Compute the Weighted Average of Absolute Scores (Olivoto et al., 2019) for quantifying the stability of g genotypes conducted in e environments using linear mixed-effect models.

waasb(
  .data,
  env,
  gen,
  rep,
  resp,
  block = NULL,
  mresp = NULL,
  wresp = NULL,
  random = "gen",
  prob = 0.05,
  ind_anova = TRUE,
  verbose = TRUE,
  ...
)

Arguments

.data

The dataset containing the columns related to Environments, Genotypes, replication/block and response variable(s).

env

The name of the column that contains the levels of the environments.

gen

The name of the column that contains the levels of the genotypes.

rep

The name of the column that contains the levels of the replications/blocks.

resp

The response variable(s). To analyze multiple variables in a single procedure a vector of variables may be used. For example resp = c(var1, var2, var3).

block

Defaults to NULL. In this case, a randomized complete block design is considered. If block is informed, then an alpha-lattice design is employed considering block as random to make use of inter-block information, whereas the complete replicate effect is always taken as fixed, as no inter-replicate information was to be recovered (Mohring et al., 2015).

mresp

The new maximum value after rescaling the response variable. By default, all variables in resp are rescaled so that de maximum value is 100 and the minimum value is 0 (i.e., mresp = 100). It must be a numeric vector of the same length of resp if rescaling is assumed to be different across variables, e.g., if for the first variable smaller values are better and for the second one, higher values are better, then mresp = c(0, 100) must be used. Numeric value of length 1 will be recycled with a warning message.

wresp

The weight for the response variable(s) for computing the WAASBY index. By default, all variables in resp have equal weights for mean performance and stability (i.e., wresp = 50). It must be a numeric vector of the same length of resp to assign different weights across variables, e.g., if for the first variable equal weights for mean performance and stability are assumed and for the second one, a higher weight for mean performance (e.g. 65) is assumed, then wresp = c(50, 65) must be used. Numeric value of length 1 will be recycled with a warning message.

random

The effects of the model assumed to be random. Defaults to random = "gen". See Details to see the random effects assumed depending on the experimental design of the trials.

prob

The probability for estimating confidence interval for BLUP's prediction.

ind_anova

Logical argument set to TRUE. If FALSE the within-environment ANOVA is not performed.

verbose

Logical argument. If verbose = FALSE the code will run silently.

...

Arguments passed to the function impute_missing_val() for imputation of missing values in the matrix of BLUPs for genotype-environment interaction, thus allowing the computation of the WAASB index.

Value

An object of class waasb with the following items for each variable:

  • individual A within-environments ANOVA considering a fixed-effect model.

  • fixed Test for fixed effects.

  • random Variance components for random effects.

  • LRT The Likelihood Ratio Test for the random effects.

  • model A tibble with the response variable, the scores of all IPCAs, the estimates of Weighted Average of Absolute Scores, and WAASBY (the index that considers the weights for stability and mean performance in the genotype ranking), and their respective ranks.

  • BLUPgen The random effects and estimated BLUPS for genotypes (If random = "gen" or random = "all")

  • BLUPenv The random effects and estimated BLUPS for environments, (If random = "env" or random = "all").

  • BLUPint The random effects and estimated BLUPS of all genotypes in all environments.

  • PCA The results of Principal Component Analysis with the eigenvalues and explained variance of the matrix of genotype-environment effects estimated by the linear fixed-effect model.

  • MeansGxE The phenotypic means of genotypes in the environments.

  • Details A list summarizing the results. The following information are shown: Nenv, the number of environments in the analysis; Ngen the number of genotypes in the analysis; mresp The value attributed to the highest value of the response variable after rescaling it; wresp The weight of the response variable for estimating the WAASBY index. Mean the grand mean; SE the standard error of the mean; SD the standard deviation. CV the coefficient of variation of the phenotypic means, estimating WAASB, Min the minimum value observed (returning the genotype and environment), Max the maximum value observed (returning the genotype and environment); MinENV the environment with the lower mean, MaxENV the environment with the larger mean observed, MinGEN the genotype with the lower mean, MaxGEN the genotype with the larger.

  • ESTIMATES A tibble with the genetic parameters (if random = "gen" or random = "all") with the following columns: Phenotypic variance the phenotypic variance; Heritability the broad-sense heritability; GEr2 the coefficient of determination of the interaction effects; Heribatility of means the heritability on the mean basis; Accuracy the selective accuracy; rge the genotype-environment correlation; CVg the genotypic coefficient of variation; CVr the residual coefficient of variation; CV ratio the ratio between genotypic and residual coefficient of variation.

  • residuals The residuals of the model.

Details

The weighted average of absolute scores is computed considering all Interaction Principal Component Axis (IPCA) from the Singular Value Decomposition (SVD) of the matrix of genotype-environment interaction (GEI) effects generated by a linear mixed-effect model, as follows: $$WAASB_i = \sum_{k = 1}^{p} |IPCA_{ik} \times EP_k|/ \sum_{k = 1}^{p}EP_k$$

where \(WAASB_i\) is the weighted average of absolute scores of the ith genotype; \(IPCA_{ik}\) is the score of the ith genotype in the kth Interaction Principal Component Axis (IPCA); and \(EP_k\) is the explained variance of the kth IPCA for k = 1,2,..,p, considering \(p = min(g - 1; e - 1)\).

The nature of the effects in the model is chosen with the argument random. By default, the experimental design considered in each environment is a randomized complete block design. If block is informed, a resolvable alpha-lattice design (Patterson and Williams, 1976) is implemented. The following six models can be fitted depending on the values of random and block arguments.

  • Model 1: block = NULL and random = "gen" (The default option). This model considers a Randomized Complete Block Design in each environment assuming genotype and genotype-environment interaction as random effects. Environments and blocks nested within environments are assumed to fixed factors.

  • Model 2: block = NULL and random = "env". This model considers a Randomized Complete Block Design in each environment treating environment, genotype-environment interaction, and blocks nested within environments as random factors. Genotypes are assumed to be fixed factors.

  • Model 3: block = NULL and random = "all". This model considers a Randomized Complete Block Design in each environment assuming a random-effect model, i.e., all effects (genotypes, environments, genotype-vs-environment interaction and blocks nested within environments) are assumed to be random factors.

  • Model 4: block is not NULL and random = "gen". This model considers an alpha-lattice design in each environment assuming genotype, genotype-environment interaction, and incomplete blocks nested within complete replicates as random to make use of inter-block information (Mohring et al., 2015). Complete replicates nested within environments and environments are assumed to be fixed factors.

  • Model 5: block is not NULL and random = "env". This model considers an alpha-lattice design in each environment assuming genotype as fixed. All other sources of variation (environment, genotype-environment interaction, complete replicates nested within environments, and incomplete blocks nested within replicates) are assumed to be random factors.

  • Model 6: block is not NULL and random = "all". This model considers an alpha-lattice design in each environment assuming all effects, except the intercept, as random factors.

References

Olivoto, T., A.D.C. L\'ucio, J.A.G. da silva, V.S. Marchioro, V.Q. de Souza, and E. Jost. 2019. Mean performance and stability in multi-environment trials I: Combining features of AMMI and BLUP techniques. Agron. J. 111:2949-2960. doi:10.2134/agronj2019.03.0220

Mohring, J., E. Williams, and H.-P. Piepho. 2015. Inter-block information: to recover or not to recover it? TAG. Theor. Appl. Genet. 128:1541-54. doi:10.1007/s00122-015-2530-0

Patterson, H.D., and E.R. Williams. 1976. A new class of resolvable incomplete block designs. Biometrika 63:83-92. doi:10.1093/biomet/63.1.83

See also

Examples

# \donttest{ library(metan) #===============================================================# # Example 1: Analyzing all numeric variables assuming genotypes # # as random effects with equal weights for mean performance and # # stability # #===============================================================# model <- waasb(data_ge, env = ENV, gen = GEN, rep = REP, resp = everything())
#> Method: REML/BLUP
#> Random effects: GEN, GEN:ENV
#> Fixed effects: ENV, REP(ENV)
#> Denominador DF: Satterthwaite's method
#> --------------------------------------------------------------------------- #> P-values for Likelihood Ratio Test of the analyzed traits #> --------------------------------------------------------------------------- #> model GY HM #> COMPLETE NA NA #> GEN 1.11e-05 5.07e-03 #> GEN:ENV 2.15e-11 2.27e-15 #> --------------------------------------------------------------------------- #> All variables with significant (p < 0.05) genotype-vs-environment interaction
# Distribution of random effects (first variable) plot(model, type = "re")
# Genetic parameters get_model_data(model, "genpar")
#> Class of the model: waasb
#> Variable extracted: genpar
#> # A tibble: 9 x 3 #> Parameters GY HM #> <chr> <dbl> <dbl> #> 1 Phenotypic variance 0.181 5.52 #> 2 Heritability 0.154 0.0888 #> 3 GEIr2 0.313 0.397 #> 4 Heribatility of means 0.815 0.686 #> 5 Accuracy 0.903 0.828 #> 6 rge 0.370 0.435 #> 7 CVg 6.26 1.46 #> 8 CVr 11.6 3.50 #> 9 CV ratio 0.538 0.415
#===============================================================# # Example 2: Analyzing variables that starts with "N" # # assuming environment as random effects with higher weight for # # response variable (65) for the three traits. # #===============================================================# model2 <- waasb(data_ge2, env = ENV, gen = GEN, rep = REP, random = "env", resp = starts_with("N"), wresp = 65)
#> Warning: Invalid length in 'wresp'. Setting wresp = 65 to all the 3 variables.
#> Method: REML/BLUP
#> Random effects: REP(ENV), ENV, GEN:ENV
#> Fixed effects: GEN
#> Denominador DF: Satterthwaite's method
#> --------------------------------------------------------------------------- #> P-values for Likelihood Ratio Test of the analyzed traits #> --------------------------------------------------------------------------- #> model NR NKR NKE #> COMPLETE NA NA NA #> REP(ENV) 1.00e+00 1.00000 1.000000 #> ENV 2.84e-01 0.02314 0.003903 #> GEN:ENV 2.03e-05 0.00242 0.000165 #> --------------------------------------------------------------------------- #> All variables with significant (p < 0.05) genotype-vs-environment interaction
# Get the index WAASBY get_model_data(model2, what = "WAASBY")
#> Class of the model: waasb
#> Variable extracted: WAASBY
#> # A tibble: 13 x 4 #> gen NR NKR NKE #> <fct> <dbl> <dbl> <dbl> #> 1 H1 69.2 42.7 33.5 #> 2 H10 35.7 53.7 49.3 #> 3 H11 9.63 58.2 47.1 #> 4 H12 63.6 35 36.8 #> 5 H13 84.6 27.0 60.0 #> 6 H2 39.7 50.8 62.8 #> 7 H3 18.4 48.4 16.0 #> 8 H4 28.4 97.6 88.1 #> 9 H5 28.5 74.0 94.0 #> 10 H6 55.8 34.5 32.2 #> 11 H7 52.0 42.0 40.9 #> 12 H8 42.1 27.3 21.9 #> 13 H9 26.4 48.2 14.9
# Plot the scores (response x WAASB) plot_scores(model2, type = 3)
#===============================================================# # Example 3: Analyzing GY and HM assuming a random-effect model.# # Smaller values for HM and higher values for GY are better. # # To estimate WAASBY, higher weight for the GY (60%) and lower # # weight for HM (40%) are considered for mean performance. # #===============================================================# model3 <- waasb(data_ge, env = ENV, gen = GEN, rep = REP, resp = c(GY, HM), random = "all", mresp = c(100, 0), wresp = c(60, 40))
#> Method: REML/BLUP
#> Random effects: GEN, REP(ENV), ENV, GEN:ENV
#> Fixed effects: -
#> Denominador DF: Satterthwaite's method
#> --------------------------------------------------------------------------- #> P-values for Likelihood Ratio Test of the analyzed traits #> --------------------------------------------------------------------------- #> model GY HM #> COMPLETE NA NA #> GEN 1.11e-05 5.07e-03 #> REP(ENV) 9.91e-08 5.73e-05 #> ENV 8.26e-17 3.55e-16 #> GEN:ENV 2.15e-11 2.27e-15 #> --------------------------------------------------------------------------- #> All variables with significant (p < 0.05) genotype-vs-environment interaction
# Get P-values for Likelihood-ratio test get_model_data(model3, "pval_lrt")
#> Class of the model: waasb
#> Variable extracted: pval_lrt
#> # A tibble: 5 x 3 #> model GY HM #> <chr> <dbl> <dbl> #> 1 COMPLETE NA NA #> 2 GEN 1.11e- 5 5.07e- 3 #> 3 REP(ENV) 9.91e- 8 5.73e- 5 #> 4 ENV 8.26e-17 3.55e-16 #> 5 GEN:ENV 2.15e-11 2.27e-15
# Get the random effects get_model_data(model3, what = "ranef")
#> Class of the model: waasb
#> Variable extracted: ranef
#> $ENV #> ENV GY HM #> 1 E1 -0.14987889 -0.6482226 #> 2 E10 -0.48707480 -3.6654133 #> 3 E11 -1.27468381 5.9118693 #> 4 E12 -1.04019284 1.4594569 #> 5 E13 0.22974885 -1.4787160 #> 6 E14 -0.87092729 -6.8277388 #> 7 E2 0.49359950 -3.8392525 #> 8 E3 1.35678285 4.6556472 #> 9 E4 0.97704468 1.8497082 #> 10 E5 1.20663561 4.0080235 #> 11 E6 -0.01076389 -2.1269847 #> 12 E7 -0.66892377 0.3935226 #> 13 E8 -0.13456257 -2.8316947 #> 14 E9 0.37319638 3.1397951 #> #> $GEN #> GEN GY HM #> 1 G1 -0.05752558 -0.69206449 #> 2 G10 -0.16550417 0.28693603 #> 3 G2 0.05698730 -0.98027492 #> 4 G3 0.22915684 -0.33245498 #> 5 G4 -0.02635264 -0.03868947 #> 6 G5 -0.11159969 0.82986306 #> 7 G6 -0.11430129 0.44133448 #> 8 G7 0.05434795 -0.08378362 #> 9 G8 0.26852599 0.69588769 #> 10 G9 -0.13373472 -0.12675377 #> #> $ENV_GEN #> ENV GEN GY HM #> 1 E1 G1 -6.330168e-02 -0.149391245 #> 2 E1 G10 -2.442190e-01 -0.562983564 #> 3 E1 G2 2.054250e-01 -0.783713457 #> 4 E1 G3 8.725359e-02 -0.847360490 #> 5 E1 G4 5.887241e-02 0.627955640 #> 6 E1 G5 -1.420185e-01 1.131700496 #> 7 E1 G6 -6.853722e-02 0.262525651 #> 8 E1 G7 1.254787e-01 0.070591591 #> 9 E1 G8 6.902008e-02 -0.113049271 #> 10 E1 G9 -4.012444e-02 0.263518262 #> 11 E10 G1 1.171684e-01 1.148082763 #> 12 E10 G10 -3.058352e-01 -2.095716746 #> 13 E10 G2 3.740561e-02 2.047565271 #> 14 E10 G3 -4.707773e-02 0.198746433 #> 15 E10 G4 7.845121e-03 -1.868351371 #> 16 E10 G5 4.465353e-02 4.042113306 #> 17 E10 G6 8.993467e-02 1.054938015 #> 18 E10 G7 1.314426e-01 -2.302359350 #> 19 E10 G8 7.966421e-02 -0.984780116 #> 20 E10 G9 -1.946898e-01 -1.806861187 #> 21 E11 G1 1.831286e-02 0.436133229 #> 22 E11 G10 -2.039568e-01 -0.247445959 #> 23 E11 G2 3.141004e-02 -0.293615376 #> 24 E11 G3 -2.926851e-02 -0.280455799 #> 25 E11 G4 7.575449e-03 -0.020080585 #> 26 E11 G5 3.366320e-02 -2.092848361 #> 27 E11 G6 1.469708e-01 1.273977688 #> 28 E11 G7 -4.749476e-02 0.604911659 #> 29 E11 G8 1.940646e-02 1.096237972 #> 30 E11 G9 -7.996110e-02 0.437080012 #> 31 E12 G1 -1.414765e-01 0.729227051 #> 32 E12 G10 -2.767933e-01 0.441318276 #> 33 E12 G2 2.006029e-01 -2.735302350 #> 34 E12 G3 -5.517842e-02 1.001814055 #> 35 E12 G4 -4.339200e-02 0.098452761 #> 36 E12 G5 1.140472e-01 -0.880402696 #> 37 E12 G6 -7.764413e-02 0.845554874 #> 38 E12 G7 1.756944e-01 0.653620815 #> 39 E12 G8 6.997070e-02 0.283782111 #> 40 E12 G9 -5.016229e-02 -0.212452738 #> 41 E13 G1 9.505456e-02 -1.047833026 #> 42 E13 G10 -5.805854e-01 1.585236836 #> 43 E13 G2 4.119824e-02 -2.289625695 #> 44 E13 G3 2.125098e-01 -0.077004117 #> 45 E13 G4 -1.559900e-01 -0.026101474 #> 46 E13 G5 -1.447488e-01 0.170416943 #> 47 E13 G6 7.604247e-02 -1.303900886 #> 48 E13 G7 1.420521e-01 0.971286454 #> 49 E13 G8 2.198158e-01 1.334601751 #> 50 E13 G9 1.132777e-01 0.454333853 #> 51 E14 G1 -1.288953e-01 0.169372047 #> 52 E14 G10 1.477430e-01 0.533155716 #> 53 E14 G2 -2.699808e-01 -0.595288654 #> 54 E14 G3 2.390501e-02 -0.442480696 #> 55 E14 G4 6.037002e-02 -0.089006561 #> 56 E14 G5 6.560496e-02 -0.544180589 #> 57 E14 G6 7.660861e-02 -1.238794957 #> 58 E14 G7 5.673516e-02 2.072117876 #> 59 E14 G8 -4.442278e-02 -0.869578514 #> 60 E14 G9 -5.827650e-02 -0.050791107 #> 61 E2 G1 -4.812017e-02 -0.656301418 #> 62 E2 G10 8.777667e-02 0.454601091 #> 63 E2 G2 2.442319e-04 0.161719534 #> 64 E2 G3 1.309945e-01 -1.086611265 #> 65 E2 G4 2.725838e-02 -0.276952418 #> 66 E2 G5 4.976904e-02 1.290447607 #> 67 E2 G6 1.477969e-01 1.135806979 #> 68 E2 G7 -3.923673e-01 0.771639915 #> 69 E2 G8 1.649977e-03 0.441368253 #> 70 E2 G9 3.501539e-02 -2.829214434 #> 71 E3 G1 5.788183e-02 -1.004208532 #> 72 E3 G10 1.484114e-01 1.803421807 #> 73 E3 G2 2.962404e-01 -0.337473326 #> 74 E3 G3 -9.175833e-02 -1.022555655 #> 75 E3 G4 -1.060989e-01 0.401556069 #> 76 E3 G5 -1.237950e-01 0.260590898 #> 77 E3 G6 -3.176117e-01 0.299130531 #> 78 E3 G7 -6.170699e-05 0.665789995 #> 79 E3 G8 -1.311016e-01 -1.275092996 #> 80 E3 G9 3.778921e-01 0.928540856 #> 81 E4 G1 -7.077092e-02 -1.117408718 #> 82 E4 G10 4.933040e-01 1.224727017 #> 83 E4 G2 -2.718684e-03 -1.381662719 #> 84 E4 G3 1.543018e-01 0.027980668 #> 85 E4 G4 -2.131786e-01 0.055608581 #> 86 E4 G5 -1.097593e-01 0.612885316 #> 87 E4 G6 -9.250403e-02 0.651424948 #> 88 E4 G7 -4.452204e-01 -0.145652096 #> 89 E4 G8 1.316049e-01 0.008190629 #> 90 E4 G9 2.341530e-01 0.349846067 #> 91 E5 G1 2.122655e-01 -0.744698535 #> 92 E5 G10 -2.320885e-01 1.934920788 #> 93 E5 G2 -7.952062e-02 1.830564546 #> 94 E5 G3 1.491774e-03 1.669163646 #> 95 E5 G4 -5.496378e-02 -1.992253176 #> 96 E5 G5 -2.005712e-01 0.322265688 #> 97 E5 G6 -1.351330e-01 -0.546909157 #> 98 E5 G7 6.274755e-02 -1.623282963 #> 99 E5 G8 4.134477e-01 -0.166055348 #> 100 E5 G9 1.101501e-01 -0.064129631 #> 101 E6 G1 1.296053e-01 -0.752955412 #> 102 E6 G10 -8.269867e-03 -3.496348221 #> 103 E6 G2 -1.141383e-01 0.181439191 #> 104 E6 G3 5.879493e-02 0.764829657 #> 105 E6 G4 4.092769e-02 2.351864492 #> 106 E6 G5 -7.551417e-02 -0.046749506 #> 107 E6 G6 -1.361296e-01 0.084889047 #> 108 E6 G7 -3.302734e-02 -0.444528600 #> 109 E6 G8 -1.514557e-02 1.478193619 #> 110 E6 G9 1.520242e-01 -0.449437136 #> 111 E7 G1 -2.677761e-02 -0.554559900 #> 112 E7 G10 2.625305e-01 0.856586628 #> 113 E7 G2 -4.256924e-02 0.112175306 #> 114 E7 G3 -4.336599e-02 -0.805654325 #> 115 E7 G4 6.273048e-03 0.327523271 #> 116 E7 G5 -1.460391e-01 -0.918991583 #> 117 E7 G6 -7.990328e-02 0.422932940 #> 118 E7 G7 3.182175e-01 -0.897825534 #> 119 E7 G8 -2.644370e-03 0.803786749 #> 120 E7 G9 -2.999530e-01 0.714859678 #> 121 E8 G1 -1.330269e-01 -1.011725144 #> 122 E8 G10 2.089684e-01 -2.626293540 #> 123 E8 G2 -3.505536e-01 0.132142030 #> 124 E8 G3 5.303573e-02 0.296587353 #> 125 E8 G4 -1.346314e-01 1.790523267 #> 126 E8 G5 1.797112e-01 -0.270607144 #> 127 E8 G6 7.370646e-02 -0.010957574 #> 128 E8 G7 -5.771310e-03 -0.412364206 #> 129 E8 G8 4.606275e-02 1.743105315 #> 130 E8 G9 5.158931e-02 -0.068151789 #> 131 E9 G1 -1.343245e-01 1.462754589 #> 132 E9 G10 1.681103e-01 1.477417306 #> 133 E9 G2 1.622708e-01 -0.430730526 #> 134 E9 G3 8.069142e-03 -0.883065553 #> 135 E9 G4 4.458071e-01 -1.553679546 #> 136 E9 G5 2.291709e-01 0.632828302 #> 137 E9 G6 6.511019e-02 -0.957863178 #> 138 E9 G7 2.155002e-02 -0.358456412 #> 139 E9 G8 -3.139560e-01 -0.670108290 #> 140 E9 G9 -6.215517e-01 1.766272864 #> #> $ENV_REP #> ENV REP GY HM #> 1 E1 1 0.03884834 0.006904903 #> 2 E1 2 0.02419067 -0.365515083 #> 3 E1 3 -0.06836048 0.336474535 #> 4 E10 1 0.09511641 -0.671874704 #> 5 E10 2 0.10576201 0.525414591 #> 6 E10 3 -0.21817207 0.021292783 #> 7 E11 1 -0.01425579 -0.060837694 #> 8 E11 2 0.12431110 0.446434875 #> 9 E11 3 -0.15531313 -0.183717385 #> 10 E12 1 -0.05274477 -0.211292451 #> 11 E12 2 0.06736038 -0.135674179 #> 12 E12 3 -0.05154779 0.396804480 #> 13 E13 1 0.03192273 0.890587416 #> 14 E13 2 0.04587586 0.156460033 #> 15 E13 3 -0.06964133 -1.097542965 #> 16 E14 1 0.03191981 0.406448617 #> 17 E14 2 0.03202911 0.151236952 #> 18 E14 3 -0.09487132 -0.790840677 #> 19 E2 1 -0.21319687 -0.549293373 #> 20 E2 2 0.12929106 -0.063445981 #> 21 E2 3 0.10143113 0.481635725 #> 22 E3 1 0.19691509 -0.010021209 #> 23 E3 2 -0.10237073 0.620131051 #> 24 E3 3 -0.04637161 -0.451127791 #> 25 E4 1 0.06533069 -0.294021385 #> 26 E4 2 0.04685241 0.273115649 #> 27 E4 3 -0.07749300 0.084069971 #> 28 E5 1 0.01046573 0.001091526 #> 29 E5 2 0.12260795 0.588393433 #> 30 E5 3 -0.09023193 -0.452618101 #> 31 E6 1 0.10343963 -0.830805805 #> 32 E6 2 -0.03651287 0.530323077 #> 33 E6 3 -0.06730893 0.227849992 #> 34 E7 1 -0.14554935 -0.187717075 #> 35 E7 2 -0.01347347 0.117906772 #> 36 E7 3 0.13527260 0.083248397 #> 37 E8 1 -0.23259159 -0.650831910 #> 38 E8 2 0.06599532 -0.156162386 #> 39 E8 3 0.16181861 0.710296972 #> 40 E9 1 0.05991207 0.581871427 #> 41 E9 2 0.14362285 -0.300341738 #> 42 E9 3 -0.19028454 -0.174311286 #> #> $ENV_GEN_REP #> ENV GEN REP GY HM #> 1 E1 G1 1 -0.2318578116 -1.48277342 #> 2 E1 G1 2 -0.2465154753 -1.85519341 #> 3 E1 G1 3 -0.3390666298 -1.15320379 #> 4 E1 G10 1 -0.5207537223 -0.91736522 #> 5 E1 G10 2 -0.5354113860 -1.28978520 #> 6 E1 G10 3 -0.6279625405 -0.58779559 #> 7 E1 G2 1 0.1513817047 -2.40530606 #> 8 E1 G2 2 0.1367240410 -2.77772605 #> 9 E1 G2 3 0.0441728865 -2.07573643 #> 10 E1 G3 1 0.2053798727 -1.82113316 #> 11 E1 G3 2 0.1907222091 -2.19355315 #> 12 E1 G3 3 0.0981710546 -1.49156353 #> 13 E1 G4 1 -0.0785107757 -0.05205152 #> 14 E1 G4 2 -0.0931684393 -0.42447151 #> 15 E1 G4 3 -0.1857195938 0.27751811 #> 16 E1 G5 1 -0.3646487756 1.32024587 #> 17 E1 G5 2 -0.3793064392 0.94782588 #> 18 E1 G5 3 -0.4718575937 1.64981550 #> 19 E1 G6 1 -0.2938690633 0.06254244 #> 20 E1 G6 2 -0.3085267270 -0.30987754 #> 21 E1 G6 3 -0.4010778815 0.39211208 #> 22 E1 G7 1 0.0687960948 -0.65450972 #> 23 E1 G7 2 0.0541384311 -1.02692970 #> 24 E1 G7 3 -0.0384127234 -0.32494009 #> 25 E1 G8 1 0.2265155187 -0.05847927 #> 26 E1 G8 2 0.2118578551 -0.43089926 #> 27 E1 G8 3 0.1193067006 0.27109036 #> 28 E1 G9 1 -0.2848897096 -0.50455320 #> 29 E1 G9 2 -0.2995473733 -0.87697318 #> 30 E1 G9 3 -0.3920985278 -0.17498357 #> 31 E10 G1 1 -0.3323155813 -3.88126974 #> 32 E10 G1 2 -0.3216699888 -2.68398045 #> 33 E10 G1 3 -0.6456040680 -3.18810226 #> 34 E10 G10 1 -0.8632977271 -6.14606873 #> 35 E10 G10 2 -0.8526521346 -4.94877944 #> 36 E10 G10 3 -1.1765862138 -5.45290125 #> 37 E10 G2 1 -0.2975654704 -3.26999767 #> 38 E10 G2 2 -0.2869198779 -2.07270838 #> 39 E10 G2 3 -0.6108539571 -2.57683018 #> 40 E10 G3 1 -0.2098792705 -4.47099657 #> 41 E10 G3 2 -0.1992336780 -3.27370728 #> 42 E10 G3 3 -0.5231677572 -3.77782908 #> 43 E10 G4 1 -0.4104658987 -6.24432887 #> 44 E10 G4 2 -0.3998203062 -5.04703957 #> 45 E10 G4 3 -0.7237543854 -5.55116138 #> 46 E10 G5 1 -0.4589045468 0.53468834 #> 47 E10 G5 2 -0.4482589543 1.73197764 #> 48 E10 G5 3 -0.7721930335 1.22785583 #> 49 E10 G6 1 -0.4163250116 -2.84101553 #> 50 E10 G6 2 -0.4056794191 -1.64372623 #> 51 E10 G6 3 -0.7296134983 -2.14784804 #> 52 E10 G7 1 -0.2061678052 -6.72343099 #> 53 E10 G7 2 -0.1955222127 -5.52614170 #> 54 E10 G7 3 -0.5194562919 -6.03026351 #> 55 E10 G8 1 -0.0437681891 -4.62618045 #> 56 E10 G8 2 -0.0331225966 -3.42889116 #> 57 E10 G8 3 -0.3570566758 -3.93301296 #> 58 E10 G9 1 -0.7203829262 -6.27090298 #> 59 E10 G9 2 -0.7097373337 -5.07361369 #> 60 E10 G9 3 -1.0336714129 -5.57773549 #> 61 E11 G1 1 -1.3281523186 5.59510032 #> 62 E11 G1 2 -1.1895854286 6.10237289 #> 63 E11 G1 3 -1.4692096583 5.47222063 #> 64 E11 G10 1 -1.6584005541 5.89052165 #> 65 E11 G10 2 -1.5198336640 6.39779422 #> 66 E11 G10 3 -1.7994578937 5.76764196 #> 67 E11 G2 1 -1.2005422638 4.57714128 #> 68 E11 G2 2 -1.0619753738 5.08441385 #> 69 E11 G2 3 -1.3415996034 4.45426159 #> 70 E11 G3 1 -1.0890512717 5.23812080 #> 71 E11 G3 2 -0.9504843817 5.74539337 #> 72 E11 G3 3 -1.2301086114 5.11524111 #> 73 E11 G4 1 -1.3077167894 5.79226152 #> 74 E11 G4 2 -1.1691498994 6.29953409 #> 75 E11 G4 3 -1.4487741291 5.66938183 #> 76 E11 G5 1 -1.3668760957 4.58804628 #> 77 E11 G5 2 -1.2283092057 5.09531885 #> 78 E11 G5 3 -1.5079334353 4.46516658 #> 79 E11 G6 1 -1.2562701159 7.56634375 #> 80 E11 G6 2 -1.1177032259 8.07361632 #> 81 E11 G6 3 -1.3973274556 7.44346406 #> 82 E11 G7 1 -1.2820864140 6.37215962 #> 83 E11 G7 2 -1.1435195240 6.87943219 #> 84 E11 G7 3 -1.4231437537 6.24927993 #> 85 E11 G8 1 -1.0010071562 7.64315724 #> 86 E11 G8 2 -0.8624402662 8.15042981 #> 87 E11 G8 3 -1.1420644958 7.52027755 #> 88 E11 G9 1 -1.5026354163 6.16135782 #> 89 E11 G9 2 -1.3640685263 6.66863039 #> 90 E11 G9 3 -1.6436927560 6.03847813 #> 91 E12 G1 1 -1.2919396847 1.28532699 #> 92 E12 G1 2 -1.1718345389 1.36094526 #> 93 E12 G1 3 -1.2907427120 1.89342392 #> 94 E12 G10 1 -1.5352350914 1.97641873 #> 95 E12 G10 2 -1.4151299456 2.05203701 #> 96 E12 G10 3 -1.5340381187 2.58451566 #> 97 E12 G2 1 -0.8353474016 -2.46741285 #> 98 E12 G2 2 -0.7152422558 -2.39179457 #> 99 E12 G2 3 -0.8341504289 -1.85931591 #> 100 E12 G3 1 -0.9189591954 1.91752350 #> 101 E12 G3 2 -0.7988540496 1.99314177 #> 102 E12 G3 3 -0.9177622228 2.52562043 #> 103 E12 G4 1 -1.1626822483 1.30792771 #> 104 E12 G4 2 -1.0425771025 1.38354598 #> 105 E12 G4 3 -1.1614852756 1.91602464 #> 106 E12 G5 1 -1.0904901119 1.19762479 #> 107 E12 G5 2 -0.9703849660 1.27324306 #> 108 E12 G5 3 -1.0892931392 1.80572172 #> 109 E12 G6 1 -1.2848830322 2.53505378 #> 110 E12 G6 2 -1.1647778864 2.61067205 #> 111 E12 G6 3 -1.2836860595 3.14315071 #> 112 E12 G7 1 -0.8628952928 1.81800162 #> 113 E12 G7 2 -0.7427901470 1.89361989 #> 114 E12 G7 3 -0.8616983201 2.42609855 #> 115 E12 G8 1 -0.7544409255 2.22783422 #> 116 E12 G8 2 -0.6343357796 2.30345249 #> 117 E12 G8 3 -0.7532439528 2.83593115 #> 118 E12 G9 1 -1.2768346159 0.90895791 #> 119 E12 G9 2 -1.1567294701 0.98457619 #> 120 E12 G9 3 -1.2756376432 1.51705484 #> 121 E13 G1 1 0.2992005672 -2.32802613 #> 122 E13 G1 2 0.3131536964 -3.06215351 #> 123 E13 G1 3 0.1976365016 -4.31615651 #> 124 E13 G10 1 -0.4844179652 1.28404426 #> 125 E13 G10 2 -0.4704648360 0.54991687 #> 126 E13 G10 3 -0.5859820308 -0.70408612 #> 127 E13 G2 1 0.3598571249 -3.85802923 #> 128 E13 G2 2 0.3738102542 -4.59215661 #> 129 E13 G2 3 0.2582930593 -5.84615961 #> 130 E13 G3 1 0.7033382547 -0.99758771 #> 131 E13 G3 2 0.7172913840 -1.73171510 #> 132 E13 G3 3 0.6017741891 -2.98571809 #> 133 E13 G4 1 0.0793289232 -0.65291956 #> 134 E13 G4 2 0.0932820525 -1.38704694 #> 135 E13 G4 3 -0.0222351424 -2.64104994 #> 136 E13 G5 1 0.0053231096 0.41215139 #> 137 E13 G5 2 0.0192762389 -0.32197600 #> 138 E13 G5 3 -0.0962409560 -1.57597899 #> 139 E13 G6 1 0.2234127643 -1.45069502 #> 140 E13 G6 2 0.2373658935 -2.18482240 #> 141 E13 G6 3 0.1218486987 -3.43882540 #> 142 E13 G7 1 0.4580715883 0.29937422 #> 143 E13 G7 2 0.4720247176 -0.43475317 #> 144 E13 G7 3 0.3565075227 -1.68875616 #> 145 E13 G8 1 0.7500133569 1.44236082 #> 146 E13 G8 2 0.7639664862 0.70823344 #> 147 E13 G8 3 0.6484492914 -0.54576956 #> 148 E13 G9 1 0.2412145329 -0.26054853 #> 149 E13 G9 2 0.2551676622 -0.99467592 #> 150 E13 G9 3 0.1396504673 -2.24867891 #> 151 E14 G1 1 -1.0254283896 -6.94398264 #> 152 E14 G1 2 -1.0253190892 -7.19919431 #> 153 E14 G1 3 -1.1522195210 -8.14127194 #> 154 E14 G10 1 -0.8567686423 -5.60119845 #> 155 E14 G10 2 -0.8566593419 -5.85641012 #> 156 E14 G10 3 -0.9835597736 -6.79848775 #> 157 E14 G2 1 -1.0520009663 -7.99685378 #> 158 E14 G2 2 -1.0518916659 -8.25206544 #> 159 E14 G2 3 -1.1787920976 -9.19414307 #> 160 E14 G3 1 -0.5859456237 -7.19622588 #> 161 E14 G3 2 -0.5858363233 -7.45143755 #> 162 E14 G3 3 -0.7127367551 -8.39351518 #> 163 E14 G4 1 -0.8049900973 -6.54898624 #> 164 E14 G4 2 -0.8048807969 -6.80419790 #> 165 E14 G4 3 -0.9317812287 -7.74627553 #> 166 E14 G5 1 -0.8850022126 -6.13560773 #> 167 E14 G5 2 -0.8848929122 -6.39081940 #> 168 E14 G5 3 -1.0117933440 -7.33289703 #> 169 E14 G6 1 -0.8767001613 -7.21875068 #> 170 E14 G6 2 -0.8765908609 -7.47396235 #> 171 E14 G6 3 -1.0034912926 -8.41603997 #> 172 E14 G7 1 -0.7279243673 -4.43295595 #> 173 E14 G7 2 -0.7278150669 -4.68816762 #> 174 E14 G7 3 -0.8547154987 -5.63024524 #> 175 E14 G8 1 -0.6149042660 -6.59498103 #> 176 E14 G8 2 -0.6147949656 -6.85019270 #> 177 E14 G8 3 -0.7416953974 -7.79227032 #> 178 E14 G9 1 -1.0310186907 -6.59883508 #> 179 E14 G9 2 -1.0309093903 -6.85404675 #> 180 E14 G9 3 -1.1578098221 -7.79612438 #> 181 E2 G1 1 0.1747568868 -5.73691180 #> 182 E2 G1 2 0.5172448157 -5.25106441 #> 183 E2 G1 3 0.4893848899 -4.70598271 #> 184 E2 G10 1 0.2026751298 -3.64700878 #> 185 E2 G10 2 0.5451630587 -3.16116138 #> 186 E2 G10 3 0.5173031329 -2.61607968 #> 187 E2 G2 1 0.3376341664 -5.20710129 #> 188 E2 G2 2 0.6801220953 -4.72125389 #> 189 E2 G2 3 0.6522621695 -4.17617219 #> 190 E2 G3 1 0.6405539455 -5.80761215 #> 191 E2 G3 2 0.9830418744 -5.32176476 #> 192 E2 G3 3 0.9551819486 -4.77668305 #> 193 E2 G4 1 0.2813083772 -4.70418779 #> 194 E2 G4 2 0.6237963061 -4.21834040 #> 195 E2 G4 3 0.5959363803 -3.67325869 #> 196 E2 G5 1 0.2185719758 -2.26823524 #> 197 E2 G5 2 0.5610599048 -1.78238784 #> 198 E2 G5 3 0.5331999789 -1.23730614 #> 199 E2 G6 1 0.3138982546 -2.81140444 #> 200 E2 G6 2 0.6563861836 -2.32555705 #> 201 E2 G6 3 0.6285262577 -1.78047534 #> 202 E2 G7 1 -0.0576167620 -3.70068961 #> 203 E2 G7 2 0.2848711669 -3.21484221 #> 204 E2 G7 3 0.2570112411 -2.66976051 #> 205 E2 G8 1 0.5505785984 -3.25128996 #> 206 E2 G8 2 0.8930665274 -2.76544257 #> 207 E2 G8 3 0.8652066015 -2.22036086 #> 208 E2 G9 1 0.1816833091 -7.34451411 #> 209 E2 G9 2 0.5241712381 -6.85866671 #> 210 E2 G9 3 0.4963113122 -6.31358501 #> 211 E3 G1 1 1.5540541992 2.94935293 #> 212 E3 G1 2 1.2547683735 3.57950519 #> 213 E3 G1 3 1.3107675011 2.50824635 #> 214 E3 G10 1 1.5366051842 6.73598379 #> 215 E3 G10 2 1.2373193585 7.36613605 #> 216 E3 G10 3 1.2933184861 6.29487721 #> 217 E3 G2 1 1.9069256020 3.32787770 #> 218 E3 G2 2 1.6076397763 3.95802996 #> 219 E3 G2 3 1.6636389039 2.88677112 #> 220 E3 G3 1 1.6910964504 3.29061531 #> 221 E3 G3 2 1.3918106247 3.92076757 #> 222 E3 G3 3 1.4478097523 2.84950873 #> 223 E3 G4 1 1.4212463786 5.00849255 #> 224 E3 G4 2 1.1219605529 5.63864481 #> 225 E3 G4 3 1.1779596805 4.56738596 #> 226 E3 G5 1 1.3183032581 5.73607991 #> 227 E3 G5 2 1.0190174324 6.36623217 #> 228 E3 G5 3 1.0750165600 5.29497332 #> 229 E3 G6 1 1.1217849099 5.38609096 #> 230 E3 G6 2 0.8224990842 6.01624322 #> 231 E3 G6 3 0.8784982117 4.94498438 #> 232 E3 G7 1 1.6079841870 5.22763232 #> 233 E3 G7 2 1.3086983613 5.85778458 #> 234 E3 G7 3 1.3646974888 4.78652574 #> 235 E3 G8 1 1.6911223467 4.06642064 #> 236 E3 G8 2 1.3918365211 4.69657290 #> 237 E3 G8 3 1.4478356486 3.62531406 #> 238 E3 G9 1 1.7978553074 5.44741303 #> 239 E3 G9 2 1.4985694818 6.07756529 #> 240 E3 G9 3 1.5545686093 5.00630645 #> 241 E4 G1 1 0.9140788720 -0.25378643 #> 242 E4 G1 2 0.8956005995 0.31335061 #> 243 E4 G1 3 0.7712551846 0.12430493 #> 244 E4 G10 1 1.3701751877 3.06734983 #> 245 E4 G10 2 1.3516969152 3.63448686 #> 246 E4 G10 3 1.2273515002 3.44544118 #> 247 E4 G2 1 1.0966439900 -0.80625086 #> 248 E4 G2 2 1.0781657175 -0.23911383 #> 249 E4 G2 3 0.9538203026 -0.42815951 #> 250 E4 G3 1 1.4258340577 1.25121246 #> 251 E4 G3 2 1.4073557852 1.81834950 #> 252 E4 G3 3 1.2830103702 1.62930382 #> 253 E4 G4 1 0.8028441443 1.57260588 #> 254 E4 G4 2 0.7843658718 2.13974292 #> 255 E4 G4 3 0.6600204569 1.95069724 #> 256 E4 G5 1 0.8210164059 2.99843515 #> 257 E4 G5 2 0.8025381334 3.56557218 #> 258 E4 G5 3 0.6781927185 3.37652651 #> 259 E4 G6 1 0.8355700491 2.64844620 #> 260 E4 G6 2 0.8170917766 3.21558324 #> 261 E4 G6 3 0.6927463617 3.02653756 #> 262 E4 G7 1 0.6515028931 1.32625106 #> 263 E4 G7 2 0.6330246206 1.89338809 #> 264 E4 G7 3 0.5086792057 1.70434241 #> 265 E4 G8 1 1.4425063051 2.25976509 #> 266 E4 G8 2 1.4240280326 2.82690213 #> 267 E4 G8 3 1.2996826177 2.63785645 #> 268 E4 G9 1 1.1427936932 1.77877907 #> 269 E4 G9 2 1.1243154207 2.34591610 #> 270 E4 G9 3 0.9999700057 2.15687043 #> 271 E5 G1 1 1.3718413066 2.57235204 #> 272 E5 G1 2 1.4839835184 3.15965395 #> 273 E5 G1 3 1.2711436467 2.11864241 #> 274 E5 G10 1 0.8195086869 6.23097188 #> 275 E5 G10 2 0.9316508986 6.81827379 #> 276 E5 G10 3 0.7188110269 5.77726226 #> 277 E5 G2 1 1.1945680216 4.85940469 #> 278 E5 G2 2 1.3067102334 5.44670660 #> 279 E5 G2 3 1.0938703617 4.40569506 #> 280 E5 G3 1 1.4477499546 5.34582373 #> 281 E5 G3 2 1.5598921663 5.93312563 #> 282 E5 G3 3 1.3470522946 4.89211410 #> 283 E5 G4 1 1.1357849230 1.97817241 #> 284 E5 G4 2 1.2479271347 2.56547432 #> 285 E5 G4 3 1.0350872630 1.52446279 #> 286 E5 G5 1 0.9049304041 5.16124381 #> 287 E5 G5 2 1.0170726159 5.74854572 #> 288 E5 G5 3 0.8042327442 4.70753418 #> 289 E5 G6 1 0.9676670250 3.90354039 #> 290 E5 G6 2 1.0798092367 4.49084229 #> 291 E5 G6 3 0.8669693650 3.44983076 #> 292 E5 G7 1 1.3341968408 2.30204848 #> 293 E5 G7 2 1.4463390525 2.88935038 #> 294 E5 G7 3 1.2334991808 1.84833885 #> 295 E5 G8 1 1.8990750365 4.53894740 #> 296 E5 G8 2 2.0112172482 5.12624931 #> 297 E5 G8 3 1.7983773765 4.08523777 #> 298 E5 G9 1 1.1935166949 3.81823166 #> 299 E5 G9 2 1.3056589066 4.40553357 #> 300 E5 G9 3 1.0928190349 3.36452203 #> 301 E6 G1 1 0.1647554920 -4.40281040 #> 302 E6 G1 2 0.0248029928 -3.04168152 #> 303 E6 G1 3 -0.0059930618 -3.34415460 #> 304 E6 G10 1 -0.0810983001 -6.16720269 #> 305 E6 G10 2 -0.2210507993 -4.80607381 #> 306 E6 G10 3 -0.2518468538 -5.10854689 #> 307 E6 G2 1 0.0355247489 -3.75662623 #> 308 E6 G2 2 -0.1044277503 -2.39549735 #> 309 E6 G2 3 -0.1352238048 -2.69797043 #> 310 E6 G3 1 0.3806275015 -2.52541582 #> 311 E6 G3 2 0.2406750023 -1.16428694 #> 312 E6 G3 3 0.2098789477 -1.46676003 #> 313 E6 G4 1 0.1072507938 -0.64461548 #> 314 E6 G4 2 -0.0327017054 0.71651340 #> 315 E6 G4 3 -0.0634977600 0.41404032 #> 316 E6 G5 1 -0.0944381314 -2.17467695 #> 317 E6 G5 2 -0.2343906306 -0.81354807 #> 318 E6 G5 3 -0.2651866852 -1.11602115 #> 319 E6 G6 1 -0.1577551275 -2.43156697 #> 320 E6 G6 2 -0.2977076267 -1.07043809 #> 321 E6 G6 3 -0.3285036813 -1.37291117 #> 322 E6 G7 1 0.1139963489 -3.48610272 #> 323 E6 G7 2 -0.0259561503 -2.12497384 #> 324 E6 G7 3 -0.0567522048 -2.42744692 #> 325 E6 G8 1 0.3460561552 -0.78370919 #> 326 E6 G8 2 0.2061036560 0.57741969 #> 327 E6 G8 3 0.1753076014 0.27494660 #> 328 E6 G9 1 0.1109652098 -3.53398141 #> 329 E6 G9 2 -0.0289872894 -2.17285253 #> 330 E6 G9 3 -0.0597833439 -2.47532561 #> 331 E7 G1 1 -0.8987763086 -1.04081888 #> 332 E7 G1 2 -0.7667004229 -0.73519504 #> 333 E7 G1 3 -0.6179543598 -0.76985341 #> 334 E7 G10 1 -0.7174468070 1.34932816 #> 335 E7 G10 2 -0.5853709213 1.65495201 #> 336 E7 G10 3 -0.4366248582 1.62029364 #> 337 E7 G2 1 -0.8000550695 -0.66229411 #> 338 E7 G2 2 -0.6679791837 -0.35667026 #> 339 E7 G2 3 -0.5192331207 -0.39132864 #> 340 E7 G3 1 -0.6286822724 -0.93230380 #> 341 E7 G3 2 -0.4966063867 -0.62667996 #> 342 E7 G3 3 -0.3478603236 -0.66133833 #> 343 E7 G4 1 -0.8345527144 0.49463930 #> 344 E7 G4 2 -0.7024768286 0.80026315 #> 345 E7 G4 3 -0.5537307656 0.76560477 #> 346 E7 G5 1 -1.0721119348 0.11667698 #> 347 E7 G5 2 -0.9400360490 0.42230082 #> 348 E7 G5 3 -0.7912899860 0.38764245 #> 349 E7 G6 1 -1.0086777013 1.07007292 #> 350 E7 G6 2 -0.8766018155 1.37569677 #> 351 E7 G6 3 -0.7278557525 1.34103840 #> 352 E7 G7 1 -0.4419077042 -0.77580365 #> 353 E7 G7 2 -0.3098318185 -0.47017981 #> 354 E7 G7 3 -0.1610857554 -0.50483818 #> 355 E7 G8 1 -0.5485915080 1.70547994 #> 356 E7 G8 2 -0.4165156223 2.01110378 #> 357 E7 G8 3 -0.2677695592 1.97644541 #> 358 E7 G9 1 -1.2481608662 0.79391141 #> 359 E7 G9 2 -1.1160849804 1.09953526 #> 360 E7 G9 3 -0.9673389174 1.06487688 #> 361 E8 G1 1 -0.5577066728 -5.18631620 #> 362 E8 G1 2 -0.2591197565 -4.69164668 #> 363 E8 G1 3 -0.1632964679 -3.82518732 #> 364 E8 G10 1 -0.3236899396 -5.82188408 #> 365 E8 G10 2 -0.0251030233 -5.32721455 #> 366 E8 G10 3 0.0707202653 -4.46075520 #> 367 E8 G2 1 -0.6607204204 -4.33065946 #> 368 E8 G2 2 -0.3621335042 -3.83598994 #> 369 E8 G2 3 -0.2663102156 -2.96953058 #> 370 E8 G3 1 -0.0849615904 -3.51839420 #> 371 E8 G3 2 0.2136253259 -3.02372468 #> 372 E8 G3 3 0.3094486145 -2.15726532 #> 373 E8 G4 1 -0.5281381800 -1.73069278 #> 374 E8 G4 2 -0.2295512638 -1.23602325 #> 375 E8 G4 3 -0.1337279752 -0.36956390 #> 376 E8 G5 1 -0.2990427070 -2.92327066 #> 377 E8 G5 2 -0.0004557907 -2.42860113 #> 378 E8 G5 3 0.0953674979 -1.56214178 #> 379 E8 G6 1 -0.4077490025 -3.05214967 #> 380 E8 G6 2 -0.1091620863 -2.55748014 #> 381 E8 G6 3 -0.0133387977 -1.69102078 #> 382 E8 G7 1 -0.3185775271 -3.97867440 #> 383 E8 G7 2 -0.0199906109 -3.48400488 #> 384 E8 G7 3 0.0758326777 -2.61754552 #> 385 E8 G8 1 -0.0525654275 -1.04353357 #> 386 E8 G8 2 0.2460214888 -0.54886405 #> 387 E8 G8 3 0.3418447774 0.31759531 #> 388 E8 G9 1 -0.4492995711 -3.67743213 #> 389 E8 G9 2 -0.1507126549 -3.18276261 #> 390 E8 G9 3 -0.0548893663 -2.31630325 #> 391 E9 G1 1 0.2412583939 4.49235659 #> 392 E9 G1 2 0.3249691720 3.61014342 #> 393 E9 G1 3 -0.0089382223 3.73617387 #> 394 E9 G10 1 0.4357145381 5.48601982 #> 395 E9 G10 2 0.5194253162 4.60380666 #> 396 E9 G10 3 0.1855179219 4.72983711 #> 397 E9 G2 1 0.6523665205 2.31066104 #> 398 E9 G2 2 0.7360772986 1.42844787 #> 399 E9 G2 3 0.4021699043 1.55447833 #> 400 E9 G3 1 0.6703344352 2.50614595 #> 401 E9 G3 2 0.7540452133 1.62393278 #> 402 E9 G3 3 0.4201378190 1.74996324 #> 403 E9 G4 1 0.8525628725 2.12929746 #> 404 E9 G4 2 0.9362736505 1.24708430 #> 405 E9 G4 3 0.6023662563 1.37311475 #> 406 E9 G5 1 0.5506797028 5.18435784 #> 407 E9 G5 2 0.6343904809 4.30214468 #> 408 E9 G5 3 0.3004830867 4.42817513 #> 409 E9 G6 1 0.3839173449 3.20513779 #> 410 E9 G6 2 0.4676281229 2.32292462 #> 411 E9 G6 3 0.1337207287 2.44895507 #> 412 E9 G7 1 0.5090064259 3.27942645 #> 413 E9 G7 2 0.5927172040 2.39721328 #> 414 E9 G7 3 0.2588098097 2.52324374 #> 415 E9 G8 1 0.3876784241 3.74744588 #> 416 E9 G8 2 0.4713892022 2.86523271 #> 417 E9 G8 3 0.1374818079 2.99126317 #> 418 E9 G9 1 -0.3221780050 5.36118557 #> 419 E9 G9 2 -0.2384672269 4.47897241 #> 420 E9 G9 3 -0.5723746212 4.60500286 #>
# Get the ranks for the WAASB index get_model_data(model3, what = "OrWAASB")
#> Class of the model: waasb
#> Variable extracted: OrWAASB
#> # A tibble: 10 x 3 #> gen GY HM #> <fct> <dbl> <dbl> #> 1 G1 2 2 #> 2 G10 10 10 #> 3 G2 4 8 #> 4 G3 1 1 #> 5 G4 6 6 #> 6 G5 5 9 #> 7 G6 3 3 #> 8 G7 8 7 #> 9 G8 7 4 #> 10 G9 9 5
# }