Plot relationship matrices against each other (off diagonal)
Diagnonals
summary(diag(Gibd))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 1 1 1 1 1
summary(diag(G))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.8357 0.9560 0.9859 0.9917 1.0200 1.2200
cont_struc<-rbind(
c("Finisher_Post_Front_ls",y~Sex+Rep+Finisher_Pre_Front_ls+Finisher_Pre_Obs:Finisher_Post_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Post_Middle_ls",y~Sex+Rep+Finisher_Pre_Middle_ls+Finisher_Pre_Obs:Finisher_Post_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Post_Rear_ls",y~Sex+Rep+Finisher_Pre_Rear_ls+Finisher_Pre_Obs:Finisher_Post_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Post_Total_ls",y~Sex+Rep+Finisher_Pre_Total_ls+Finisher_Pre_Obs:Finisher_Post_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Stable_Front_ls",y~Sex+Rep+Finisher_Stable_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Stable_Middle_ls",y~Sex+Rep+Finisher_Stable_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Stable_Rear_ls",y~Sex+Rep+Finisher_Stable_Obs+Fin_Wt,~Finisher_Pen),
c("Finisher_Stable_Total_ls",y~Sex+Rep+Finisher_Stable_Obs+Fin_Wt,~Finisher_Pen),
c("Nursery_Post_Front_ls",y~Sex+Rep+Nursery_Pre_Front_ls+Nursery_Pre_Obs:Nursery_Post_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Post_Middle_ls",y~Sex+Rep+Nursery_Pre_Middle_ls+Nursery_Pre_Obs:Nursery_Post_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Post_Rear_ls",y~Sex+Rep+Nursery_Pre_Rear_ls+Nursery_Pre_Obs:Nursery_Post_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Post_Total_ls",y~Sex+Rep+Nursery_Pre_Total_ls+Nursery_Pre_Obs:Nursery_Post_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Stable_Front_ls",y~Sex+Rep+Nursery_Stable_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Stable_Middle_ls",y~Sex+Rep+Nursery_Stable_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Stable_Rear_ls",y~Sex+Rep+Nursery_Stable_Obs+Nurs_Wt,~Nursery_Pen),
c("Nursery_Stable_Total_ls",y~Sex+Rep+Nursery_Stable_Obs+Nurs_Wt,~Nursery_Pen),
c("Sow_Post_Front_ls",y~Rep+Sow_Pre_Front_ls+Sow_Pre_Obs:Sow_Post_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Post_Middle_ls",y~Rep+Sow_Pre_Middle_ls+Sow_Pre_Obs:Sow_Post_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Post_Rear_ls",y~Rep+Sow_Pre_Rear_ls+Sow_Pre_Obs:Sow_Post_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Post_Total_ls",y~Rep+Sow_Pre_Total_ls+Sow_Pre_Obs:Sow_Post_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Stable_Front_ls",y~Rep+Sow_Stable_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Stable_Middle_ls",y~Rep+Sow_Stable_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Stable_Rear_ls",y~Rep+Sow_Stable_Obs+Sow_Wt,~Sow_Pen),
c("Sow_Stable_Total_ls",y~Rep+Sow_Stable_Obs+Sow_Wt,~Sow_Pen)
)
Run gblup for lesion scores
for (i in 1:nrow(cont_struc)){
print(cont_struc[i,][[1]])
gb<-gblup(cont_struc[i,][[1]],lsdata,
c(cont_struc[i,][[2]],cont_struc[i,][[3]]),
G,pos=c(T,T,T))
print(varcomp(gb))
gbib<-gblup(cont_struc[i,][[1]],lsdata,
c(cont_struc[i,][[2]],cont_struc[i,][[3]]),
Gibd,pos=c(T,T,T))
print(varcomp(gbib))
}
## [1] "Finisher_Post_Front_ls"
## Estimate StdError prop.var se
## G 0.13495722 0.02603022 0.31949855 0.05514736
## Finisher_Pen 0.02495741 0.00892020 0.05908433 0.02068122
## In 0.26248858 0.01862037 0.62141711 0.06361812
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
## Estimate StdError prop.var se
## G 3.846069e-01 0.072661876 9.399600e-01 0.26296529
## Finisher_Pen 2.456678e-02 0.008846344 6.003998e-02 0.02493541
## In 2.061154e-09 0.061412148 5.037356e-09 0.15008820
## [1] "Finisher_Post_Middle_ls"
## Estimate StdError prop.var se
## G 0.05529746 0.01601668 0.1531418 0.04252140
## Finisher_Pen 0.05283027 0.01298733 0.1463091 0.03250257
## In 0.25295895 0.01554128 0.7005491 0.05804723
## Estimate StdError prop.var se
## G 0.18357894 0.05000614 0.5102177 0.16079661
## Finisher_Pen 0.05418709 0.01321082 0.1506012 0.04233243
## In 0.12203908 0.04458721 0.3391811 0.14252504
## [1] "Finisher_Post_Rear_ls"
## Estimate StdError prop.var se
## G 0.06884347 0.01893582 0.1553651 0.04108823
## Finisher_Pen 0.09203077 0.02020550 0.2076939 0.03861665
## In 0.28223347 0.01764812 0.6369410 0.05574232
## Estimate StdError prop.var se
## G 0.21519371 0.05779152 0.4871947 0.14994048
## Finisher_Pen 0.09300626 0.02039633 0.2105645 0.05223372
## In 0.13349968 0.05139888 0.3022409 0.13117706
## [1] "Finisher_Post_Total_ls"
## Estimate StdError prop.var se
## G 0.08386041 0.017570578 0.2650744 0.05095720
## Finisher_Pen 0.03826893 0.009887334 0.1209642 0.02916262
## In 0.19423625 0.013356934 0.6139614 0.06002648
## Estimate StdError prop.var se
## G 0.25911188 0.05131198 0.82956105 0.22885916
## Finisher_Pen 0.03944386 0.01008187 0.12628172 0.03959284
## In 0.01379243 0.04393673 0.04415724 0.14183535
## [1] "Finisher_Stable_Front_ls"
## Estimate StdError prop.var se
## G 0.09455502 0.02539940 0.1776062 0.04527629
## Finisher_Pen 0.06278573 0.01667261 0.1179327 0.02912678
## In 0.37504510 0.02349282 0.7044611 0.05978193
## Estimate StdError prop.var se
## G 0.33199486 0.08065139 0.6257816 0.18715278
## Finisher_Pen 0.06028684 0.01626598 0.1136355 0.03595247
## In 0.13824664 0.07074276 0.2605830 0.14679961
## [1] "Finisher_Stable_Middle_ls"
## Estimate StdError prop.var se
## G 0.07544427 0.02473285 0.13549504 0.04276909
## Finisher_Pen 0.03441747 0.01263057 0.06181247 0.02203087
## In 0.44694290 0.02643619 0.80269249 0.06053509
## Estimate StdError prop.var se
## G 0.2157956 0.07493562 0.3885966 0.14706912
## Finisher_Pen 0.0334661 0.01255035 0.0602645 0.02429116
## In 0.3060587 0.06925557 0.5511389 0.16260214
## [1] "Finisher_Stable_Rear_ls"
## Estimate StdError prop.var se
## G 0.05034022 0.01899732 0.1026772 0.03778298
## Finisher_Pen 0.06470916 0.01663071 0.1319850 0.03095487
## In 0.37522720 0.02174951 0.7653378 0.05650681
## Estimate StdError prop.var se
## G 0.17699609 0.06202155 0.3608630 0.13627996
## Finisher_Pen 0.06501857 0.01670007 0.1325611 0.03773519
## In 0.24846539 0.05725500 0.5065759 0.14813433
## [1] "Finisher_Stable_Total_ls"
## Estimate StdError prop.var se
## G 0.08449161 0.02317520 0.1699988 0.04439483
## Finisher_Pen 0.06389535 0.01641962 0.1285587 0.03041113
## In 0.34862610 0.02172225 0.7014425 0.05913705
## Estimate StdError prop.var se
## G 0.26750898 0.07122085 0.5404780 0.16905027
## Finisher_Pen 0.06258477 0.01624809 0.1264469 0.03799844
## In 0.16485512 0.06330822 0.3330751 0.14657991
## [1] "Nursery_Post_Front_ls"
## Estimate StdError prop.var se
## G 0.14012006 0.03081159 0.25772861 0.05200779
## Nursery_Pen 0.03575283 0.01233925 0.06576166 0.02223680
## In 0.36779999 0.02448852 0.67650973 0.06379739
## Estimate StdError prop.var se
## G 0.39935266 0.08895619 0.74077338 0.21693107
## Nursery_Pen 0.03646796 0.01260987 0.06764572 0.02678562
## In 0.10328172 0.07679727 0.19158091 0.15159837
## [1] "Nursery_Post_Middle_ls"
## Estimate StdError prop.var se
## G 0.11506607 0.02795631 0.2114905 0.04808309
## Nursery_Pen 0.06363332 0.01604849 0.1169575 0.02788374
## In 0.36537269 0.02367591 0.6715520 0.06044358
## Estimate StdError prop.var se
## G 0.2869638 0.07825425 0.5356783 0.17091318
## Nursery_Pen 0.0598485 0.01570741 0.1117198 0.03453541
## In 0.1888895 0.06937909 0.3526019 0.15029709
## [1] "Nursery_Post_Rear_ls"
## Estimate StdError prop.var se
## G 0.11249825 0.02623556 0.2147921 0.04690876
## Nursery_Pen 0.08972414 0.01903602 0.1713097 0.03281490
## In 0.32153166 0.02131096 0.6138982 0.05715196
## Estimate StdError prop.var se
## G 0.23766762 0.06908129 0.4592675 0.1503640
## Nursery_Pen 0.09293383 0.01967297 0.1795848 0.0453883
## In 0.18689132 0.06170919 0.3611477 0.1390443
## [1] "Nursery_Post_Total_ls"
## Estimate StdError prop.var se
## G 0.1205613 0.02590443 0.26144945 0.05150601
## Nursery_Pen 0.0457014 0.01239390 0.09910813 0.02582160
## In 0.2948640 0.01998953 0.63944242 0.06159193
## Estimate StdError prop.var se
## G 0.31724640 0.07255620 0.6976502 0.20449045
## Nursery_Pen 0.04569579 0.01255960 0.1004887 0.03318297
## In 0.09179344 0.06279093 0.2018611 0.14766631
## [1] "Nursery_Stable_Front_ls"
## Estimate StdError prop.var se
## G 0.13342929 0.02892955 0.2516754 0.05023291
## Nursery_Pen 0.05957848 0.01502508 0.1123774 0.02696069
## In 0.33715646 0.02259866 0.6359472 0.06021670
## Estimate StdError prop.var se
## G 0.37466711 0.08345448 0.7062646 0.20260194
## Nursery_Pen 0.06117615 0.01548116 0.1153198 0.03574414
## In 0.09464790 0.07201177 0.1784156 0.14358400
## [1] "Nursery_Stable_Middle_ls"
## Estimate StdError prop.var se
## G 0.1694685 0.03658176 0.2504168 0.04981390
## Nursery_Pen 0.0852717 0.02034489 0.1260026 0.02830447
## In 0.4220054 0.02840634 0.6235806 0.05931397
## Estimate StdError prop.var se
## G 0.39013917 0.09853787 0.5833541 0.17697829
## Nursery_Pen 0.08398999 0.02053027 0.1255857 0.03698555
## In 0.19465701 0.08660451 0.2910602 0.14501161
## [1] "Nursery_Stable_Rear_ls"
## Estimate StdError prop.var se
## G 0.10198776 0.02666872 0.1805120 0.04473628
## Nursery_Pen 0.08243433 0.01867339 0.1459037 0.03042458
## In 0.38056939 0.02393627 0.6735843 0.05790299
## Estimate StdError prop.var se
## G 0.29431553 0.07926556 0.5259274 0.16495980
## Nursery_Pen 0.07675493 0.01804744 0.1371573 0.03862737
## In 0.18854204 0.07027441 0.3369153 0.14432032
## [1] "Nursery_Stable_Total_ls"
## Estimate StdError prop.var se
## G 0.18900894 0.03859557 0.2710296 0.05059867
## Nursery_Pen 0.09582746 0.02186155 0.1374119 0.02929678
## In 0.41253749 0.02852634 0.5915586 0.05829610
## Estimate StdError prop.var se
## G 0.48170566 0.10703272 0.6954501 0.19793818
## Nursery_Pen 0.09490962 0.02211883 0.1370233 0.03979613
## In 0.11603778 0.09240392 0.1675266 0.14039130
## [1] "Sow_Post_Front_ls"
## Estimate StdError prop.var se
## G 0.18216408 0.04606445 0.39455808 0.09186994
## Sow_Pen 0.01342438 0.01165887 0.02907651 0.02505188
## In 0.26610296 0.03303881 0.57636541 0.10179882
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
## Estimate StdError prop.var se
## G 4.450338e-01 1.251235e-15 9.631985e-01 1.261749e-08
## Sow_Pen 1.700365e-02 4.651148e-16 3.680145e-02 4.820825e-10
## In 5.328510e-11 6.052496e-09 1.153264e-10 1.309958e-08
## [1] "Sow_Post_Middle_ls"
## Estimate StdError prop.var se
## G 0.13350574 0.04217018 0.28361360 0.08451652
## Sow_Pen 0.01528884 0.01280856 0.03247893 0.02691903
## In 0.32193651 0.03506204 0.68390747 0.10507261
## Estimate StdError prop.var se
## G 0.35793183 0.12423552 0.76318529 0.36126785
## Sow_Pen 0.01447868 0.01279872 0.03087156 0.02893679
## In 0.09658678 0.11140722 0.20594315 0.25262953
## [1] "Sow_Post_Rear_ls"
## Estimate StdError prop.var se
## G 0.12871513 0.04169870 0.2558732 0.07892359
## Sow_Pen 0.05581951 0.02330404 0.1109638 0.04350077
## In 0.31850804 0.03480933 0.6331631 0.09688989
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
## Estimate StdError prop.var se
## G 4.505476e-01 0.1329202 8.904008e-01 0.3880137
## Sow_Pen 5.545766e-02 0.0233641 1.095990e-01 0.0566892
## In 1.293862e-07 0.1170224 2.557013e-07 0.2312670
## [1] "Sow_Post_Total_ls"
## Estimate StdError prop.var se
## G 0.13679494 0.035716597 0.37567499 0.09068209
## Sow_Pen 0.01219085 0.009656551 0.03347928 0.02625973
## In 0.21514529 0.026170859 0.59084573 0.10220867
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
## Estimate StdError prop.var se
## G 3.481148e-01 0.10141787 9.634774e-01 0.43317338
## Sow_Pen 1.319481e-02 0.01014146 3.651928e-02 0.03060552
## In 1.193892e-06 0.08918042 3.304334e-06 0.24682476
## [1] "Sow_Stable_Front_ls"
## Estimate StdError prop.var se
## G 0.10586982 0.04314596 0.1803979 0.07120330
## Sow_Pen 0.07132743 0.02854838 0.1215391 0.04513525
## In 0.40967111 0.04093395 0.6980630 0.09572826
## Estimate StdError prop.var se
## G 0.24500224 0.12617072 0.4223511 0.24413702
## Sow_Pen 0.06400607 0.02690572 0.1103379 0.05329569
## In 0.27108309 0.11806558 0.4673110 0.25005013
## [1] "Sow_Stable_Middle_ls"
## Estimate StdError prop.var se
## G 0.06617994 0.03620363 0.12636097 0.06765143
## Sow_Pen 0.02970918 0.01765112 0.05672536 0.03281720
## In 0.42784809 0.03939485 0.81691367 0.09913527
## Estimate StdError prop.var se
## G 0.23132110 0.11943016 0.44008801 0.25735725
## Sow_Pen 0.02981048 0.01770455 0.05671439 0.03680056
## In 0.26449305 0.11200817 0.50319760 0.26757177
## [1] "Sow_Stable_Rear_ls"
## Estimate StdError prop.var se
## G 0.04669147 0.02974880 0.10345434 0.06482753
## Sow_Pen 0.02239839 0.01450983 0.04962813 0.03142937
## In 0.38223458 0.03423806 0.84691753 0.09808184
## Estimate StdError prop.var se
## G 0.12921436 0.09448740 0.28622286 0.22104535
## Sow_Pen 0.02205873 0.01447280 0.04886231 0.03407113
## In 0.30017359 0.09111133 0.66491483 0.27973334
## [1] "Sow_Stable_Total_ls"
## Estimate StdError prop.var se
## G 0.08394056 0.03412803 0.1824440 0.07178145
## Sow_Pen 0.05243401 0.02155945 0.1139648 0.04377214
## In 0.32371486 0.03234323 0.7035912 0.09642974
## Estimate StdError prop.var se
## G 0.19620147 0.10009139 0.4304487 0.24747048
## Sow_Pen 0.04693349 0.02034335 0.1029679 0.05114142
## In 0.21267189 0.09358840 0.4665834 0.25214468
Weight gains
gbng<-gblup("nurs_ADG",lswt,
c(y~Sex+Rep,~Nursery_Pen),
G=G,pos=c(T,T,T),start=c(1,1,1))
varcomp(gbng)
## Estimate StdError prop.var se
## G 0.8433749 0.15706771 0.3261432 0.05447699
## Nursery_Pen 0.3698224 0.08236411 0.1430148 0.02991052
## In 1.3727063 0.10521566 0.5308420 0.05842508
gbni<-gblup("nurs_ADG",lswt,
c(y~Sex+Rep,~Nursery_Pen),
G=Gibd,pos=c(T,T,T),start=c(1,1,1))
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
varcomp(gbni)
## Estimate StdError prop.var se
## G 2.1356286434 0.42182200 8.564980e-01 0.23859563
## Nursery_Pen 0.3576764863 0.08152267 1.434468e-01 0.04290061
## In 0.0001376328 0.35682272 5.519789e-05 0.14310562
gbfg<-gblup("fin_ADG",lswt,
c(y~Sex+Rep,~Finisher_Pen),
G=G,pos=c(T,T,T),start=c(1,1,1))
varcomp(gbfg)
## Estimate StdError prop.var se
## G 0.8836395 0.1905000 0.2417446 0.04852180
## Finisher_Pen 0.6667855 0.1492106 0.1824180 0.03592507
## In 2.1048359 0.1464179 0.5758374 0.05706226
gbfi<-gblup("fin_ADG",lswt,
c(y~Sex+Rep,~Finisher_Pen),
G=Gibd,pos=c(T,T,T),start=c(1,1,1))
varcomp(gbfi)
## Estimate StdError prop.var se
## G 2.6738008 0.5601592 0.73290451 0.20164919
## Finisher_Pen 0.6766595 0.1515310 0.18547636 0.05085366
## In 0.2977650 0.4799933 0.08161913 0.13405885
gbtg<-gblup("total_ADG",lswt,
c(y~Sex+Rep,~Finisher_Pen+Nursery_Pen),
G=G,pos=c(T,T,T,T),start=c(1,1,1,1))
varcomp(gbtg)
## Estimate StdError prop.var se
## G 0.3980127 0.08935479 0.18317794 0.04008611
## Finisher_Pen 0.7197828 0.14156833 0.33126664 0.04777854
## Nursery_Pen 0.0927925 0.03712532 0.04270602 0.01706761
## In 0.9622320 0.06984036 0.44284940 0.04831049
gbti<-gblup("total_ADG",lswt,
c(y~Sex+Rep,~Finisher_Pen+Nursery_Pen),
G=Gibd,pos=c(T,T,T,T),start=c(1,1,1,1))
varcomp(gbti)
## Estimate StdError prop.var se
## G 1.11580873 0.25806507 0.51511748 0.14030130
## Finisher_Pen 0.73586637 0.14432588 0.33971560 0.06985131
## Nursery_Pen 0.07170421 0.03473942 0.03310253 0.01677346
## In 0.24274544 0.22285091 0.11206439 0.10605180