'cccp2' with parameters:
Value
trace 0
abstol 1e-05
feastol 1e-05
stepadj 0.9
maxiters 100
reltol 1e-06
beta 0.5
valid solver status
TRUE cccp2 optimal
Variable Value Bound OK?
---------------------------------------------------------
BV Hinterwaelder 0.8336 max :
---------------------------------------------------------
lower bounds all x >= lb : TRUE
upper bounds all x <= ub : TRUE
breed contribution Hinterwaelder 1 == 1 : TRUE
sex contrib. diff. Hinterwaelder 0 == 0 : TRUE
BV Hinterwaelder 0.8336 :
fA Hinterwaelder 0.0175 <= 0.0175 : TRUE
---------------------------------------------------------
[1] 250
[1] 250
[1] 0.27 -0.73 0.27 -0.73 -0.73 0.00 0.27 0.27 -0.73 0.00 -0.73 -0.73
[13] 0.27 -0.73 0.00 -0.73 0.27 0.27 -0.73 0.27 0.27 0.27 0.27 -0.73
[25] 0.27 -0.73 0.27 -0.73 0.27 0.27 0.27 0.27 -0.73 0.27 0.27 -0.73
[37] 0.27 0.27 0.27 0.27 0.00 0.27 0.00 0.27 0.27 0.27 0.27 0.27
[49] -0.73 0.00 0.00 0.24 0.00 0.00 0.24 0.24 0.24 0.24 0.24 0.24
[61] 0.24 0.24 0.24 -0.76 0.24 -0.76 -0.76 0.24 0.24 0.24 0.24 0.24
[73] 0.24 -0.76 0.24 -0.76 0.24 0.24 0.24 0.24 -0.76 0.24 -0.76 0.24
[85] 0.24 0.24 0.24 -0.76 -0.76 -0.76 0.00 0.24 -0.76 0.24 -0.76 -0.76
[97] 0.24 0.24 -0.76 -0.76 0.00 0.32 0.32 -0.68 0.32 0.32 0.32 -0.68
[109] 0.00 -0.68 0.00 0.32 0.32 0.32 0.32 0.32 0.32 -0.68 -0.68 0.32
[121] 0.32 0.32 0.32 -0.68 0.32 -0.68 0.32 0.32 0.32 -0.68 -0.68 -0.68
[133] 0.32 0.32 0.32 0.32 0.00 -0.68 0.32 -0.68 0.32 0.32 0.32 0.32
[145] 0.00 -0.68 0.32 0.32 -0.68 0.32 0.00 0.49 -0.51 0.49 -0.51 0.49
[157] -0.51 -0.51 0.49 -0.51 0.49 0.49 -0.51 0.49 0.49 0.49 0.49 0.49
[169] -0.51 0.49 -0.51 0.49 -0.51 0.49 -0.51 0.49 0.00 0.00 0.49 0.49
[181] 0.49 -0.51 0.49 0.49 0.00 -0.51 0.49 0.49 -0.51 0.49 0.00 0.49
[193] 0.49 0.49 -0.51 0.49 0.00 0.00 -0.51 -0.51 0.51 -0.49 -0.49 0.00
[205] -0.49 0.51 0.51 -0.49 0.00 -0.49 -0.49 0.51 -0.49 0.51 -0.49 0.51
[217] -0.49 -0.49 0.51 -0.49 0.51 0.51 -0.49 0.51 -0.49 0.51 -0.49 0.51
[229] -0.49 -0.49 0.51 -0.49 0.51 0.00 -0.49 0.51 0.51 -0.49 0.51 0.51
[241] -0.49 -0.49 -0.49 0.51 -0.49 -0.49 0.51 0.51 -0.49 0.00 -0.44 -0.44
[253] -0.44 0.56 -0.44 0.56 -0.44 0.56 -0.44 -0.44 -0.44 0.56 -0.44 -0.44
[265] 0.56 -0.44 -0.44 -0.44 -0.44 -0.44 -0.44 0.00 -0.44 0.56 -0.44 0.00
[277] -0.44 -0.44 -0.44 -0.44 0.56 -0.44 -0.44 0.56 0.56 -0.44 -0.44 -0.44
[289] -0.44 0.56 0.56 0.00 0.56 0.00 -0.44 -0.44 0.56 0.56 0.56 0.00
[301] 0.80 0.80 -0.20 -0.20 -0.20 0.80 0.80 0.80 0.80 0.00 -0.20 -0.20
[313] -0.20 0.80 -0.20 0.80 0.80 0.80 0.00 0.80 -0.20 0.80 0.00 0.00
[325] -0.20 -0.20 0.80 -0.20 -0.20 0.80 0.80 -0.20 -0.20 0.80 0.80 -0.20
[337] -0.20 -0.20 -0.20 -0.20 -0.20 0.80 -0.20 -0.20 -0.20 0.00 -0.20 -0.20
[349] 0.80 -0.20 -0.10 0.90 0.90 0.90 -0.10 -0.10 -0.10 -0.10 -0.10 0.00
[361] -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 0.90
[373] -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 0.90 0.00 -0.10 -0.10
[385] -0.10 -0.10 -0.10 -0.10 0.90 -0.10 -0.10 -0.10 -0.10 0.90 0.00 -0.10
[397] -0.10 0.90 -0.10 0.90 0.02 0.02 0.02 0.00 0.02 -0.98 0.02 0.02
[409] 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
[421] 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
[433] -0.98 0.00 0.02 0.02 0.02 0.02 0.00 0.02 0.02 0.02 0.02 0.02
[445] 0.00 0.02 0.02 0.00 0.02 0.02 -0.55 -0.55 0.00 -0.55 -0.55 -0.55
[457] 0.45 0.45 -0.55 0.00 0.00 0.00 0.45 -0.55 0.45 0.45 -0.55 0.45
[469] 0.45 -0.55 -0.55 -0.55 0.45 0.00 -0.55 0.00 0.00 0.45 -0.55 0.45
[481] 0.00 -0.55 -0.55 0.00 0.45 -0.55 0.00 -0.46 -0.55 -0.55 -0.55 0.45
[493] 0.45 0.45 0.19 -0.55 -0.55 0.45 -0.55 -0.55 0.00 0.03 0.22 0.03
[505] 0.00 0.00 0.00 0.03 0.03 0.00 0.03 0.03 0.03 0.00 0.00 0.03
[517] 0.03 0.03 0.00 0.03 0.03 0.03 0.03 0.03 0.00 0.03 0.03 0.03
[529] 0.03 0.03 0.03 0.03 0.00 0.03 0.03 0.03 0.00 0.03 0.03 0.03
[541] 0.03 0.00 0.03 -0.97 0.03 0.03 0.00 0.03 0.03 0.03
optiSel documentation built on Jan. 16, 2021, 5:30 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Author(s) Examples
Description
Calculates the optimum numbers of offspring from optimum contributions of selection candidates.
Usage
1 | noffspring(cand, N, random=TRUE)
|
Arguments
cand |
Data frame with optimum contributions (column |
N |
Desired number of individuals in the offspring population. |
random |
Logical. If |
Details
The function calculates the optimum numbers of offspring of the selection candidates from the optimum contributions cand$oc and the size N of the offspring population.
Value
Data frame with column Indiv containing the individual IDs and column nOff containing the optimum numbers of offspring.
Column nOff is approximately 2*N*cand$oc with sum(noff[cand$Sex=="male"])=N and sum(noff[cand$Sex=="female"])=N.
Author(s)
Robin Wellmann
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | set.seed(1)
data(PedigWithErrors)
Pedig <- prePed(PedigWithErrors, thisBreed="Hinterwaelder")
use <- Pedig$Born %in% (1998:2008) & Pedig$Breed=="Hinterwaelder"
Population <- sampleIndiv(Pedig[use, ], each=50)
pKin <- pedIBD(Pedig, keep.only=Population)
Phen <- Pedig[Population, ]
Phen$isCandidate <- Phen$Born %in% (2003:2008)
cont <- agecont(Pedig, Population)
cand <- candes(phen=Phen, fA=pedIBD(Pedig, keep.only=Phen$Indiv), cont=cont)
con <- list(ub.fA=0.0175, uniform="female")
Offspring <- opticont("max.BV", cand, con, trace = FALSE)
N <- 250
Candidate <- Offspring$parent
Candidate$nOff <- noffspring(Candidate, N)$nOff
sum(Candidate$nOff[Candidate$Sex=="male"])
#[1] 250
sum(Candidate$nOff[Candidate$Sex=="female"])
#[1] 250
round(2*N*Candidate$oc-Candidate$nOff, 2)
|
Example output
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl.init' failed, running with 'rgl.useNULL = TRUE'.
Pedigree loops were detected. We recommend to correct them manually before
using prePed(). The parents of the following individuals are set to unknown
to remove the loops.
Sire Dam
276000802875148 276000803622423 276000890878480
276000811476506 276000810087663 276000811476506
The sex of the following animals was not compatible with the pedigree, so
it was modified:
Sire Dam
276000810087663 276000802940621 276000802925028
The population is evaluated at time 2008
Mean values of the parameters are: Value
for trait 'BV' in Hinterwaelder: 0.7062
for kinship 'fA' in Hinterwaelder: 0.0164
Available objective functions and constraints:
for trait 'BV' in Hinterwaelder: min.BV, max.BV, lb.BV, eq.BV, ub.BV
for kinship 'fA' in Hinterwaelder: min.fA, ub.fA
ub lb uniform
Using solver 'cccp2' with parameters:
Value
trace 0
abstol 1e-05
feastol 1e-05
stepadj 0.9
maxiters 100
reltol 1e-06
beta 0.5
valid solver status
TRUE cccp2 optimal
Variable Value Bound OK?
---------------------------------------------------------
BV Hinterwaelder 0.8336 max :
---------------------------------------------------------
lower bounds all x >= lb : TRUE
upper bounds all x <= ub : TRUE
breed contribution Hinterwaelder 1 == 1 : TRUE
sex contrib. diff. Hinterwaelder 0 == 0 : TRUE
BV Hinterwaelder 0.8336 :
fA Hinterwaelder 0.0175 <= 0.0175 : TRUE
---------------------------------------------------------
[1] 250
[1] 250
[1] 0.27 -0.73 0.27 -0.73 -0.73 0.00 0.27 0.27 -0.73 0.00 -0.73 -0.73
[13] 0.27 -0.73 0.00 -0.73 0.27 0.27 -0.73 0.27 0.27 0.27 0.27 -0.73
[25] 0.27 -0.73 0.27 -0.73 0.27 0.27 0.27 0.27 -0.73 0.27 0.27 -0.73
[37] 0.27 0.27 0.27 0.27 0.00 0.27 0.00 0.27 0.27 0.27 0.27 0.27
[49] -0.73 0.00 0.00 0.24 0.00 0.00 0.24 0.24 0.24 0.24 0.24 0.24
[61] 0.24 0.24 0.24 -0.76 0.24 -0.76 -0.76 0.24 0.24 0.24 0.24 0.24
[73] 0.24 -0.76 0.24 -0.76 0.24 0.24 0.24 0.24 -0.76 0.24 -0.76 0.24
[85] 0.24 0.24 0.24 -0.76 -0.76 -0.76 0.00 0.24 -0.76 0.24 -0.76 -0.76
[97] 0.24 0.24 -0.76 -0.76 0.00 0.32 0.32 -0.68 0.32 0.32 0.32 -0.68
[109] 0.00 -0.68 0.00 0.32 0.32 0.32 0.32 0.32 0.32 -0.68 -0.68 0.32
[121] 0.32 0.32 0.32 -0.68 0.32 -0.68 0.32 0.32 0.32 -0.68 -0.68 -0.68
[133] 0.32 0.32 0.32 0.32 0.00 -0.68 0.32 -0.68 0.32 0.32 0.32 0.32
[145] 0.00 -0.68 0.32 0.32 -0.68 0.32 0.00 0.49 -0.51 0.49 -0.51 0.49
[157] -0.51 -0.51 0.49 -0.51 0.49 0.49 -0.51 0.49 0.49 0.49 0.49 0.49
[169] -0.51 0.49 -0.51 0.49 -0.51 0.49 -0.51 0.49 0.00 0.00 0.49 0.49
[181] 0.49 -0.51 0.49 0.49 0.00 -0.51 0.49 0.49 -0.51 0.49 0.00 0.49
[193] 0.49 0.49 -0.51 0.49 0.00 0.00 -0.51 -0.51 0.51 -0.49 -0.49 0.00
[205] -0.49 0.51 0.51 -0.49 0.00 -0.49 -0.49 0.51 -0.49 0.51 -0.49 0.51
[217] -0.49 -0.49 0.51 -0.49 0.51 0.51 -0.49 0.51 -0.49 0.51 -0.49 0.51
[229] -0.49 -0.49 0.51 -0.49 0.51 0.00 -0.49 0.51 0.51 -0.49 0.51 0.51
[241] -0.49 -0.49 -0.49 0.51 -0.49 -0.49 0.51 0.51 -0.49 0.00 -0.44 -0.44
[253] -0.44 0.56 -0.44 0.56 -0.44 0.56 -0.44 -0.44 -0.44 0.56 -0.44 -0.44
[265] 0.56 -0.44 -0.44 -0.44 -0.44 -0.44 -0.44 0.00 -0.44 0.56 -0.44 0.00
[277] -0.44 -0.44 -0.44 -0.44 0.56 -0.44 -0.44 0.56 0.56 -0.44 -0.44 -0.44
[289] -0.44 0.56 0.56 0.00 0.56 0.00 -0.44 -0.44 0.56 0.56 0.56 0.00
[301] 0.80 0.80 -0.20 -0.20 -0.20 0.80 0.80 0.80 0.80 0.00 -0.20 -0.20
[313] -0.20 0.80 -0.20 0.80 0.80 0.80 0.00 0.80 -0.20 0.80 0.00 0.00
[325] -0.20 -0.20 0.80 -0.20 -0.20 0.80 0.80 -0.20 -0.20 0.80 0.80 -0.20
[337] -0.20 -0.20 -0.20 -0.20 -0.20 0.80 -0.20 -0.20 -0.20 0.00 -0.20 -0.20
[349] 0.80 -0.20 -0.10 0.90 0.90 0.90 -0.10 -0.10 -0.10 -0.10 -0.10 0.00
[361] -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 0.90
[373] -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 0.90 0.00 -0.10 -0.10
[385] -0.10 -0.10 -0.10 -0.10 0.90 -0.10 -0.10 -0.10 -0.10 0.90 0.00 -0.10
[397] -0.10 0.90 -0.10 0.90 0.02 0.02 0.02 0.00 0.02 -0.98 0.02 0.02
[409] 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
[421] 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
[433] -0.98 0.00 0.02 0.02 0.02 0.02 0.00 0.02 0.02 0.02 0.02 0.02
[445] 0.00 0.02 0.02 0.00 0.02 0.02 -0.55 -0.55 0.00 -0.55 -0.55 -0.55
[457] 0.45 0.45 -0.55 0.00 0.00 0.00 0.45 -0.55 0.45 0.45 -0.55 0.45
[469] 0.45 -0.55 -0.55 -0.55 0.45 0.00 -0.55 0.00 0.00 0.45 -0.55 0.45
[481] 0.00 -0.55 -0.55 0.00 0.45 -0.55 0.00 -0.46 -0.55 -0.55 -0.55 0.45
[493] 0.45 0.45 0.19 -0.55 -0.55 0.45 -0.55 -0.55 0.00 0.03 0.22 0.03
[505] 0.00 0.00 0.00 0.03 0.03 0.00 0.03 0.03 0.03 0.00 0.00 0.03
[517] 0.03 0.03 0.00 0.03 0.03 0.03 0.03 0.03 0.00 0.03 0.03 0.03
[529] 0.03 0.03 0.03 0.03 0.00 0.03 0.03 0.03 0.00 0.03 0.03 0.03
[541] 0.03 0.00 0.03 -0.97 0.03 0.03 0.00 0.03 0.03 0.03
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.