ANOVA of Eberhart & Russel’s Model
Usage
er_anova(.data, .y, .rep, .gen, .env)
# Default S3 method
er_anova(.data, .y, .rep, .gen, .env)References
Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.
Author
Muhammad Yaseen (myaseen208@gmail.com)
Kent M. Edkridge (keskridge1@unl.edu)
Examples
data(ge_data)
Yield.er_anova <-
er_anova(
.data = ge_data
, .y = Yield
, .rep = Rep
, .gen = Gen
, .env = Env
)
Yield.er_anova
#> [[1]]
#> Analysis of Variance Table
#>
#> Response: .data[[Y]]
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Env 10 263940496 26394050 68.0551 1.995e-08 ***
#> Rep(Env) 11 4266167 387833 3.9109 1.658e-05 ***
#> Gen 59 71662431 1214617 12.2482 < 2.2e-16 ***
#> Gen:Env 590 201336476 341248 3.4411 < 2.2e-16 ***
#> Residuals 649 64359568 99167
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> $er_anova
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Total 659 234771569 356254
#> Gen 59 2133083 36154 0.4497 0.999871
#> Env + (Gen x Env) 600 232638486 387731
#> Env (linear) 1 131970248 131970248
#> Gen x Env(linear) 59 57257951 970474 12.0722 < 2.2e-16 ***
#> Pooled deviation 540 43410287 80389
#> 013BT034 9 471024 52336 1.0734 0.380296
#> 122557 9 287034 31893 0.6541 0.750685
#> 122559 9 899838 99982 2.0506 0.031866 *
#> 12B.2511 9 800492 88944 1.8242 0.060823 .
#> 12FJ26 9 686360 76262 1.5641 0.122229
#> 14B.1030 9 668473 74275 1.5234 0.135661
#> 14C036 9 1110265 123363 2.5301 0.007408 **
#> 14C040 9 722512 80279 1.6465 0.098562 .
#> 9496 9 495207 55023 1.1285 0.339863
#> AUR0809 9 541979 60220 1.2351 0.270158
#> AUR0810 9 979857 108873 2.2330 0.018531 *
#> AZRC11 9 771590 85732 1.7583 0.072941 .
#> AZRC18 9 589136 65460 1.3426 0.211227
#> AZRC20 9 1050318 116702 2.3935 0.011344 *
#> BARDC1016 9 578318 64258 1.3179 0.223765
#> CT12176 9 1320714 146746 3.0097 0.001576 **
#> DANI16 9 588746 65416 1.3417 0.211669
#> DN111 9 864812 96090 1.9708 0.040168 *
#> DN117 9 557660 61962 1.2708 0.249318
#> DN126 9 575662 63962 1.3119 0.226930
#> FAISALABAD08 9 896257 99584 2.0424 0.032635 *
#> IVI 9 1077549 119728 2.4556 0.009356 **
#> IVII 9 1928363 214263 4.3945 1.353e-05 ***
#> KT325 9 923816 102646 2.1052 0.027138 *
#> KT335 9 959688 106632 2.1870 0.021279 *
#> LOCAL CHECK 9 648921 72102 1.4788 0.151768
#> MSH3 9 541577 60175 1.2342 0.270708
#> NIBGE GANDUM3 9 763272 84808 1.7394 0.076811 .
#> NIBGE GANDUM4 9 183983 20443 0.4193 0.925128
#> NR443 9 556283 61809 1.2677 0.251098
#> NR448 9 602418 66935 1.3728 0.196605
#> NR487 9 529356 58817 1.2063 0.287854
#> NR488 9 439359 48818 1.0012 0.437501
#> NR491 9 690621 76736 1.5738 0.119205
#> NRL1123 9 774862 86096 1.7658 0.071467 .
#> NRL1206 9 715799 79533 1.6312 0.102626
#> NW181838 9 611492 67944 1.3935 0.187095
#> NW5201 9 890418 98935 2.0291 0.033926 *
#> PAKISTAN13 9 991699 110189 2.2599 0.017078 *
#> PR115 9 570781 63420 1.3007 0.232841
#> PR118 9 553769 61530 1.2620 0.254371
#> PR119 9 329640 36627 0.7512 0.661879
#> PR120 9 505853 56206 1.1528 0.323002
#> PR121 9 967924 107547 2.2058 0.020113 *
#> QS3 9 1144963 127218 2.6092 0.005769 **
#> SD1013 9 930660 103407 2.1208 0.025915 *
#> SRN13121 9 1231054 136784 2.8054 0.003074 **
#> TWS12155 9 326136 36237 0.7432 0.669305
#> TWS12245 9 474036 52671 1.0803 0.375101
#> TWS12464 9 432572 48064 0.9858 0.450346
#> UOS1 9 571017 63446 1.3013 0.232552
#> V12066 9 529175 58797 1.2059 0.288114
#> V13348 9 1202805 133645 2.7410 0.003785 **
#> V14154 9 189614 21068 0.4321 0.917993
#> V14168 9 751301 83478 1.7121 0.082705 .
#> V14170 9 892534 99170 2.0340 0.033453 *
#> V14225 9 463652 51517 1.0566 0.393196
#> V14227 9 700835 77871 1.5971 0.112217
#> WBG14 9 467120 51902 1.0645 0.387096
#> WV1038 9 889119 98791 2.0262 0.034219 *
#> Pooled error 660 32179784 48757
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>