README.md
In jwoollia/DenHaag: Simulation of Populations with Managed Inbreeding
DenHaag is a package of functions for simulating populations using various options for managing inbreeding. The package provides a function for establising base population new_pop(), and the simplest option is then to add generations with random pairing of males and females using the function add_gen(). Both functions output a data frame containing data on pedigree ("id", "sire", "dam" and inbreeding coefficient "f"), a phenotype ("ptype") with true breeding value ("tbv"), an estimate of breeding value ("ebv"). The populations are assumed to have two sexes so the data frame also contains data on sex ("sex"), and also a data column called "noff" which is set by the user to determine the number of offspring each parent has in the next generation. The EBV is calculated within add_gen() using a call to blup(), which in turn makes a call to function a_inv().
The base is set up by calling new_pop(nn,hh) where nn is the number of individuals and hh is the heritability of the trait. The functions in the package assume an equal number of males and females in the population, and so new_pop() checks that the number in the base is an even positive integer and flags an error if not. It also flags an error if the heritability is outside the open interval (0,1).
library("DenHaag", lib.loc="~/R/win-library/3.3")
# new_pop() accepts numbers ...
my.df <- new_pop(7,0.25)
## [1] "Census size is expected to be even not odd! 7!"
my.df <- new_pop(8,1.25)
## [1] "Heritability out of bounds! 1.25!"
# ... or assigned variables
my.nn <- 8; my.hh <- 0.25
my.df <- new_pop(my.nn,my.hh)
## [1] "Creating 8 individuals for base generation"
## id sex noff ptype ebv sire dam tbv f
## 1 1 0 1.1115616 0.27789041 0 0 1.4538470 0
## 2 1 0 -0.6444710 -0.16111776 0 0 -0.4767513 0
## 3 1 0 0.5282433 0.13206082 0 0 -0.1035248 0
## 4 1 0 -0.4727104 -0.11817761 0 0 0.4150990 0
## 5 2 0 -0.8769355 -0.21923388 0 0 0.5052995 0
## 6 2 0 2.0394407 0.50986017 0 0 0.7150695 0
## 7 2 0 0.2724047 0.06810118 0 0 0.0370429 0
## 8 2 0 1.7381912 0.43454781 0 0 0.5985120 0
Note "noff" is set to 0. The user selects which individuals become parents and the number of offspring for each parent by assigning "noff" values.
my.df$noff[2]=4; my.df$noff[4]=4; my.df$noff[5]=4; my.df$noff[7]=4
The next generation is then formed by calling add_gen(my.df,my.hh). The output from add_gen() is an extended data frame. The function add_gen() detects errors in setting "noff" such as total offspring not being a even positive integer, or numbers of offspring from males not being equal to the number of offsring from females.
my.df$noff[2]=4; my.df$noff[4]=3; my.df$noff[5]=4; my.df$noff[7]=3
my.df <- add_gen(my.df,my.hh)
## [1] "Offspring numbers are expected to be even not odd! 7!"
my.df$noff[2]=4; my.df$noff[4]=4; my.df$noff[5]=4; my.df$noff[7]=3
my.df <- add_gen(my.df,my.hh)
## [1] "Numbers of male and female parents unequal in matings!"
# the heritability can be entered as a number
my.df$noff[2]=4; my.df$noff[4]=4; my.df$noff[5]=4; my.df$noff[7]=4
my.df <- add_gen(my.df,0.25)
## [1] "Creating 8 offspring"
## id sex noff ptype ebv sire dam tbv f
## 1 1 0 1.1115616 0.277890408 0 0 1.45384703 0
## 2 1 0 -0.6444710 -0.034343695 0 0 -0.47675133 0
## 3 1 0 0.5282433 0.132060817 0 0 -0.10352478 0
## 4 1 0 -0.4727104 -0.167030941 0 0 0.41509903 0
## 5 2 0 -0.8769355 -0.068519477 0 0 0.50529954 0
## 6 2 0 2.0394407 0.509860174 0 0 0.71506947 0
## 7 2 0 0.2724047 -0.004692485 0 0 0.03704290 0
## 8 2 0 1.7381912 0.434547807 0 0 0.59851201 0
## 9 1 0 -0.9563188 -0.210212732 4 7 -0.03663645 0
## 10 1 0 -0.5640533 -0.181529215 4 5 0.54092939 0
## 11 1 0 0.9407434 0.090307702 2 5 0.61727108 0
## 12 1 0 0.7876834 0.011576024 4 5 0.56567800 0
## 13 2 0 -0.1624631 -0.124159193 4 5 0.69237522 0
## 14 2 0 0.8863580 0.109892779 2 7 0.13421549 0
## 15 2 0 -1.5201154 -0.233889136 2 7 0.34568413 0
## 16 2 0 0.7662527 0.092734885 2 7 0.03717500 0
Note that on output my.df has "noff" set to 0. Successive generations are then produced by repeated assigment of "noff" and calls to add_gen().
Recommended contributions can be obtained for achieving a target group coancestry by setting a group coancestry, say my.gc, followed by a call to oc_sel(my.df,my.tp,my.tc,my.gc). The parameter my.tp is a number which gives the number of eligible candidates for selection, which are assumed to be the most recent individuals. The parameter my.tc is the total number of offspring required in the next generation, which is only used to scale the optimum contributions to a projected number of offspring - although these projections are not integer! The function oc_sel() calls a_mat() to produce the numerator relationships among the candidates. The function prints a table listing selected parents and contributions but the function only returns "TRUE" or "FALSE" indicating the success of the algorithm. The user can then set "noff" guided by the recommendations.
my.gc <- 0.2
oc_sel(my.df,8,8,my.gc)
## [1] "Recommendations for 8 offspring from the most recent 8 parents"
## id sex ebv c noff
## 11 1 0.09030770 0.221437169 3.54299471
## 12 1 0.01157602 0.278562831 4.45700529
## 13 2 -0.12415919 0.001382103 0.02211365
## 14 2 0.10989278 0.267062447 4.27299916
## 16 2 0.09273488 0.231555450 3.70488719
## [1] TRUE
# NOTE due to authors inexperience of 'SWEAVE", the following may not agree with
# the recommendations!
my.df$noff[9]=1; my.df$noff[11]=4; my.df$noff[12]=3
my.df$noff[14]=3; my.df$noff[15]=2; my.df$noff[16]=3
my.df <- add_gen(my.df,0.25)
## [1] "Creating 8 offspring"
## id sex noff ptype ebv sire dam tbv f
## 1 1 0 1.11156163 0.277890408 0 0 1.453847029 0.000
## 2 1 0 -0.64447104 0.063665337 0 0 -0.476751331 0.000
## 3 1 0 0.52824327 0.132060817 0 0 -0.103524785 0.000
## 4 1 0 -0.47271044 -0.170069089 0 0 0.415099031 0.000
## 5 2 0 -0.87693550 -0.019282829 0 0 0.505299538 0.000
## 6 2 0 2.03944069 0.509860174 0 0 0.715069469 0.000
## 7 2 0 0.27240472 0.041041750 0 0 0.037042896 0.000
## 8 2 0 1.73819123 0.434547807 0 0 0.598512012 0.000
## 9 1 0 -0.95631884 -0.187532041 4 7 -0.036636447 0.000
## 10 1 0 -0.56405325 -0.161729858 4 5 0.540929392 0.000
## 11 1 0 0.94074343 0.234962920 2 5 0.617271077 0.000
## 12 1 0 0.78768342 0.035891547 4 5 0.565677997 0.000
## 13 2 0 -0.16246309 -0.104359835 4 5 0.692375218 0.000
## 14 2 0 0.88635799 0.159595980 2 7 0.134215495 0.000
## 15 2 0 -1.52011541 -0.085890714 2 7 0.345684130 0.000
## 16 2 0 0.76625273 0.170294494 2 7 0.037174999 0.000
## 17 1 0 0.05761065 -0.003742504 9 14 0.779866416 0.125
## 18 1 0 0.89620687 0.106601338 12 15 0.722394743 0.000
## 19 1 0 0.20228744 0.197994877 11 14 0.088293309 0.125
## 20 1 0 0.56443435 0.144521567 11 15 0.729130167 0.125
## 21 2 0 0.19934688 0.116843571 12 16 0.833518634 0.000
## 22 2 0 1.31067520 0.360921063 11 16 0.603970399 0.125
## 23 2 0 -0.84060325 -0.031720732 12 16 0.201733473 0.000
## 24 2 0 -0.07369115 0.158569364 11 14 0.009346568 0.125
An alternative to setting a target group coancestry is to set a cost of inbreeding and use penalised contributions. Analagous to oc_sel(), recommended contributions for penalised contributions can be obtained by using the function cost_f(my.df,my.tp,my.tc,my.cost) where the first three arguments are as with oc_sel() and the final argument is set by the user.
my.cost <- 1000
cost_f(my.df,8,8,my.cost)
## [1] "Recommendations for 8 offspring from the most recent 8 parents"
## [1] "Recommendations have group coancestry 0.213942324423893"
## id sex ebv c noff
## 17 1 -0.003742504 0.16981576 2.717052
## 18 1 0.106601338 0.16029243 2.564679
## 19 1 0.197994877 0.07376138 1.180182
## 20 1 0.144521567 0.09613043 1.538087
## 21 2 0.116843571 0.15385620 2.461699
## 22 2 0.360921063 0.08990035 1.438406
## 23 2 -0.031720732 0.15370763 2.459322
## 24 2 0.158569364 0.10253581 1.640573
## [1] TRUE
# NOTE due to authors inexperience of 'SWEAVE", the following may not agree with
# the recommendations!
my.df$noff[17]=2; my.df$noff[18]=2; my.df$noff[19]=2; my.df$noff[20]=2
my.df$noff[21]=4; my.df$noff[22]=1; my.df$noff[23]=1; my.df$noff[24]=2
my.df <- add_gen(my.df,0.25)
## [1] "Creating 8 offspring"
## id sex noff ptype ebv sire dam tbv f
## 1 1 0 1.11156163 0.277890408 0 0 1.453847029 0.0000
## 2 1 0 -0.64447104 0.057267161 0 0 -0.476751331 0.0000
## 3 1 0 0.52824327 0.132060817 0 0 -0.103524785 0.0000
## 4 1 0 -0.47271044 -0.122813103 0 0 0.415099031 0.0000
## 5 2 0 -0.87693550 -0.022494369 0 0 0.505299538 0.0000
## 6 2 0 2.03944069 0.509860174 0 0 0.715069469 0.0000
## 7 2 0 0.27240472 0.085111101 0 0 0.037042896 0.0000
## 8 2 0 1.73819123 0.434547807 0 0 0.598512012 0.0000
## 9 1 0 -0.95631884 -0.113950043 4 7 -0.036636447 0.0000
## 10 1 0 -0.56405325 -0.142853667 4 5 0.540929392 0.0000
## 11 1 0 0.94074343 0.190787354 2 5 0.617271077 0.0000
## 12 1 0 0.78768342 0.099294487 4 5 0.565677997 0.0000
## 13 2 0 -0.16246309 -0.085483644 4 5 0.692375218 0.0000
## 14 2 0 0.88635799 0.156106723 2 7 0.134215495 0.0000
## 15 2 0 -1.52011541 -0.061092742 2 7 0.345684130 0.0000
## 16 2 0 0.76625273 0.236332346 2 7 0.037174999 0.0000
## 17 1 0 0.05761065 0.111669857 9 14 0.779866416 0.1250
## 18 1 0 0.89620687 0.175162167 12 15 0.722394743 0.0000
## 19 1 0 0.20228744 0.124068561 11 14 0.088293309 0.1250
## 20 1 0 0.56443435 0.130563157 11 15 0.729130167 0.1250
## 21 2 0 0.19934688 0.292357774 12 16 0.833518634 0.0000
## 22 2 0 1.31067520 0.408833979 11 16 0.603970399 0.1250
## 23 2 0 -0.84060325 0.001641232 12 16 0.201733473 0.0000
## 24 2 0 -0.07369115 0.058652094 11 14 0.009346568 0.1250
## 25 1 0 0.88799838 0.348391271 20 22 0.891376060 0.2500
## 26 1 0 1.94193172 0.437137856 17 21 0.609946093 0.1250
## 27 1 0 -0.30129316 0.008284098 17 23 0.250287505 0.1250
## 28 1 0 0.15207180 0.200626496 19 21 0.204757260 0.1250
## 29 2 0 1.21734653 0.374272337 18 21 0.807455395 0.1875
## 30 2 0 -0.50397995 0.114779328 20 21 0.907517735 0.1250
## 31 2 0 -0.71120974 -0.010784954 19 24 -0.095289601 0.3125
## 32 2 0 -0.35996282 0.052465245 18 24 -0.477802959 0.1250
The function add_ma_gen() is a modification of add-gen to produce the next generation using maximum avoidance (minimum coancestry) mating. The function call to add_ma_gen() is identical to add_gen(). Function add_ma_gen() calls a_mat() to obtain the numerator relationships for the parents. Prior to printing the updated population data frame it prints out the group coancestry, the average inbreeding coefficient achieved and the estimate of alpha - which is expected to be negative!
my.df$noff[25]=2; my.df$noff[26]=2; my.df$noff[27]=2; my.df$noff[28]=2
my.df$noff[29]=2; my.df$noff[30]=2; my.df$noff[31]=2; my.df$noff[32]=2
my.df <- add_ma_gen(my.df,0.25)
## [1] "Creating 8 offspring with maximum avoidance"
## [1] "Group coancestry 0.25048828125 and average offspring F 0.1640625 with alpha -0.115309446254072"
## id sex noff ptype ebv sire dam tbv f
## 1 1 0 1.11156163 0.277890408 0 0 1.453847029 0.00000
## 2 1 0 -0.64447104 0.043141635 0 0 -0.476751331 0.00000
## 3 1 0 0.52824327 0.132060817 0 0 -0.103524785 0.00000
## 4 1 0 -0.47271044 -0.129088743 0 0 0.415099031 0.00000
## 5 2 0 -0.87693550 -0.026334029 0 0 0.505299538 0.00000
## 6 2 0 2.03944069 0.509860174 0 0 0.715069469 0.00000
## 7 2 0 0.27240472 0.068549595 0 0 0.037042896 0.00000
## 8 2 0 1.73819123 0.434547807 0 0 0.598512012 0.00000
## 9 1 0 -0.95631884 -0.134596734 4 7 -0.036636447 0.00000
## 10 1 0 -0.56405325 -0.147188796 4 5 0.540929392 0.00000
## 11 1 0 0.94074343 0.175824616 2 5 0.617271077 0.00000
## 12 1 0 0.78768342 0.093652392 4 5 0.565677997 0.00000
## 13 2 0 -0.16246309 -0.089818773 4 5 0.692375218 0.00000
## 14 2 0 0.88635799 0.096030002 2 7 0.134215495 0.00000
## 15 2 0 -1.52011541 -0.054600128 2 7 0.345684130 0.00000
## 16 2 0 0.76625273 0.231032018 2 7 0.037174999 0.00000
## 17 1 0 0.05761065 0.045969684 9 14 0.779866416 0.12500
## 18 1 0 0.89620687 0.190536073 12 15 0.722394743 0.00000
## 19 1 0 0.20228744 0.045032698 11 14 0.088293309 0.12500
## 20 1 0 0.56443435 0.157215912 11 15 0.729130167 0.12500
## 21 2 0 0.19934688 0.270309900 12 16 0.833518634 0.00000
## 22 2 0 1.31067520 0.435020280 11 16 0.603970399 0.12500
## 23 2 0 -0.84060325 -0.005251553 12 16 0.201733473 0.00000
## 24 2 0 -0.07369115 -0.021505021 11 14 0.009346568 0.12500
## 25 1 0 0.88799838 0.446004679 20 22 0.891376060 0.25000
## 26 1 0 1.94193172 0.330042351 17 21 0.609946093 0.12500
## 27 1 0 -0.30129316 -0.032831826 17 23 0.250287505 0.12500
## 28 1 0 0.15207180 0.106468148 19 21 0.204757260 0.12500
## 29 2 0 1.21734653 0.455097781 18 21 0.807455395 0.18750
## 30 2 0 -0.50397995 0.107046253 20 21 0.907517735 0.12500
## 31 2 0 -0.71120974 -0.145378089 19 24 -0.095289601 0.31250
## 32 2 0 -0.35996282 -0.025995577 18 24 -0.477802959 0.12500
## 33 1 0 -1.32442290 -0.133447610 28 32 -0.372036380 0.18750
## 34 1 0 0.89431519 0.146206411 27 30 0.478694586 0.15625
## 35 1 0 0.01095697 0.399906730 25 29 0.756313986 0.15625
## 36 1 0 0.54940756 0.105039902 28 32 0.015727326 0.18750
## 37 2 0 -0.06454976 0.074258180 26 31 0.123151700 0.15625
## 38 2 0 2.21429761 0.653747817 25 29 1.091035242 0.15625
## 39 2 0 -0.98500995 -0.092980426 27 30 0.748245575 0.15625
## 40 2 0 -0.90747056 -0.022852511 26 31 0.638566927 0.15625
At any time the population data frame my.df can be saved and reloaded.
save(my.df,file="my_df.Rda")
# ... and later ...
load("my_df.Rda")
jwoollia/DenHaag documentation built on May 20, 2019, 6:27 a.m.
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DenHaag is a package of functions for simulating populations using various options for managing inbreeding. The package provides a function for establising base population new_pop(), and the simplest option is then to add generations with random pairing of males and females using the function add_gen(). Both functions output a data frame containing data on pedigree ("id", "sire", "dam" and inbreeding coefficient "f"), a phenotype ("ptype") with true breeding value ("tbv"), an estimate of breeding value ("ebv"). The populations are assumed to have two sexes so the data frame also contains data on sex ("sex"), and also a data column called "noff" which is set by the user to determine the number of offspring each parent has in the next generation. The EBV is calculated within add_gen() using a call to blup(), which in turn makes a call to function a_inv().
The base is set up by calling new_pop(nn,hh) where nn is the number of individuals and hh is the heritability of the trait. The functions in the package assume an equal number of males and females in the population, and so new_pop() checks that the number in the base is an even positive integer and flags an error if not. It also flags an error if the heritability is outside the open interval (0,1).
library("DenHaag", lib.loc="~/R/win-library/3.3")
# new_pop() accepts numbers ...
my.df <- new_pop(7,0.25)
## [1] "Census size is expected to be even not odd! 7!"
my.df <- new_pop(8,1.25)
## [1] "Heritability out of bounds! 1.25!"
# ... or assigned variables
my.nn <- 8; my.hh <- 0.25
my.df <- new_pop(my.nn,my.hh)
## [1] "Creating 8 individuals for base generation"
## id sex noff ptype ebv sire dam tbv f
## 1 1 0 1.1115616 0.27789041 0 0 1.4538470 0
## 2 1 0 -0.6444710 -0.16111776 0 0 -0.4767513 0
## 3 1 0 0.5282433 0.13206082 0 0 -0.1035248 0
## 4 1 0 -0.4727104 -0.11817761 0 0 0.4150990 0
## 5 2 0 -0.8769355 -0.21923388 0 0 0.5052995 0
## 6 2 0 2.0394407 0.50986017 0 0 0.7150695 0
## 7 2 0 0.2724047 0.06810118 0 0 0.0370429 0
## 8 2 0 1.7381912 0.43454781 0 0 0.5985120 0
Note "noff" is set to 0. The user selects which individuals become parents and the number of offspring for each parent by assigning "noff" values.
my.df$noff[2]=4; my.df$noff[4]=4; my.df$noff[5]=4; my.df$noff[7]=4
The next generation is then formed by calling add_gen(my.df,my.hh). The output from add_gen() is an extended data frame. The function add_gen() detects errors in setting "noff" such as total offspring not being a even positive integer, or numbers of offspring from males not being equal to the number of offsring from females.
my.df$noff[2]=4; my.df$noff[4]=3; my.df$noff[5]=4; my.df$noff[7]=3
my.df <- add_gen(my.df,my.hh)
## [1] "Offspring numbers are expected to be even not odd! 7!"
my.df$noff[2]=4; my.df$noff[4]=4; my.df$noff[5]=4; my.df$noff[7]=3
my.df <- add_gen(my.df,my.hh)
## [1] "Numbers of male and female parents unequal in matings!"
# the heritability can be entered as a number
my.df$noff[2]=4; my.df$noff[4]=4; my.df$noff[5]=4; my.df$noff[7]=4
my.df <- add_gen(my.df,0.25)
## [1] "Creating 8 offspring"
## id sex noff ptype ebv sire dam tbv f
## 1 1 0 1.1115616 0.277890408 0 0 1.45384703 0
## 2 1 0 -0.6444710 -0.034343695 0 0 -0.47675133 0
## 3 1 0 0.5282433 0.132060817 0 0 -0.10352478 0
## 4 1 0 -0.4727104 -0.167030941 0 0 0.41509903 0
## 5 2 0 -0.8769355 -0.068519477 0 0 0.50529954 0
## 6 2 0 2.0394407 0.509860174 0 0 0.71506947 0
## 7 2 0 0.2724047 -0.004692485 0 0 0.03704290 0
## 8 2 0 1.7381912 0.434547807 0 0 0.59851201 0
## 9 1 0 -0.9563188 -0.210212732 4 7 -0.03663645 0
## 10 1 0 -0.5640533 -0.181529215 4 5 0.54092939 0
## 11 1 0 0.9407434 0.090307702 2 5 0.61727108 0
## 12 1 0 0.7876834 0.011576024 4 5 0.56567800 0
## 13 2 0 -0.1624631 -0.124159193 4 5 0.69237522 0
## 14 2 0 0.8863580 0.109892779 2 7 0.13421549 0
## 15 2 0 -1.5201154 -0.233889136 2 7 0.34568413 0
## 16 2 0 0.7662527 0.092734885 2 7 0.03717500 0
Note that on output my.df has "noff" set to 0. Successive generations are then produced by repeated assigment of "noff" and calls to add_gen().
Recommended contributions can be obtained for achieving a target group coancestry by setting a group coancestry, say my.gc, followed by a call to oc_sel(my.df,my.tp,my.tc,my.gc). The parameter my.tp is a number which gives the number of eligible candidates for selection, which are assumed to be the most recent individuals. The parameter my.tc is the total number of offspring required in the next generation, which is only used to scale the optimum contributions to a projected number of offspring - although these projections are not integer! The function oc_sel() calls a_mat() to produce the numerator relationships among the candidates. The function prints a table listing selected parents and contributions but the function only returns "TRUE" or "FALSE" indicating the success of the algorithm. The user can then set "noff" guided by the recommendations.
my.gc <- 0.2
oc_sel(my.df,8,8,my.gc)
## [1] "Recommendations for 8 offspring from the most recent 8 parents"
## id sex ebv c noff
## 11 1 0.09030770 0.221437169 3.54299471
## 12 1 0.01157602 0.278562831 4.45700529
## 13 2 -0.12415919 0.001382103 0.02211365
## 14 2 0.10989278 0.267062447 4.27299916
## 16 2 0.09273488 0.231555450 3.70488719
## [1] TRUE
# NOTE due to authors inexperience of 'SWEAVE", the following may not agree with
# the recommendations!
my.df$noff[9]=1; my.df$noff[11]=4; my.df$noff[12]=3
my.df$noff[14]=3; my.df$noff[15]=2; my.df$noff[16]=3
my.df <- add_gen(my.df,0.25)
## [1] "Creating 8 offspring"
## id sex noff ptype ebv sire dam tbv f
## 1 1 0 1.11156163 0.277890408 0 0 1.453847029 0.000
## 2 1 0 -0.64447104 0.063665337 0 0 -0.476751331 0.000
## 3 1 0 0.52824327 0.132060817 0 0 -0.103524785 0.000
## 4 1 0 -0.47271044 -0.170069089 0 0 0.415099031 0.000
## 5 2 0 -0.87693550 -0.019282829 0 0 0.505299538 0.000
## 6 2 0 2.03944069 0.509860174 0 0 0.715069469 0.000
## 7 2 0 0.27240472 0.041041750 0 0 0.037042896 0.000
## 8 2 0 1.73819123 0.434547807 0 0 0.598512012 0.000
## 9 1 0 -0.95631884 -0.187532041 4 7 -0.036636447 0.000
## 10 1 0 -0.56405325 -0.161729858 4 5 0.540929392 0.000
## 11 1 0 0.94074343 0.234962920 2 5 0.617271077 0.000
## 12 1 0 0.78768342 0.035891547 4 5 0.565677997 0.000
## 13 2 0 -0.16246309 -0.104359835 4 5 0.692375218 0.000
## 14 2 0 0.88635799 0.159595980 2 7 0.134215495 0.000
## 15 2 0 -1.52011541 -0.085890714 2 7 0.345684130 0.000
## 16 2 0 0.76625273 0.170294494 2 7 0.037174999 0.000
## 17 1 0 0.05761065 -0.003742504 9 14 0.779866416 0.125
## 18 1 0 0.89620687 0.106601338 12 15 0.722394743 0.000
## 19 1 0 0.20228744 0.197994877 11 14 0.088293309 0.125
## 20 1 0 0.56443435 0.144521567 11 15 0.729130167 0.125
## 21 2 0 0.19934688 0.116843571 12 16 0.833518634 0.000
## 22 2 0 1.31067520 0.360921063 11 16 0.603970399 0.125
## 23 2 0 -0.84060325 -0.031720732 12 16 0.201733473 0.000
## 24 2 0 -0.07369115 0.158569364 11 14 0.009346568 0.125
An alternative to setting a target group coancestry is to set a cost of inbreeding and use penalised contributions. Analagous to oc_sel(), recommended contributions for penalised contributions can be obtained by using the function cost_f(my.df,my.tp,my.tc,my.cost) where the first three arguments are as with oc_sel() and the final argument is set by the user.
my.cost <- 1000
cost_f(my.df,8,8,my.cost)
## [1] "Recommendations for 8 offspring from the most recent 8 parents"
## [1] "Recommendations have group coancestry 0.213942324423893"
## id sex ebv c noff
## 17 1 -0.003742504 0.16981576 2.717052
## 18 1 0.106601338 0.16029243 2.564679
## 19 1 0.197994877 0.07376138 1.180182
## 20 1 0.144521567 0.09613043 1.538087
## 21 2 0.116843571 0.15385620 2.461699
## 22 2 0.360921063 0.08990035 1.438406
## 23 2 -0.031720732 0.15370763 2.459322
## 24 2 0.158569364 0.10253581 1.640573
## [1] TRUE
# NOTE due to authors inexperience of 'SWEAVE", the following may not agree with
# the recommendations!
my.df$noff[17]=2; my.df$noff[18]=2; my.df$noff[19]=2; my.df$noff[20]=2
my.df$noff[21]=4; my.df$noff[22]=1; my.df$noff[23]=1; my.df$noff[24]=2
my.df <- add_gen(my.df,0.25)
## [1] "Creating 8 offspring"
## id sex noff ptype ebv sire dam tbv f
## 1 1 0 1.11156163 0.277890408 0 0 1.453847029 0.0000
## 2 1 0 -0.64447104 0.057267161 0 0 -0.476751331 0.0000
## 3 1 0 0.52824327 0.132060817 0 0 -0.103524785 0.0000
## 4 1 0 -0.47271044 -0.122813103 0 0 0.415099031 0.0000
## 5 2 0 -0.87693550 -0.022494369 0 0 0.505299538 0.0000
## 6 2 0 2.03944069 0.509860174 0 0 0.715069469 0.0000
## 7 2 0 0.27240472 0.085111101 0 0 0.037042896 0.0000
## 8 2 0 1.73819123 0.434547807 0 0 0.598512012 0.0000
## 9 1 0 -0.95631884 -0.113950043 4 7 -0.036636447 0.0000
## 10 1 0 -0.56405325 -0.142853667 4 5 0.540929392 0.0000
## 11 1 0 0.94074343 0.190787354 2 5 0.617271077 0.0000
## 12 1 0 0.78768342 0.099294487 4 5 0.565677997 0.0000
## 13 2 0 -0.16246309 -0.085483644 4 5 0.692375218 0.0000
## 14 2 0 0.88635799 0.156106723 2 7 0.134215495 0.0000
## 15 2 0 -1.52011541 -0.061092742 2 7 0.345684130 0.0000
## 16 2 0 0.76625273 0.236332346 2 7 0.037174999 0.0000
## 17 1 0 0.05761065 0.111669857 9 14 0.779866416 0.1250
## 18 1 0 0.89620687 0.175162167 12 15 0.722394743 0.0000
## 19 1 0 0.20228744 0.124068561 11 14 0.088293309 0.1250
## 20 1 0 0.56443435 0.130563157 11 15 0.729130167 0.1250
## 21 2 0 0.19934688 0.292357774 12 16 0.833518634 0.0000
## 22 2 0 1.31067520 0.408833979 11 16 0.603970399 0.1250
## 23 2 0 -0.84060325 0.001641232 12 16 0.201733473 0.0000
## 24 2 0 -0.07369115 0.058652094 11 14 0.009346568 0.1250
## 25 1 0 0.88799838 0.348391271 20 22 0.891376060 0.2500
## 26 1 0 1.94193172 0.437137856 17 21 0.609946093 0.1250
## 27 1 0 -0.30129316 0.008284098 17 23 0.250287505 0.1250
## 28 1 0 0.15207180 0.200626496 19 21 0.204757260 0.1250
## 29 2 0 1.21734653 0.374272337 18 21 0.807455395 0.1875
## 30 2 0 -0.50397995 0.114779328 20 21 0.907517735 0.1250
## 31 2 0 -0.71120974 -0.010784954 19 24 -0.095289601 0.3125
## 32 2 0 -0.35996282 0.052465245 18 24 -0.477802959 0.1250
The function add_ma_gen() is a modification of add-gen to produce the next generation using maximum avoidance (minimum coancestry) mating. The function call to add_ma_gen() is identical to add_gen(). Function add_ma_gen() calls a_mat() to obtain the numerator relationships for the parents. Prior to printing the updated population data frame it prints out the group coancestry, the average inbreeding coefficient achieved and the estimate of alpha - which is expected to be negative!
my.df$noff[25]=2; my.df$noff[26]=2; my.df$noff[27]=2; my.df$noff[28]=2
my.df$noff[29]=2; my.df$noff[30]=2; my.df$noff[31]=2; my.df$noff[32]=2
my.df <- add_ma_gen(my.df,0.25)
## [1] "Creating 8 offspring with maximum avoidance"
## [1] "Group coancestry 0.25048828125 and average offspring F 0.1640625 with alpha -0.115309446254072"
## id sex noff ptype ebv sire dam tbv f
## 1 1 0 1.11156163 0.277890408 0 0 1.453847029 0.00000
## 2 1 0 -0.64447104 0.043141635 0 0 -0.476751331 0.00000
## 3 1 0 0.52824327 0.132060817 0 0 -0.103524785 0.00000
## 4 1 0 -0.47271044 -0.129088743 0 0 0.415099031 0.00000
## 5 2 0 -0.87693550 -0.026334029 0 0 0.505299538 0.00000
## 6 2 0 2.03944069 0.509860174 0 0 0.715069469 0.00000
## 7 2 0 0.27240472 0.068549595 0 0 0.037042896 0.00000
## 8 2 0 1.73819123 0.434547807 0 0 0.598512012 0.00000
## 9 1 0 -0.95631884 -0.134596734 4 7 -0.036636447 0.00000
## 10 1 0 -0.56405325 -0.147188796 4 5 0.540929392 0.00000
## 11 1 0 0.94074343 0.175824616 2 5 0.617271077 0.00000
## 12 1 0 0.78768342 0.093652392 4 5 0.565677997 0.00000
## 13 2 0 -0.16246309 -0.089818773 4 5 0.692375218 0.00000
## 14 2 0 0.88635799 0.096030002 2 7 0.134215495 0.00000
## 15 2 0 -1.52011541 -0.054600128 2 7 0.345684130 0.00000
## 16 2 0 0.76625273 0.231032018 2 7 0.037174999 0.00000
## 17 1 0 0.05761065 0.045969684 9 14 0.779866416 0.12500
## 18 1 0 0.89620687 0.190536073 12 15 0.722394743 0.00000
## 19 1 0 0.20228744 0.045032698 11 14 0.088293309 0.12500
## 20 1 0 0.56443435 0.157215912 11 15 0.729130167 0.12500
## 21 2 0 0.19934688 0.270309900 12 16 0.833518634 0.00000
## 22 2 0 1.31067520 0.435020280 11 16 0.603970399 0.12500
## 23 2 0 -0.84060325 -0.005251553 12 16 0.201733473 0.00000
## 24 2 0 -0.07369115 -0.021505021 11 14 0.009346568 0.12500
## 25 1 0 0.88799838 0.446004679 20 22 0.891376060 0.25000
## 26 1 0 1.94193172 0.330042351 17 21 0.609946093 0.12500
## 27 1 0 -0.30129316 -0.032831826 17 23 0.250287505 0.12500
## 28 1 0 0.15207180 0.106468148 19 21 0.204757260 0.12500
## 29 2 0 1.21734653 0.455097781 18 21 0.807455395 0.18750
## 30 2 0 -0.50397995 0.107046253 20 21 0.907517735 0.12500
## 31 2 0 -0.71120974 -0.145378089 19 24 -0.095289601 0.31250
## 32 2 0 -0.35996282 -0.025995577 18 24 -0.477802959 0.12500
## 33 1 0 -1.32442290 -0.133447610 28 32 -0.372036380 0.18750
## 34 1 0 0.89431519 0.146206411 27 30 0.478694586 0.15625
## 35 1 0 0.01095697 0.399906730 25 29 0.756313986 0.15625
## 36 1 0 0.54940756 0.105039902 28 32 0.015727326 0.18750
## 37 2 0 -0.06454976 0.074258180 26 31 0.123151700 0.15625
## 38 2 0 2.21429761 0.653747817 25 29 1.091035242 0.15625
## 39 2 0 -0.98500995 -0.092980426 27 30 0.748245575 0.15625
## 40 2 0 -0.90747056 -0.022852511 26 31 0.638566927 0.15625
At any time the population data frame my.df can be saved and reloaded.
save(my.df,file="my_df.Rda")
# ... and later ...
load("my_df.Rda")
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