[Stable]

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.

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.

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

by

One variable (factor) to compute the function by. It is a shortcut to dplyr::group_by().This is especially useful, for example, when the researcher want to compute the indexes by mega-environments. In this case, an object of class waasb_grouped is returned. mtsi() can then be used to compute the mtsi index within each mega-environment.

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 = NULL). It must be a character 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("l, h") must be used. Character 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 FALSE. If TRUE an within-environment ANOVA is 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; h2mg 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.

  • formula The formula used to fit the model.

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.

See also

Author

Tiago Olivoto tiagoolivoto@gmail.com

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())
#> Evaluating trait GY |======================                      | 50% 00:00:00 
Evaluating trait HM |============================================| 100% 00:00:01 

#> 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 0.0887
#> 3 GEIr2                0.313 0.397 
#> 4 h2mg                 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.
#> Evaluating trait NR |===============                             | 33% 00:00:00 
Evaluating trait NKR |=============================              | 67% 00:00:01 
Evaluating trait NKE |===========================================| 100% 00:00:01 

#> 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 0.999984
#>       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("h, l"),
                wresp = c(60, 40))
#> Evaluating trait GY |======================                      | 50% 00:00:00 
Evaluating trait HM |============================================| 100% 00:00:01 

#> 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 Likelihood-ratio test
get_model_data(model3, "lrt")
#> Class of the model: waasb
#> Variable extracted: lrt
#> # A tibble: 8 x 8
#>   VAR   model     npar logLik   AIC   LRT    Df `Pr(>Chisq)`
#>   <chr> <chr>    <int>  <dbl> <dbl> <dbl> <dbl>        <dbl>
#> 1 GY    GEN          5  -242.  495. 19.3      1     1.11e- 5
#> 2 GY    REP(ENV)     5  -247.  504. 28.4      1     9.91e- 8
#> 3 GY    ENV          5  -267.  545. 69.3      1     8.26e-17
#> 4 GY    GEN:ENV      5  -255.  520. 44.8      1     2.15e-11
#> 5 HM    GEN          5  -948. 1905.  7.86     1     5.07e- 3
#> 6 HM    REP(ENV)     5  -952. 1913. 16.2      1     5.73e- 5
#> 7 HM    ENV          5  -977. 1964. 66.5      1     3.55e-16
#> 8 HM    GEN:ENV      5  -975. 1960. 62.8      1     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 -0.14987968 -0.6482287
#> 2  E10 -0.48707736 -3.6654480
#> 3  E11 -1.27469053  5.9119251
#> 4  E12 -1.04019833  1.4594707
#> 5  E13  0.22975006 -1.4787300
#> 6  E14 -0.87093188 -6.8278033
#> 7   E2  0.49360211 -3.8392888
#> 8   E3  1.35679000  4.6556912
#> 9   E4  0.97704983  1.8497256
#> 10  E5  1.20664196  4.0080614
#> 11  E6 -0.01076395 -2.1270048
#> 12  E7 -0.66892730  0.3935263
#> 13  E8 -0.13456328 -2.8317214
#> 14  E9  0.37319834  3.1398247
#> 
#> $GEN
#>    GEN          GY          HM
#> 1   G1 -0.05752492 -0.69204146
#> 2  G10 -0.16550227  0.28692648
#> 3   G2  0.05698665 -0.98024230
#> 4   G3  0.22915421 -0.33244392
#> 5   G4 -0.02635233 -0.03868819
#> 6   G5 -0.11159841  0.82983544
#> 7   G6 -0.11429998  0.44131979
#> 8   G7  0.05434733 -0.08378083
#> 9   G8  0.26852291  0.69586453
#> 10  G9 -0.13373318 -0.12674955
#> 
#> $ENV_GEN
#>     ENV GEN            GY          HM
#> 1    E1  G1 -6.330189e-02 -0.14938119
#> 2    E1 G10 -2.442200e-01 -0.56288436
#> 3    E1  G2  2.054257e-01 -0.78360825
#> 4    E1  G3  8.725552e-02 -0.84723001
#> 5    E1  G4  5.887247e-02  0.62785605
#> 6    E1  G5 -1.420192e-01  1.13154021
#> 7    E1  G6 -6.853784e-02  0.26249589
#> 8    E1  G7  1.254794e-01  0.07058045
#> 9    E1  G8  6.902230e-02 -0.11301281
#> 10   E1  G9 -4.012520e-02  0.26347515
#> 11  E10  G1  1.171687e-01  1.14789445
#> 12  E10 G10 -3.058357e-01 -2.09536147
#> 13  E10  G2  3.740679e-02  2.04722584
#> 14  E10  G3 -4.707532e-02  0.19871891
#> 15  E10  G4  7.845677e-03 -1.86804017
#> 16  E10  G5  4.465347e-02  4.04149567
#> 17  E10  G6  8.993460e-02  1.05479099
#> 18  E10  G7  1.314438e-01 -2.30197951
#> 19  E10  G8  7.966694e-02 -0.98459372
#> 20  E10  G9 -1.946901e-01 -1.80656190
#> 21  E11  G1  1.831440e-02  0.43602759
#> 22  E11 G10 -2.039561e-01 -0.24741909
#> 23  E11  G2  3.141242e-02 -0.29361054
#> 24  E11  G3 -2.926489e-02 -0.28043802
#> 25  E11  G4  7.577213e-03 -0.02009779
#> 26  E11  G5  3.366435e-02 -2.09251257
#> 27  E11  G6  1.469719e-01  1.27376385
#> 28  E11  G7 -4.749242e-02  0.60479305
#> 29  E11  G8  1.941038e-02  1.09605859
#> 30  E11  G9 -7.996014e-02  0.43698735
#> 31  E12  G1 -1.414754e-01  0.72908906
#> 32  E12 G10 -2.767930e-01  0.44124926
#> 33  E12  G2  2.006050e-01 -2.73489075
#> 34  E12  G3 -5.517517e-02  1.00164065
#> 35  E12  G4 -4.339061e-02  0.09843123
#> 36  E12  G5  1.140480e-01 -0.88024688
#> 37  E12  G6 -7.764339e-02  0.84542454
#> 38  E12  G7  1.756964e-01  0.65350910
#> 39  E12  G8  6.997427e-02  0.28374789
#> 40  E12  G9 -5.016168e-02 -0.21242639
#> 41  E13  G1  9.505381e-02 -1.04767597
#> 42  E13 G10 -5.805871e-01  1.58499385
#> 43  E13  G2  4.119831e-02 -2.28927595
#> 44  E13  G3  2.125112e-01 -0.07699459
#> 45  E13  G4 -1.559906e-01 -0.02609330
#> 46  E13  G5 -1.447500e-01  0.17041375
#> 47  E13  G6  7.604131e-02 -1.30367639
#> 48  E13  G7  1.420521e-01  0.97113344
#> 49  E13  G8  2.198175e-01  1.33440851
#> 50  E13  G9  1.132764e-01  0.45426284
#> 51  E14  G1 -1.288945e-01  0.16935133
#> 52  E14 G10  1.477432e-01  0.53309934
#> 53  E14  G2 -2.699791e-01 -0.59519329
#> 54  E14  G3  2.390803e-02 -0.44239481
#> 55  E14  G4  6.037118e-02 -0.08897061
#> 56  E14  G5  6.560550e-02 -0.54405137
#> 57  E14  G6  7.660913e-02 -1.23856324
#> 58  E14  G7  5.673691e-02  2.07180578
#> 59  E14  G8 -4.441949e-02 -0.86940016
#> 60  E14  G9 -5.827615e-02 -0.05076334
#> 61   E2  G1 -4.812136e-02 -0.65619943
#> 62   E2 G10  8.777472e-02  0.45454745
#> 63   E2  G2  2.438918e-04  0.16168348
#> 64   E2  G3  1.309954e-01 -1.08643183
#> 65   E2  G4  2.725744e-02 -0.27689616
#> 66   E2  G5  4.976748e-02  1.29027237
#> 67   E2  G6  1.477954e-01  1.13564755
#> 68   E2  G7 -3.923678e-01  0.77152675
#> 69   E2  G8  1.651184e-03  0.44132624
#> 70   E2  G9  3.501367e-02 -2.82875042
#> 71   E3  G1  5.787934e-02 -1.00407875
#> 72   E3 G10  1.484082e-01  1.80312352
#> 73   E3  G2  2.962388e-01 -0.33745730
#> 74   E3  G3 -9.175877e-02 -1.02241457
#> 75   E3  G4 -1.061012e-01  0.40147531
#> 76   E3  G5 -1.237979e-01  0.26055295
#> 77   E3  G6 -3.176147e-01  0.29907737
#> 78   E3  G7 -6.339002e-05  0.66566576
#> 79   E3  G8 -1.311017e-01 -1.27488747
#> 80   E3  G9  3.778891e-01  0.92837344
#> 81   E4  G1 -7.077286e-02 -1.11725149
#> 82   E4 G10  4.933014e-01  1.22453092
#> 83   E4  G2 -2.719766e-03 -1.38146976
#> 84   E4  G3  1.543021e-01  0.02796234
#> 85   E4  G4 -2.131803e-01  0.05559264
#> 86   E4  G5 -1.097616e-01  0.61280008
#> 87   E4  G6 -9.250639e-02  0.65132449
#> 88   E4  G7 -4.452217e-01 -0.14563677
#> 89   E4  G8  1.316054e-01  0.00819937
#> 90   E4  G9  2.341506e-01  0.34978084
#> 91   E5  G1  2.122633e-01 -0.74460828
#> 92   E5 G10 -2.320916e-01  1.93460353
#> 93   E5  G2 -7.952208e-02  1.83023460
#> 94   E5  G3  1.491600e-03  1.66887467
#> 95   E5  G4 -5.496584e-02 -1.99194743
#> 96   E5  G5 -2.005740e-01  0.32221998
#> 97   E5  G6 -1.351357e-01 -0.54682433
#> 98   E5  G7  6.274612e-02 -1.62303751
#> 99   E5  G8  4.134479e-01 -0.16602576
#> 100  E5  G9  1.101473e-01 -0.06413552
#> 101  E6  G1  1.296050e-01 -0.75284356
#> 102  E6 G10 -8.271063e-03 -3.49577313
#> 103  E6  G2 -1.141379e-01  0.18139431
#> 104  E6  G3  5.879663e-02  0.76470616
#> 105  E6  G4  4.092753e-02  2.35149299
#> 106  E6  G5 -7.551500e-02 -0.04671569
#> 107  E6  G6 -1.361304e-01  0.08489270
#> 108  E6  G7 -3.302693e-02 -0.44445214
#> 109  E6  G8 -1.514359e-02  1.47797947
#> 110  E6  G9  1.520233e-01 -0.44936089
#> 111  E7  G1 -2.677701e-02 -0.55448824
#> 112  E7 G10  2.625304e-01  0.85645446
#> 113  E7  G2 -4.256781e-02  0.11213322
#> 114  E7  G3 -4.336330e-02 -0.80553399
#> 115  E7  G4  6.273883e-03  0.32746848
#> 116  E7  G5 -1.460389e-01 -0.91882604
#> 117  E7  G6 -7.990311e-02  0.42287398
#> 118  E7  G7  3.182190e-01 -0.89768462
#> 119  E7  G8 -2.641382e-03  0.80367256
#> 120  E7  G9 -2.999531e-01  0.71474065
#> 121  E8  G1 -1.330272e-01 -1.01156942
#> 122  E8 G10  2.089674e-01 -2.62585583
#> 123  E8  G2 -3.505530e-01  0.13210739
#> 124  E8  G3  5.303763e-02  0.29654136
#> 125  E8  G4 -1.346314e-01  1.79024423
#> 126  E8  G5  1.797106e-01 -0.27053506
#> 127  E8  G6  7.370585e-02 -0.01093621
#> 128  E8  G7 -5.770708e-03 -0.41229058
#> 129  E8  G8  4.606493e-02  1.74285096
#> 130  E8  G9  5.158855e-02 -0.06813444
#> 131  E9  G1 -1.343255e-01  1.46249327
#> 132  E9 G10  1.681085e-01  1.47717638
#> 133  E9  G2  1.622707e-01 -0.43069451
#> 134  E9  G3  8.070248e-03 -0.88294186
#> 135  E9  G4  4.458064e-01 -1.55344135
#> 136  E9  G5  2.291696e-01  0.63273560
#> 137  E9  G6  6.510880e-02 -0.95770950
#> 138  E9  G7  2.154986e-02 -0.35841118
#> 139  E9  G8 -3.139547e-01 -0.66999490
#> 140  E9  G9 -6.215535e-01  1.76597594
#> 
#> $ENV_REP
#>    ENV REP          GY           HM
#> 1   E1   1  0.03884817  0.006904938
#> 2   E1   2  0.02419072 -0.365436229
#> 3   E1   3 -0.06835907  0.336404819
#> 4  E10   1  0.09511632 -0.671724046
#> 5  E10   2  0.10576176  0.525311852
#> 6  E10   3 -0.21816755  0.021296737
#> 7  E11   1 -0.01425215 -0.060838465
#> 8  E11   2  0.12431270  0.446326745
#> 9  E11   3 -0.15530741 -0.183692149
#> 10 E12   1 -0.05274119 -0.211251101
#> 11 E12   2  0.06736219 -0.135648834
#> 12 E12   3 -0.05154424  0.396717131
#> 13 E13   1  0.03192165  0.890402344
#> 14 E13   2  0.04587457  0.156430333
#> 15 E13   3 -0.06964093 -1.097307266
#> 16 E14   1  0.03192169  0.406378357
#> 17 E14   2  0.03203099  0.151220705
#> 18 E14   3 -0.09486758 -0.790657542
#> 19  E2   1 -0.21319506 -0.549168258
#> 20  E2   2  0.12928783 -0.063423690
#> 21  E2   3  0.10142831  0.481542653
#> 22  E3   1  0.19690854 -0.010029835
#> 23  E3   2 -0.10237288  0.619989059
#> 24  E3   3 -0.04637457 -0.451043061
#> 25  E4   1  0.06532710 -0.293963427
#> 26  E4   2  0.04684910  0.273053577
#> 27  E4   3 -0.07749449  0.084047909
#> 28  E5   1  0.01046233  0.001082043
#> 29  E5   2  0.12260290  0.588259653
#> 30  E5   3 -0.09023385 -0.452531560
#> 31  E6   1  0.10343813 -0.830625062
#> 32  E6   2 -0.03651231  0.530215749
#> 33  E6   3 -0.06730791  0.227806680
#> 34  E7   1 -0.14554541 -0.187678254
#> 35  E7   2 -0.01347147  0.117880909
#> 36  E7   3  0.13527241  0.083229870
#> 37  E8   1 -0.23258781 -0.650687631
#> 38  E8   2  0.06599471 -0.156122799
#> 39  E8   3  0.16181659  0.710153180
#> 40  E9   1  0.05991019  0.581741031
#> 41  E9   2  0.14361974 -0.300285421
#> 42  E9   3 -0.19028275 -0.174281642
#> 
#> $ENV_GEN_REP
#>     ENV GEN REP            GY          HM
#> 1    E1  G1   1 -0.2318583184 -1.48274643
#> 2    E1  G1   2 -0.2465157665 -1.85508760
#> 3    E1  G1   3 -0.3390655594 -1.15324655
#> 4    E1 G10   1 -0.5207538309 -0.91728165
#> 5    E1 G10   2 -0.5354112790 -1.28962282
#> 6    E1 G10   3 -0.6279610720 -0.58778177
#> 7    E1  G2   1  0.1513807970 -2.40517433
#> 8    E1  G2   2  0.1367233490 -2.77751549
#> 9    E1  G2   3  0.0441735560 -2.07567445
#> 10   E1  G3   1  0.2053782167 -1.82099771
#> 11   E1  G3   2  0.1907207686 -2.19333888
#> 12   E1  G3   3  0.0981709757 -1.49149783
#> 13   E1  G4   1 -0.0785113779 -0.05215591
#> 14   E1  G4   2 -0.0931688259 -0.42449708
#> 15   E1  G4   3 -0.1857186189  0.27734397
#> 16   E1  G5   1 -0.3646490797  1.32005187
#> 17   E1  G5   2 -0.3793065277  0.94771070
#> 18   E1  G5   3 -0.4718563207  1.64955175
#> 19   E1  G6   1 -0.2938693357  0.06249191
#> 20   E1  G6   2 -0.3085267837 -0.30984926
#> 21   E1  G6   3 -0.4010765767  0.39199179
#> 22   E1  G7   1  0.0687951758 -0.65452416
#> 23   E1  G7   2  0.0541377278 -1.02686533
#> 24   E1  G7   3 -0.0384120652 -0.32502428
#> 25   E1  G8   1  0.2265136940 -0.05847206
#> 26   E1  G8   2  0.2118562459 -0.43081323
#> 27   E1  G8   3  0.1193064530  0.27102782
#> 28   E1  G9   1 -0.2848898933 -0.50459819
#> 29   E1  G9   2 -0.2995473413 -0.87693935
#> 30   E1  G9   3 -0.3920971343 -0.17509830
#> 31  E10  G1   1 -0.3323172181 -3.88131900
#> 32  E10  G1   2 -0.3216717822 -2.68428311
#> 33  E10  G1   3 -0.6456010960 -3.18829822
#> 34  E10 G10   1 -0.8632990332 -6.14560699
#> 35  E10 G10   2 -0.8526535973 -4.94857109
#> 36  E10 G10   3 -1.1765829111 -5.45258620
#> 37  E10  G2   1 -0.2975676052 -3.27018846
#> 38  E10  G2   2 -0.2869221693 -2.07315256
#> 39  E10  G2   3 -0.6108514832 -2.57716768
#> 40  E10  G3   1 -0.2098821444 -4.47089701
#> 41  E10  G3   2 -0.1992367085 -3.27386112
#> 42  E10  G3   3 -0.5231660223 -3.77787623
#> 43  E10  G4   1 -0.4104676955 -6.24390035
#> 44  E10  G4   2 -0.3998222596 -5.04686446
#> 45  E10  G4   3 -0.7237515735 -5.55087957
#> 46  E10  G5   1 -0.4589059791  0.53415911
#> 47  E10  G5   2 -0.4482605432  1.73119501
#> 48  E10  G5   3 -0.7721898570  1.22717989
#> 49  E10  G6   1 -0.4163264200 -2.84106122
#> 50  E10  G6   2 -0.4056809841 -1.64402532
#> 51  E10  G6   3 -0.7296102979 -2.14804043
#> 52  E10  G7   1 -0.2061699029 -6.72293234
#> 53  E10  G7   2 -0.1955244670 -5.52589644
#> 54  E10  G7   3 -0.5194537808 -6.02991156
#> 55  E10  G8   1 -0.0437711912 -4.62590119
#> 56  E10  G8   2 -0.0331257553 -3.42886529
#> 57  E10  G8   3 -0.3570550691 -3.93288041
#> 58  E10  G9   1 -0.7203843334 -6.27048346
#> 59  E10  G9   2 -0.7097388975 -5.07344756
#> 60  E10  G9   3 -1.0336682113 -5.57746267
#> 61  E11  G1   1 -1.3281531975  5.59507281
#> 62  E11  G1   2 -1.1895883459  6.10223802
#> 63  E11  G1   3 -1.4692084620  5.47221913
#> 64  E11 G10   1 -1.6584010463  5.89059407
#> 65  E11 G10   2 -1.5198361947  6.39775928
#> 66  E11 G10   3 -1.7994563108  5.76774038
#> 67  E11  G2   1 -1.2005436148  4.57723384
#> 68  E11  G2   2 -1.0619787632  5.08439905
#> 69  E11  G2   3 -1.3415988794  4.45438015
#> 70  E11  G3   1 -1.0890533551  5.23820473
#> 71  E11  G3   2 -0.9504885035  5.74536994
#> 72  E11  G3   3 -1.2301086197  5.11535105
#> 73  E11  G4   1 -1.3077178008  5.79230070
#> 74  E11  G4   2 -1.1691529492  6.29946591
#> 75  E11  G4   3 -1.4487730654  5.66944701
#> 76  E11  G5   1 -1.3668767455  4.58840954
#> 77  E11  G5   2 -1.2283118940  5.09557475
#> 78  E11  G5   3 -1.5079320101  4.46555586
#> 79  E11  G6   1 -1.2562707229  7.56617032
#> 80  E11  G6   2 -1.1177058713  8.07333553
#> 81  E11  G6   3 -1.3973259875  7.44331664
#> 82  E11  G7   1 -1.2820877761  6.37209889
#> 83  E11  G7   2 -1.1435229245  6.87926410
#> 84  E11  G7   3 -1.4231430407  6.24924521
#> 85  E11  G8   1 -1.0010093896  7.64300979
#> 86  E11  G8   2 -0.8624445380  8.15017500
#> 87  E11  G8   3 -1.1420646541  7.52015611
#> 88  E11  G9   1 -1.5026360059  6.16132448
#> 89  E11  G9   2 -1.3640711543  6.66848969
#> 90  E11  G9   3 -1.6436912705  6.03847079
#> 91  E12  G1   1 -1.2919397968  1.28526717
#> 92  E12  G1   2 -1.1718364178  1.36086944
#> 93  E12  G1   3 -1.2907428417  1.89323540
#> 94  E12 G10   1 -1.5352347926  1.97639531
#> 95  E12 G10   2 -1.4151314136  2.05199758
#> 96  E12 G10   3 -1.5340378375  2.58436354
#> 97  E12  G2   1 -0.8353478940 -2.46691348
#> 98  E12  G2   2 -0.7152445150 -2.39131122
#> 99  E12  G2   3 -0.8341509389 -1.85894525
#> 100 E12  G3   1 -0.9189604747  1.91741630
#> 101 E12  G3   2 -0.7988570957  1.99301857
#> 102 E12  G3   3 -0.9177635196  2.52538453
#> 103 E12  G4   1 -1.1626824625  1.30796261
#> 104 E12  G4   2 -1.0425790835  1.38356488
#> 105 E12  G4   3 -1.1614855075  1.91593084
#> 106 E12  G5   1 -1.0904899279  1.19780813
#> 107 E12  G5   2 -0.9703865489  1.27341040
#> 108 E12  G5   3 -1.0892929728  1.80577636
#> 109 E12  G6   1 -1.2848828906  2.53496390
#> 110 E12  G6   2 -1.1647795116  2.61056616
#> 111 E12  G6   3 -1.2836859355  3.14293213
#> 112 E12  G7   1 -0.8628957812  1.81794783
#> 113 E12  G7   2 -0.7427924022  1.89355010
#> 114 E12  G7   3 -0.8616988261  2.42591606
#> 115 E12  G8   1 -0.7544423334  2.22783198
#> 116 E12  G8   2 -0.6343389544  2.30343425
#> 117 E12  G8   3 -0.7532453783  2.83580022
#> 118 E12  G9   1 -1.2768343858  0.90904362
#> 119 E12  G9   2 -1.1567310068  0.98464589
#> 120 E12  G9   3 -1.2756374307  1.51701185
#> 121 E13  G1   1  0.2992006033 -2.32804509
#> 122 E13  G1   2  0.3131535273 -3.06201710
#> 123 E13  G1   3  0.1976380318 -4.31575470
#> 124 E13 G10   1 -0.4844176690  1.28359268
#> 125 E13 G10   2 -0.4704647450  0.54962067
#> 126 E13 G10   3 -0.5859802405 -0.70411693
#> 127 E13  G2   1  0.3598566702 -3.85784591
#> 128 E13  G2   2  0.3738095942 -4.59181792
#> 129 E13  G2   3  0.2582940987 -5.84555552
#> 130 E13  G3   1  0.7033371324 -0.99776617
#> 131 E13  G3   2  0.7172900564 -1.73173818
#> 132 E13  G3   3  0.6017745609 -2.98547578
#> 133 E13  G4   1  0.0793287597 -0.65310914
#> 134 E13  G4   2  0.0932816837 -1.38708115
#> 135 E13  G4   3 -0.0222338118 -2.64081875
#> 136 E13  G5   1  0.0053233035  0.41192153
#> 137 E13  G5   2  0.0192762276 -0.32205048
#> 138 E13  G5   3 -0.0962392679 -1.57578808
#> 139 E13  G6   1  0.2234130310 -1.45068425
#> 140 E13  G6   2  0.2373659550 -2.18465626
#> 141 E13  G6   3  0.1218504595 -3.43839386
#> 142 E13  G7   1  0.4580711727  0.29902494
#> 143 E13  G7   2  0.4720240967 -0.43494707
#> 144 E13  G7   3  0.3565086012 -1.68868467
#> 145 E13  G8   1  0.7500120731  1.44194538
#> 146 E13  G8   2  0.7639649971  0.70797337
#> 147 E13  G8   3  0.6484495016 -0.54576423
#> 148 E13  G9   1  0.2412148908 -0.26081437
#> 149 E13  G9   2  0.2551678149 -0.99478638
#> 150 E13  G9   3  0.1396523194 -2.24852398
#> 151 E14  G1   1 -1.0254295667 -6.94411511
#> 152 E14  G1   2 -1.0253202679 -7.19927276
#> 153 E14  G1   3 -1.1522188328 -8.14115101
#> 154 E14 G10   1 -0.8567692934 -5.60139917
#> 155 E14 G10   2 -0.8566599946 -5.85655682
#> 156 E14 G10   3 -0.9835585596 -6.79843506
#> 157 E14  G2   1 -1.0520026586 -7.99686057
#> 158 E14  G2   2 -1.0518933598 -8.25201822
#> 159 E14  G2   3 -1.1787919247 -9.19389647
#> 160 E14  G3   1 -0.5859479494 -7.19626372
#> 161 E14  G3   2 -0.5858386506 -7.45142137
#> 162 E14  G3   3 -0.7127372155 -8.39329962
#> 163 E14  G4   1 -0.8049913511 -6.54908378
#> 164 E14  G4   2 -0.8048820523 -6.80424143
#> 165 E14  G4   3 -0.9317806173 -7.74611968
#> 166 E14  G5   1 -0.8850031107 -6.13564092
#> 167 E14  G5   2 -0.8848938119 -6.39079857
#> 168 E14  G5   3 -1.0117923769 -7.33267682
#> 169 E14  G6   1 -0.8767010451 -7.21866843
#> 170 E14  G6   2 -0.8765917463 -7.47382608
#> 171 E14  G6   3 -1.0034903112 -8.41570433
#> 172 E14  G7   1 -0.7279259575 -4.43340004
#> 173 E14  G7   2 -0.7278166587 -4.68855770
#> 174 E14  G7   3 -0.8547152236 -5.63043594
#> 175 E14  G8   1 -0.6149067744 -6.59496062
#> 176 E14  G8   2 -0.6147974756 -6.85011827
#> 177 E14  G8   3 -0.7416960405 -7.79199652
#> 178 E14  G9   1 -1.0310195314 -6.59893788
#> 179 E14  G9   2 -1.0309102326 -6.85409553
#> 180 E14  G9   3 -1.1578087976 -7.79597378
#> 181  E2  G1   1  0.1747607648 -5.73669795
#> 182  E2  G1   2  0.5172436554 -5.25095339
#> 183  E2  G1   3  0.4893841394 -4.70598704
#> 184  E2 G10   1  0.2026794944 -3.64698313
#> 185  E2 G10   2  0.5451623850 -3.16123856
#> 186  E2 G10   3  0.5173028690 -2.61627222
#> 187  E2  G2   1  0.3376375820 -5.20701588
#> 188  E2  G2   2  0.6801204726 -4.72127132
#> 189  E2  G2   3  0.6522609566 -4.17630497
#> 190  E2  G3   1  0.6405566822 -5.80733282
#> 191  E2  G3   2  0.9830395728 -5.32158825
#> 192  E2  G3   3  0.9551800568 -4.77662191
#> 193  E2  G4   1  0.2813121467 -4.70404141
#> 194  E2  G4   2  0.6237950373 -4.21829685
#> 195  E2  G4   3  0.5959355213 -3.67333050
#> 196  E2  G5   1  0.2185761058 -2.26834925
#> 197  E2  G5   2  0.5610589965 -1.78260469
#> 198  E2  G5   3  0.5331994805 -1.23763834
#> 199  E2  G6   1  0.3139024232 -2.81148973
#> 200  E2  G6   2  0.6563853138 -2.32574516
#> 201  E2  G6   3  0.6285257978 -1.78077882
#> 202  E2  G7   1 -0.0576134450 -3.70071114
#> 203  E2  G7   2  0.2848694456 -3.21496658
#> 204  E2  G7   3  0.2570099296 -2.67000023
#> 205  E2  G8   1  0.5505811354 -3.25126630
#> 206  E2  G8   2  0.8930640261 -2.76552173
#> 207  E2  G8   3  0.8652045101 -2.22055539
#> 208  E2  G9   1  0.1816875270 -7.34395704
#> 209  E2  G9   2  0.5241704176 -6.85821247
#> 210  E2  G9   3  0.4963109016 -6.31324613
#> 211  E3  G1   1  1.5540529761  2.94954111
#> 212  E3  G1   2  1.2547715532  3.57956001
#> 213  E3  G1   3  1.3107698570  2.50852789
#> 214  E3 G10   1  1.5366044351  6.73571133
#> 215  E3 G10   2  1.2373230122  7.36573022
#> 216  E3 G10   3  1.2933213159  6.29469810
#> 217  E3  G2   1  1.9069239696  3.32796173
#> 218  E3  G2   2  1.6076425467  3.95798062
#> 219  E3  G2   3  1.6636408505  2.88694850
#> 220  E3  G3   1  1.6910939943  3.29080283
#> 221  E3  G3   2  1.3918125714  3.92082172
#> 222  E3  G3   3  1.4478108751  2.84978960
#> 223  E3  G4   1  1.4212449802  5.00844845
#> 224  E3  G4   2  1.1219635573  5.63846734
#> 225  E3  G4   3  1.1779618610  4.56743522
#> 226  E3  G5   1  1.3183022090  5.73604971
#> 227  E3  G5   2  1.0190207861  6.36606861
#> 228  E3  G5   3  1.0750190898  5.29503649
#> 229  E3  G6   1  1.1217838179  5.38605848
#> 230  E3  G6   2  0.8225023950  6.01607738
#> 231  E3  G6   3  0.8785006987  4.94504526
#> 232  E3  G7   1  1.6079824828  5.22754625
#> 233  E3  G7   2  1.3087010599  5.85756514
#> 234  E3  G7   3  1.3646993637  4.78653302
#> 235  E3  G8   1  1.6911197161  4.06663837
#> 236  E3  G8   2  1.3918382931  4.69665727
#> 237  E3  G8   3  1.4478365969  3.62562515
#> 238  E3  G9   1  1.7978544904  5.44728520
#> 239  E3  G9   2  1.4985730675  6.07730410
#> 240  E3  G9   3  1.5545713712  5.00627198
#> 241  E4  G1   1  0.9140791518 -0.25353073
#> 242  E4  G1   2  0.8956011511  0.31348627
#> 243  E4  G1   3  0.7712575654  0.12448060
#> 244  E4 G10   1  1.3701760736  3.06721962
#> 245  E4 G10   2  1.3516980729  3.63423663
#> 246  E4 G10   3  1.2273544872  3.44523096
#> 247  E4  G2   1  1.0966438129 -0.80594984
#> 248  E4  G2   2  1.0781658122 -0.23893284
#> 249  E4  G2   3  0.9538222265 -0.42793851
#> 250  E4  G3   1  1.4258332090  1.25128063
#> 251  E4  G3   2  1.4073552083  1.81829764
#> 252  E4  G3   3  1.2830116226  1.62929197
#> 253  E4  G4   1  0.8028442548  1.57266667
#> 254  E4  G4   2  0.7843662541  2.13968367
#> 255  E4  G4   3  0.6600226684  1.95067801
#> 256  E4  G5   1  0.8210168994  2.99839773
#> 257  E4  G5   2  0.8025388987  3.56541473
#> 258  E4  G5   3  0.6781953130  3.37640907
#> 259  E4  G6   1  0.8355705587  2.64840650
#> 260  E4  G6   2  0.8170925580  3.21542350
#> 261  E4  G6   3  0.6927489723  3.02641784
#> 262  E4  G7   1  0.6515026035  1.32634461
#> 263  E4  G7   2  0.6330246028  1.89336162
#> 264  E4  G7   3  0.5086810171  1.70435595
#> 265  E4  G8   1  1.4425052865  2.25982611
#> 266  E4  G8   2  1.4240272858  2.82684312
#> 267  E4  G8   3  1.2996837001  2.63783745
#> 268  E4  G9   1  1.1427943747  1.77879350
#> 269  E4  G9   2  1.1243163740  2.34581050
#> 270  E4  G9   3  0.9999727883  2.15680483
#> 271  E5  G1   1  1.3718427125  2.57249372
#> 272  E5  G1   2  1.4839832745  3.15967133
#> 273  E5  G1   3  1.2711465339  2.11888011
#> 274  E5 G10   1  0.8195104174  6.23067347
#> 275  E5 G10   2  0.9316509794  6.81785108
#> 276  E5 G10   3  0.7188142388  5.77705987
#> 277  E5  G2   1  1.1945688702  4.85913576
#> 278  E5  G2   2  1.3067094322  5.44631337
#> 279  E5  G2   3  1.0938726916  4.40552216
#> 280  E5  G3   1  1.4477501103  5.34557421
#> 281  E5  G3   2  1.5598906724  5.93275182
#> 282  E5  G3   3  1.3470539317  4.89196060
#> 283  E5  G4   1  1.1357861247  1.97850784
#> 284  E5  G4   2  1.2479266867  2.56568545
#> 285  E5  G4   3  1.0350899461  1.52489424
#> 286  E5  G5   1  0.9049319195  5.16119888
#> 287  E5  G5   2  1.0170724815  5.74837649
#> 288  E5  G5   3  0.8042357409  4.70758527
#> 289  E5  G6   1  0.9676685698  3.90363892
#> 290  E5  G6   2  1.0798091318  4.49081653
#> 291  E5  G6   3  0.8669723912  3.45002532
#> 292  E5  G7   1  1.3341977400  2.30232511
#> 293  E5  G7   2  1.4463383020  2.88950272
#> 294  E5  G7   3  1.2335015614  1.84871151
#> 295  E5  G8   1  1.8990751436  4.53898222
#> 296  E5  G8   2  2.0112157057  5.12615983
#> 297  E5  G8   3  1.7983789650  4.08536862
#> 298  E5  G9   1  1.1935183889  3.81825838
#> 299  E5  G9   2  1.3056589509  4.40543599
#> 300  E5  G9   3  1.0928222103  3.36464478
#> 301  E6  G1   1  0.1647542342 -4.40251488
#> 302  E6  G1   2  0.0248037939 -3.04167406
#> 303  E6  G1   3 -0.0059918076 -3.34408313
#> 304  E6 G10   1 -0.0810991476 -6.16647651
#> 305  E6 G10   2 -0.2210495880 -4.80563569
#> 306  E6 G10   3 -0.2518451895 -5.10804476
#> 307  E6  G2   1  0.0355229473 -3.75647784
#> 308  E6  G2   2 -0.1044274931 -2.39563703
#> 309  E6  G2   3 -0.1352230946 -2.69804610
#> 310  E6  G3   1  0.3806250327 -2.52536762
#> 311  E6  G3   2  0.2406745923 -1.16452681
#> 312  E6  G3   3  0.2098789908 -1.46693588
#> 313  E6  G4   1  0.1072493817 -0.64482505
#> 314  E6  G4   2 -0.0327010586  0.71601576
#> 315  E6  G4   3 -0.0634966601  0.41360669
#> 316  E6  G5   1 -0.0944392217 -2.17451011
#> 317  E6  G5   2 -0.2343896621 -0.81366930
#> 318  E6  G5   3 -0.2651852636 -1.11607837
#> 319  E6  G6   1 -0.1577562235 -2.43141737
#> 320  E6  G6   2 -0.2977066639 -1.07057655
#> 321  E6  G6   3 -0.3285022654 -1.37298562
#> 322  E6  G7   1  0.1139945809 -3.48586283
#> 323  E6  G7   2 -0.0259558595 -2.12502202
#> 324  E6  G7   3 -0.0567514610 -2.42743109
#> 325  E6  G8   1  0.3460535022 -0.78378586
#> 326  E6  G8   2  0.2061030618  0.57705495
#> 327  E6  G8   3  0.1753074603  0.27464588
#> 328  E6  G9   1  0.1109642750 -3.53374030
#> 329  E6  G9   2 -0.0289861654 -2.17289949
#> 330  E6  G9   3 -0.0597817669 -2.47530856
#> 331  E7  G1   1 -0.8987746353 -1.04068165
#> 332  E7  G1   2 -0.7667006925 -0.73512248
#> 333  E7  G1   3 -0.6179568176 -0.76977352
#> 334  E7 G10   1 -0.7174446042  1.34922898
#> 335  E7 G10   2 -0.5853706614  1.65478815
#> 336  E7 G10   3 -0.4366267865  1.62013711
#> 337  E7  G2   1 -0.8000538764 -0.66226104
#> 338  E7  G2   2 -0.6679799336 -0.35670187
#> 339  E7  G2   3 -0.5192360587 -0.39135291
#> 340  E7  G3   1 -0.6286817950 -0.93212987
#> 341  E7  G3   2 -0.4966078522 -0.62657070
#> 342  E7  G3   3 -0.3478639773 -0.66122174
#> 343  E7  G4   1 -0.8345511613  0.49462834
#> 344  E7  G4   2 -0.7024772185  0.80018750
#> 345  E7  G4   3 -0.5537333436  0.76553646
#> 346  E7  G5   1 -1.0721100700  0.11685744
#> 347  E7  G5   2 -0.9400361272  0.42241660
#> 348  E7  G5   3 -0.7912922523  0.38776556
#> 349  E7  G6   1 -1.0086758068  1.07004182
#> 350  E7  G6   2 -0.8766018640  1.37560098
#> 351  E7  G6   3 -0.7278579891  1.34094994
#> 352  E7  G7   1 -0.4419063995 -0.77561741
#> 353  E7  G7   2 -0.3098324567 -0.47005825
#> 354  E7  G7   3 -0.1610885818 -0.50470929
#> 355  E7  G8   1 -0.5485911828  1.70538513
#> 356  E7  G8   2 -0.4165172400  2.01094429
#> 357  E7  G8   3 -0.2677733651  1.97629325
#> 358  E7  G9   1 -1.2481589524  0.79383914
#> 359  E7  G9   2 -1.1160850096  1.09939830
#> 360  E7  G9   3 -0.9673411348  1.06474726
#> 361  E8  G1   1 -0.5577031900 -5.18601993
#> 362  E8  G1   2 -0.2591206662 -4.69145509
#> 363  E8  G1   3 -0.1632987872 -3.82517911
#> 364  E8 G10   1 -0.3236859126 -5.82133839
#> 365  E8 G10   2 -0.0251033888 -5.32677356
#> 366  E8 G10   3  0.0707184902 -4.46049758
#> 367  E8  G2   1 -0.6607174742 -4.33054396
#> 368  E8  G2   2 -0.3621349504 -3.83597912
#> 369  E8  G2   3 -0.2663130714 -2.96970314
#> 370  E8  G3   1 -0.0849592469 -3.51831161
#> 371  E8  G3   2  0.2136232769 -3.02374678
#> 372  E8  G3   3  0.3094451559 -2.15747080
#> 373  E8  G4   1 -0.5281348272 -1.73085301
#> 374  E8  G4   2 -0.2295523034 -1.23628818
#> 375  E8  G4   3 -0.1337304244 -0.37001220
#> 376  E8  G5   1 -0.2990389121 -2.92310867
#> 377  E8  G5   2 -0.0004563883 -2.42854384
#> 378  E8  G5   3  0.0953654906 -1.56226786
#> 379  E8  G6   1 -0.4077452260 -3.05202546
#> 380  E8  G6   2 -0.1091627023 -2.55746063
#> 381  E8  G6   3 -0.0133408233 -1.69118465
#> 382  E8  G7   1 -0.3185744736 -3.97848047
#> 383  E8  G7   2 -0.0199919498 -3.48391564
#> 384  E8  G7   3  0.0758299291 -2.61763966
#> 385  E8  G8   1 -0.0525632495 -1.04369357
#> 386  E8  G8   2  0.2460192743 -0.54912874
#> 387  E8  G8   3  0.3418411532  0.31714724
#> 388  E8  G9   1 -0.4492957201 -3.67729304
#> 389  E8  G9   2 -0.1507131963 -3.18272821
#> 390  E8  G9   3 -0.0548913173 -2.31645223
#> 391  E9  G1   1  0.2412581043  4.49201757
#> 392  E9  G1   2  0.3249676509  3.60999112
#> 393  E9  G1   3 -0.0089348313  3.73599490
#> 394  E9 G10   1  0.4357147815  5.48566862
#> 395  E9 G10   2  0.5194243282  4.60364217
#> 396  E9 G10   3  0.1855218460  4.72964595
#> 397  E9  G2   1  0.6523658378  2.31062895
#> 398  E9  G2   2  0.7360753844  1.42860250
#> 399  E9  G2   3  0.4021729022  1.55460627
#> 400  E9  G3   1  0.6703329940  2.50617998
#> 401  E9  G3   2  0.7540425407  1.62415353
#> 402  E9  G3   3  0.4201400585  1.75015731
#> 403  E9  G4   1  0.8525626152  2.12943622
#> 404  E9  G4   2  0.9362721619  1.24740977
#> 405  E9  G4   3  0.6023696797  1.37341355
#> 406  E9  G5   1  0.5506797394  5.18413680
#> 407  E9  G5   2  0.6343892860  4.30211035
#> 408  E9  G5   3  0.3004868038  4.42811412
#> 409  E9  G6   1  0.3839173468  3.20517605
#> 410  E9  G6   2  0.4676268934  2.32314960
#> 411  E9  G6   3  0.1337244113  2.44915338
#> 412  E9  G7   1  0.5090057149  3.27937375
#> 413  E9  G7   2  0.5927152615  2.39734730
#> 414  E9  G7   3  0.2588127794  2.52335108
#> 415  E9  G8   1  0.3876767294  3.74743539
#> 416  E9  G8   2  0.4713862761  2.86540893
#> 417  E9  G8   3  0.1374837939  2.99141271
#> 418  E9  G9   1 -0.3221781140  5.36079215
#> 419  E9  G9   2 -0.2384685674  4.47876570
#> 420  E9  G9   3 -0.5723710495  4.60476947
#> 
# }