• get_model_data() Easily get data from some objects generated in the metan package such as the WAASB and WAASBY indexes (Olivoto et al., 2019a, 2019b) BLUPs, variance components, details of AMMI models and AMMI-based stability statistics.

  • gmd() Is a shortcut to get_model_data.

get_model_data(x, what = NULL, type = "GEN", verbose = TRUE)

gmd(x, what = NULL, type = "GEN", verbose = TRUE)

Arguments

x

An object created with the functions AMMI_indexes, anova_ind, anova_joint, ecovalence, Fox, gai, gamem,gafem, ge_means, ge_reg, performs_ammi, Resende_indexes, Shukla, superiority, waas or waasb.

what

What should be captured from the model. See more in section Details.

type

Chose if the statistics must be show by genotype (type = "GEN", default) or environment (type = "ENV"), when possible.

verbose

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

Value

A tibble showing the values of the variable chosen in argument what.

Details

Bellow are listed the options allowed in the argument what depending on the class of the object

Objects of class AMMI_indexes:

  • "ASV" AMMI stability value.

  • "EV" Averages of the squared eigenvector values.

  • "SIPC" Sums of the absolute value of the IPCA scores.

  • "WAAS" Weighted average of absolute scores (default).

  • "ZA" Absolute value of the relative contribution of IPCAs to the interaction.

Objects of class anova_ind:

  • "MEAN"The mean value of the variable

  • "MSG", "FCG", "PFG" The mean square, F-calculated and P-values for genotype effect, respectively.

  • "MSB", "FCB", "PFB" The mean square, F-calculated and P-values for block effect (in randomized complete block design) or complete replicate (in alpha lattice design), respectively.

  • "MSIB_R", "FCIB_R", "PFIB_R" The mean square, F-calculated and P-values for incomplete blocks within complete replicates, respectively (for alpha lattice design only).

  • "MSE" The mean square of error.

  • "CV" The coefficient of variation.

  • "h2" The broad-sence heritability

  • "MSE" The accucary of selection (square root of h2).

Objects of class anova_joint or gafem:

  • "Sum Sq" Sum of squares.

  • "Mean Sq" Mean Squares.

  • "F value" F-values.

  • "Pr(>F)" P-values.

  • ".fitted" Fitted values (default).

  • ".resid" Residuals.

  • ".stdresid" Standardized residuals.

  • ".se.fit" Standard errors of the fitted values.

  • "details" Details.

Objects of class Annicchiarico and Schmildt:

  • "Sem_rp" The standard error of the relative mean performance (Schmildt).

  • "Mean_rp" The relative performance of the mean.

  • "rank" The rank for genotypic confidence index.

  • "Wi" The genotypic confidence index.

Objects of class ecovalence:

  • "Ecoval" Ecovalence value (default).

  • "Ecov_perc" Ecovalence in percentage value.

  • "rank" Rank for ecovalence.

Objects of class ge_reg:

  • "deviations" The deviations from regression.

  • "RMSE" The Root Mean Square Error.

  • "R2" The r-square of the regression.

  • "slope" The sloop of the regression (default).

Objects of class ge_effects:

  • For objects of class ge_effects no argument what is required.

Objects of class ge_means:

  • "ge_means" Genotype-environment interaction means (default).

  • "env_means" Environment means.

  • "gen_means" Genotype means.

Objects of class Shukla:

  • "rMean" Rank for the mean.

  • "ShuklaVar" Shukla's stablity variance (default).

  • "rShukaVar" Rank for Shukla's stablity variance.

  • "ssiShukaVar" Simultaneous selection index.

Objects of class Fox:

  • "TOP" The proportion of locations at which the genotype occurred in the top third (default).

Objects of class gai:

  • "GAI" The geometric adaptability index (default).

  • "GAI_R" The rank for the GAI values.

Objects of class superiority:

  • "Pi_a" The superiority measure for all environments (default).

  • "R_a" The rank for Pi_a.

  • "Pi_f" The superiority measure for favorable environments.

  • "R_f" The rank for Pi_f.

  • "Pi_u" The superiority measure for unfavorable environments.

  • "R_u" The rank for Pi_u.

Objects of class Huehn:

  • "S1" Mean of the absolute rank differences of a genotype over the n environments (default).

  • "S2" variance among the ranks over the k environments.

  • "S3" Sum of the absolute deviations.

  • "S6" Relative sum of squares of rank for each genotype.

  • "S1_R", "S2_R", "S3_R", and "S6_R", the ranks for S1, S2, S3, and S6, respectively.

Objects of class Thennarasu:

  • "N1" First statistic (default).

  • "N2" Second statistic.

  • "N3" Third statistic.

  • "N4" Fourth statistic.

  • "N1_R", "N2_R", "N3_R", and "N4_R", The ranks for the statistics.

Objects of class performs_ammi:

  • "PC1", "PC2", ..., "PCn" The values for the nth interaction principal component axis.

  • "ipca_ss" Sum of square for each IPCA.

  • "ipca_ms" Mean square for each IPCA.

  • "ipca_fval" F value for each IPCA.

  • "ipca_pval" P-value for for each IPCA.

  • "ipca_expl" Explained sum of square for each IPCA (default).

  • "ipca_accum" Accumulated explained sum of square.

Objects of class waas, waas_means, and waasb:

  • "PC1", "PC2", ..., "PCn" The values for the nth interaction principal component axis.

  • "WAASB" The weighted average of the absolute scores (default for objects of class waas).

  • "PctResp" The rescaled values of the response variable.

  • "PctWAASB" The rescaled values of the WAASB.

  • "wResp" The weight for the response variable.

  • "wWAASB" The weight for the stability.

  • "OrResp" The ranking regarding the response variable.

  • "OrWAASB" The ranking regarding the WAASB.

  • "OrPC1" The ranking regarding the first principal component axix.

  • "WAASBY" The superiority index WAASBY.

  • "OrWAASBY" The ranking regarding the superiority index.

Objects of class waasb:

  • "blupg" For genotype's predicted mean.

  • "blupge" for genotype-vs-environment's predicted mean.

  • "genpar" Genetic parameters (default).

  • "lrt" The statistic for the likelihood-ratio test for random effects.

  • "pval_lrt" The p-values for the likelihood-ratio test.

  • "vcomp" The variance components for random effects.

  • "ranef" Random effects.

Objects of class gamem:

  • "blupg" For genotype's predicted mean.

  • "genpar" Genetic parameters (default).

  • "lrt" The statistic for the likelihood-ratio test for random effects.

  • "pval_lrt" The p-values for the likelihood-ratio test.

  • "vcomp" The variance components for random effects.

  • "ranef" Random effects.

Objects of class Res_ind

  • "HMGV" For harmonic mean of genotypic values.

  • "RPGV or RPGV_Y" For relative performance of genotypic values

  • "HMRPGV" For harmonic mean of relative performance of genotypic values

References

Annicchiarico, P. 1992. Cultivar adaptation and recommendation from alfalfa trials in Northern Italy. J. Genet. Breed. 46:269-278.

Dias, P.C., A. Xavier, M.D.V. de Resende, M.H.P. Barbosa, F.A. Biernaski, R.A. Estopa. 2018. Genetic evaluation of Pinus taeda clones from somatic embryogenesis and their genotype x environment interaction. Crop Breed. Appl. Biotechnol. 18:55-64. doi:10.1590/1984-70332018v18n1a8

Azevedo Peixoto, L. de, P.E. Teodoro, L.A. Silva, E.V. Rodrigues, B.G. Laviola, and L.L. Bhering. 2018. Jatropha half-sib family selection with high adaptability and genotypic stability. PLoS One 13:e0199880. doi:10.1371/journal.pone.0199880

Eberhart, S.A., and W.A. Russell. 1966. Stability parameters for comparing Varieties. Crop Sci. 6:36-40. doi:10.2135/cropsci1966.0011183X000600010011x.

Fox, P.N., B. Skovmand, B.K. Thompson, H.J. Braun, and R. Cormier. 1990. Yield and adaptation of hexaploid spring triticale. Euphytica 47:57-64. doi:10.1007/BF00040364.

Huehn, V.M. 1979. Beitrage zur erfassung der phanotypischen stabilitat. EDV Med. Biol. 10:112.

Olivoto, T., A.D.C. L\'ucio, J.A.G. da silva, V.S. Marchioro, V.Q. de Souza, and E. Jost. 2019a. 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

Olivoto, T., A.D.C. L\'ucio, J.A.G. da silva, B.G. Sari, and M.I. Diel. 2019b. Mean performance and stability in multi-environment trials II: Selection based on multiple traits. Agron. J. 111:2961-2969. doi:10.2134/agronj2019.03.0221

Purchase, J.L., H. Hatting, and C.S. van Deventer. 2000. Genotype vs environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. South African J. Plant Soil 17:101-107. doi:10.1080/02571862.2000.10634878

Resende MDV (2007) Matematica e estatistica na analise de experimentos e no melhoramento genetico. Embrapa Florestas, Colombo

Sneller, C.H., L. Kilgore-Norquest, and D. Dombek. 1997. Repeatability of Yield Stability Statistics in Soybean. Crop Sci. 37:383-390. doi:10.2135/cropsci1997.0011183X003700020013x

Shahbazi, E. 2019. Genotype selection and stability analysis for seed yield of Nigella sativa using parametric and non-parametric statistics. Sci. Hortic. (Amsterdam). 253:172-179. doi:10.1016/j.scienta.2019.04.047.

Wricke, G. 1965. Zur berechnung der okovalenz bei sommerweizen und hafer. Z. Pflanzenzuchtg 52:127-138.

Zali, H., E. Farshadfar, S.H. Sabaghpour, and R. Karimizadeh. 2012. Evaluation of genotype vs environment interaction in chickpea using measures of stability from AMMI model. Ann. Biol. Res. 3:3126-3136.

See also

Examples

# \donttest{ library(metan) #################### joint-regression analysis ##################### ge_r <- ge_reg(data_ge2, ENV, GEN, REP, resp = c(PH, EH, CD, CL, ED)) get_model_data(ge_r)
#> Class of the model: ge_reg
#> Variable extracted: slope
#> # A tibble: 13 x 6 #> gen PH EH CD CL ED #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 H1 0.806 1.06 -0.0594 -2.19 0.0280 #> 2 H10 1.22 1.30 2.31 4.63 2.47 #> 3 H11 1.08 1.00 2.74 2.37 0.707 #> 4 H12 0.465 0.590 1.73 2.94 0.808 #> 5 H13 0.306 0.575 -0.661 0.129 0.636 #> 6 H2 0.963 0.525 -1.26 -3.86 -0.0534 #> 7 H3 1.35 1.15 1.40 -1.33 0.987 #> 8 H4 1.27 1.41 0.555 -0.393 1.20 #> 9 H5 1.17 1.30 0.356 0.486 0.197 #> 10 H6 0.936 0.780 3.00 1.60 0.940 #> 11 H7 0.992 0.950 -0.0386 1.48 0.797 #> 12 H8 1.01 0.886 0.903 3.73 1.89 #> 13 H9 1.42 1.48 2.04 3.42 2.40
get_model_data(ge_r, "deviations")
#> Class of the model: ge_reg
#> Variable extracted: deviations
#> # A tibble: 13 x 6 #> gen PH EH CD CL ED #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 H1 0.0710 0.0637 -0.268 -0.176 -0.448 #> 2 H10 0.0318 0.0303 -0.0566 0.576 -0.506 #> 3 H11 0.0278 0.0256 -0.275 0.984 4.35 #> 4 H12 0.0676 0.0861 0.152 0.300 0.475 #> 5 H13 0.0609 0.0298 0.450 9.10 5.48 #> 6 H2 0.0815 0.0981 0.237 1.18 6.78 #> 7 H3 0.0898 0.0870 1.13 0.327 1.65 #> 8 H4 0.0515 0.0335 0.317 1.50 2.16 #> 9 H5 0.0159 0.00449 -0.117 4.37 1.77 #> 10 H6 0.0481 0.0106 1.31 5.90 0.844 #> 11 H7 0.0328 0.0155 1.41 1.79 1.12 #> 12 H8 0.0767 0.0696 1.72 6.05 1.03 #> 13 H9 0.0212 -0.00212 1.03 0.713 -0.779
#################### AMMI model ##################### # Fit an AMMI model for 7 variables. AMMI <- data_ge2 %>% performs_ammi(ENV, GEN, REP, resp = c(PH, ED, TKW, NKR, CD, CL, CW))
#> New names: #> * `` -> ...9
#> variable PH #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 3 7.719 2.5728 127.913 4.25e-07 . . #> REP(ENV) 8 0.161 0.0201 0.897 5.22e-01 . . #> GEN 12 1.865 0.1554 6.929 6.89e-09 . . #> GEN:ENV 36 5.397 0.1499 6.686 5.01e-14 . . #> PC1 14 4.466 0.3190 14.230 0.00e+00 82.8 82.8 #> PC2 12 0.653 0.0545 2.430 8.40e-03 12.1 94.9 #> PC3 10 0.277 0.0277 1.240 2.76e-01 5.1 100 #> Residuals 96 2.153 0.0224 NA NA . . #> Total 155 17.294 0.1116 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #>
#> New names: #> * `` -> ...9
#> variable ED #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 3 306.0 101.99 43.386 2.70e-05 . . #> REP(ENV) 8 18.8 2.35 0.906 5.15e-01 . . #> GEN 12 212.9 17.74 6.838 8.95e-09 . . #> GEN:ENV 36 398.2 11.06 4.263 7.60e-09 . . #> PC1 14 212.2 15.16 5.840 0.00e+00 53.3 53.3 #> PC2 12 134.7 11.23 4.330 0.00e+00 33.8 87.1 #> PC3 10 51.3 5.13 1.980 4.38e-02 12.9 100 #> Residuals 96 249.1 2.59 NA NA . . #> Total 155 1185.0 7.64 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #>
#> New names: #> * `` -> ...9
#> variable TKW #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 3 37013 12338 11.13 3.16e-03 . . #> REP(ENV) 8 8869 1109 1.21 3.03e-01 . . #> GEN 12 44633 3719 4.05 4.41e-05 . . #> GEN:ENV 36 164572 4571 4.98 1.73e-10 . . #> PC1 14 104276 7448 8.11 0.00e+00 63.4 63.4 #> PC2 12 33361 2780 3.03 1.20e-03 20.3 83.6 #> PC3 10 26935 2694 2.93 3.00e-03 16.4 100 #> Residuals 96 88171 918 NA NA . . #> Total 155 343257 2215 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #>
#> New names: #> * `` -> ...9
#> variable NKR #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 3 237.0 79.01 15.843 0.000997 . . #> REP(ENV) 8 39.9 4.99 0.635 0.746348 . . #> GEN 12 227.8 18.99 2.418 0.008726 . . #> GEN:ENV 36 602.7 16.74 2.132 0.001839 . . #> PC1 14 337.4 24.10 3.070 0.000600 56 56 #> PC2 12 192.2 16.02 2.040 0.028500 31.9 87.9 #> PC3 10 73.1 7.31 0.930 0.509500 12.1 100 #> Residuals 96 753.7 7.85 NA NA . . #> Total 155 1861.1 12.01 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #>
#> New names: #> * `` -> ...9
#> variable CD #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 3 9.72 3.241 6.921 0.012981 . . #> REP(ENV) 8 3.75 0.468 0.519 0.839734 . . #> GEN 12 31.41 2.617 2.899 0.001851 . . #> GEN:ENV 36 81.59 2.266 2.511 0.000194 . . #> PC1 14 48.25 3.446 3.820 0.000000 59.1 59.1 #> PC2 12 20.72 1.727 1.910 0.042300 25.4 84.5 #> PC3 10 12.62 1.262 1.400 0.192000 15.5 100 #> Residuals 96 86.66 0.903 NA NA . . #> Total 155 213.12 1.375 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #>
#> New names: #> * `` -> ...9
#> variable CL #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 3 50.1 16.70 5.73 2.16e-02 . . #> REP(ENV) 8 23.3 2.91 1.76 9.41e-02 . . #> GEN 12 65.7 5.47 3.31 4.81e-04 . . #> GEN:ENV 36 527.3 14.65 8.86 7.16e-18 . . #> PC1 14 320.6 22.90 13.86 0.00e+00 60.8 60.8 #> PC2 12 129.1 10.76 6.51 0.00e+00 24.5 85.3 #> PC3 10 77.5 7.75 4.69 0.00e+00 14.7 100 #> Residuals 96 158.6 1.65 NA NA . . #> Total 155 825.0 5.32 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #>
#> New names: #> * `` -> ...9
#> variable CW #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 3 1258.2 419.41 45.225 2.31e-05 . . #> REP(ENV) 8 74.2 9.27 0.772 6.28e-01 . . #> GEN 12 531.6 44.30 3.686 1.43e-04 . . #> GEN:ENV 36 3056.0 84.89 7.064 9.61e-15 . . #> PC1 14 2223.8 158.84 13.220 0.00e+00 72.8 72.8 #> PC2 12 502.5 41.88 3.480 3.00e-04 16.4 89.2 #> PC3 10 329.7 32.97 2.740 5.20e-03 10.8 100 #> Residuals 96 1153.7 12.02 NA NA . . #> Total 155 6073.7 39.19 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #> #> All variables with significant (p < 0.05) genotype-vs-environment interaction #> Done!
# Sum of squares get_model_data(AMMI, "ipca_ss")
#> Class of the model: performs_ammi
#> Variable extracted: Sum Sq
#> # A tibble: 3 x 9 #> PC DF PH ED TKW NKR CD CL CW #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 PC1 14 4.47 212. 104276. 337. 48.2 321. 2224. #> 2 PC2 12 0.653 135. 33361. 192. 20.7 129. 503. #> 3 PC3 10 0.277 51.3 26935. 73.1 12.6 77.5 330.
# Mean squares get_model_data(AMMI, "ipca_ms")
#> Class of the model: performs_ammi
#> Variable extracted: Mean Sq
#> # A tibble: 3 x 9 #> PC DF PH ED TKW NKR CD CL CW #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 PC1 14 0.319 15.2 7448. 24.1 3.45 22.9 159. #> 2 PC2 12 0.0545 11.2 2780. 16.0 1.73 10.8 41.9 #> 3 PC3 10 0.0277 5.13 2694. 7.31 1.26 7.75 33.0
# Examine the significance (p-value) of the IPCAs get_model_data(AMMI, "ipca_pval")
#> Class of the model: performs_ammi
#> Variable extracted: Pr(>F)
#> # A tibble: 3 x 9 #> PC DF PH ED TKW NKR CD CL CW #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 PC1 14 0 0 0 0.000600 0 0 0 #> 2 PC2 12 0.0084 0 0.00120 0.0285 0.0423 0 0.000300 #> 3 PC3 10 0.276 0.0438 0.003 0.509 0.192 0 0.0052
# Explained sum of square for each IPCA get_model_data(AMMI)
#> Class of the model: performs_ammi
#> Variable extracted: Percent
#> # A tibble: 3 x 9 #> PC DF PH ED TKW NKR CD CL CW #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 PC1 14 82.8 53.3 63.4 56 59.1 60.8 72.8 #> 2 PC2 12 12.1 33.8 20.3 31.9 25.4 24.5 16.4 #> 3 PC3 10 5.1 12.9 16.4 12.1 15.5 14.7 10.8
# Accumulated sum of square get_model_data(AMMI, "ipca_accum")
#> Class of the model: performs_ammi
#> Variable extracted: Accumul
#> # A tibble: 3 x 9 #> PC DF PH ED TKW NKR CD CL CW #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 PC1 14 82.8 53.3 63.4 56 59.1 60.8 72.8 #> 2 PC2 12 94.9 87.1 83.6 87.9 84.5 85.3 89.2 #> 3 PC3 10 100 100 100 100 100 100 100
### AMMI-based stability statistics ### # Get the AMMI stability value AMMI %>% AMMI_indexes() %>% get_model_data("ASV")
#> Class of the model: AMMI_indexes
#> Variable extracted: ASV
#> # A tibble: 13 x 8 #> gen PH ED TKW NKR CD CL CW #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 H1 2.27 1.41 7.76 2.01 0.314 2.75 6.24 #> 2 H10 1.61 2.11 6.58 1.00 0.725 2.67 4.63 #> 3 H11 1.48 1.31 3.55 2.05 0.505 0.918 3.16 #> 4 H12 2.62 0.610 0.981 0.547 1.08 1.36 0.881 #> 5 H13 2.53 1.56 2.45 1.86 1.55 2.11 5.63 #> 6 H2 2.56 2.58 19.0 0.793 1.39 4.47 12.0 #> 7 H3 2.80 0.863 17.3 0.513 1.45 2.19 2.97 #> 8 H4 2.22 0.540 13.0 0.565 1.31 1.58 2.59 #> 9 H5 1.27 1.22 0.984 1.59 0.744 1.04 3.68 #> 10 H6 2.00 0.848 5.73 3.11 1.39 1.93 5.14 #> 11 H7 1.72 0.717 9.61 1.41 1.78 1.25 7.93 #> 12 H8 2.38 1.54 19.8 2.51 2.29 3.25 10.5 #> 13 H9 0.872 1.95 21.3 2.44 1.81 2.06 8.42
#################### WAASB model ##################### # Fitting the WAAS index AMMI <- waas(data_ge2, ENV, GEN, REP, resp = c(PH, ED, TKW, NKR))
#> New names: #> * `` -> ...9
#> variable PH #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 3 7.719 2.5728 127.913 4.25e-07 . . #> REP(ENV) 8 0.161 0.0201 0.897 5.22e-01 . . #> GEN 12 1.865 0.1554 6.929 6.89e-09 . . #> GEN:ENV 36 5.397 0.1499 6.686 5.01e-14 . . #> PC1 14 4.466 0.3190 14.230 0.00e+00 82.8 82.8 #> PC2 12 0.653 0.0545 2.430 8.40e-03 12.1 94.9 #> PC3 10 0.277 0.0277 1.240 2.76e-01 5.1 100 #> Residuals 96 2.153 0.0224 NA NA . . #> Total 155 17.294 0.1116 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #>
#> New names: #> * `` -> ...9
#> variable ED #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 3 306.0 101.99 43.386 2.70e-05 . . #> REP(ENV) 8 18.8 2.35 0.906 5.15e-01 . . #> GEN 12 212.9 17.74 6.838 8.95e-09 . . #> GEN:ENV 36 398.2 11.06 4.263 7.60e-09 . . #> PC1 14 212.2 15.16 5.840 0.00e+00 53.3 53.3 #> PC2 12 134.7 11.23 4.330 0.00e+00 33.8 87.1 #> PC3 10 51.3 5.13 1.980 4.38e-02 12.9 100 #> Residuals 96 249.1 2.59 NA NA . . #> Total 155 1185.0 7.64 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #>
#> New names: #> * `` -> ...9
#> variable TKW #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 3 37013 12338 11.13 3.16e-03 . . #> REP(ENV) 8 8869 1109 1.21 3.03e-01 . . #> GEN 12 44633 3719 4.05 4.41e-05 . . #> GEN:ENV 36 164572 4571 4.98 1.73e-10 . . #> PC1 14 104276 7448 8.11 0.00e+00 63.4 63.4 #> PC2 12 33361 2780 3.03 1.20e-03 20.3 83.6 #> PC3 10 26935 2694 2.93 3.00e-03 16.4 100 #> Residuals 96 88171 918 NA NA . . #> Total 155 343257 2215 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #>
#> New names: #> * `` -> ...9
#> variable NKR #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Percent Accumul #> ENV 3 237.0 79.01 15.843 0.000997 . . #> REP(ENV) 8 39.9 4.99 0.635 0.746348 . . #> GEN 12 227.8 18.99 2.418 0.008726 . . #> GEN:ENV 36 602.7 16.74 2.132 0.001839 . . #> PC1 14 337.4 24.10 3.070 0.000600 56 56 #> PC2 12 192.2 16.02 2.040 0.028500 31.9 87.9 #> PC3 10 73.1 7.31 0.930 0.509500 12.1 100 #> Residuals 96 753.7 7.85 NA NA . . #> Total 155 1861.1 12.01 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #> #> All variables with significant (p < 0.05) genotype-vs-environment interaction #> Done!
# Getting the weighted average of absolute scores get_model_data(AMMI, what = "WAAS")
#> Class of the model: waas
#> Variable extracted: WAAS
#> # A tibble: 13 x 5 #> gen PH ED TKW NKR #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 H1 0.318 0.667 2.72 0.929 #> 2 H10 0.230 0.973 2.15 0.506 #> 3 H11 0.201 0.649 1.26 0.836 #> 4 H12 0.364 0.315 0.558 0.228 #> 5 H13 0.363 0.838 0.514 0.946 #> 6 H2 0.342 1.08 4.41 0.404 #> 7 H3 0.374 0.486 4.10 0.252 #> 8 H4 0.294 0.378 3.07 0.281 #> 9 H5 0.168 0.567 0.738 0.611 #> 10 H6 0.270 0.409 1.64 1.60 #> 11 H7 0.228 0.384 3.44 0.518 #> 12 H8 0.315 0.653 4.91 0.941 #> 13 H9 0.146 0.907 5.50 0.888
# And the rank for the WAASB index. get_model_data(AMMI, what = "OrWAAS")
#> Class of the model: waas
#> Variable extracted: OrWAAS
#> # A tibble: 13 x 5 #> gen PH ED TKW NKR #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 H1 9 9 7 10 #> 2 H10 5 12 6 5 #> 3 H11 3 7 4 8 #> 4 H12 12 1 2 1 #> 5 H13 11 10 1 12 #> 6 H2 10 13 11 4 #> 7 H3 13 5 10 2 #> 8 H4 7 2 8 3 #> 9 H5 2 6 3 7 #> 10 H6 6 4 5 13 #> 11 H7 4 3 9 6 #> 12 H8 8 8 12 11 #> 13 H9 1 11 13 9
#################### BLUP model ##################### # Fitting a mixed-effect model blup <- waasb(data_ge2, ENV, GEN, REP, resp = c(PH, ED, TKW, NKR))
#> 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 PH ED TKW NKR #> COMPLETE NA NA NA NA #> GEN 9.39e-01 2.99e-01 1.00e+00 0.78738 #> GEN:ENV 1.09e-13 1.69e-08 4.21e-10 0.00404 #> --------------------------------------------------------------------------- #> All variables with significant (p < 0.05) genotype-vs-environment interaction
# Getting p-values for likelihood-ratio test get_model_data(blup, what = "pval_lrt")
#> Class of the model: waasb
#> Variable extracted: pval_lrt
#> # A tibble: 3 x 5 #> model PH ED TKW NKR #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 COMPLETE NA NA NA NA #> 2 GEN 9.39e- 1 0.299 1.00e+ 0 0.787 #> 3 GEN:ENV 1.09e-13 0.0000000169 4.21e-10 0.00404
# Getting the variance components get_model_data(blup, what = "vcomp")
#> Class of the model: waasb
#> Variable extracted: vcomp
#> # A tibble: 3 x 5 #> Group PH ED TKW NKR #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 GEN 0.000456 0.556 8.27e-7 0.187 #> 2 GEN:ENV 0.0425 2.82 1.15e+3 2.96 #> 3 Residual 0.0224 2.59 9.19e+2 7.85
# Getting the genetic parameters get_model_data(blup)
#> Class of the model: waasb
#> Variable extracted: genpar
#> # A tibble: 9 x 5 #> Parameters PH ED TKW NKR #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Phenotypic variance 0.0654 5.97 2.06e+ 3 11.0 #> 2 Heritability 0.00698 0.0932 4.00e-10 0.0170 #> 3 GEIr2 0.650 0.472 5.55e- 1 0.269 #> 4 Heribatility of means 0.0352 0.376 2.28e- 9 0.118 #> 5 Accuracy 0.188 0.614 4.77e- 5 0.344 #> 6 rge 0.655 0.521 5.55e- 1 0.274 #> 7 CVg 0.860 1.51 2.68e- 4 1.34 #> 8 CVr 6.03 3.25 8.95e+ 0 8.69 #> 9 CV ratio 0.143 0.463 3.00e- 5 0.154
### BLUP-based stability indexes ### blup %>% Resende_indexes() %>% get_model_data()
#> Class of the model: Res_ind
#> Variable extracted: HMRPGV
#> # A tibble: 13 x 5 #> gen PH ED TKW NKR #> <fct> <dbl> <dbl> <dbl> <dbl> #> 1 H1 1.04 1.03 1.06 0.998 #> 2 H10 0.937 0.979 0.946 1.00 #> 3 H11 0.964 0.986 0.984 1.01 #> 4 H12 0.981 0.984 0.946 0.966 #> 5 H13 1.02 1.02 1.00 0.976 #> 6 H2 1.04 1.02 1.03 0.993 #> 7 H3 1.03 0.998 1.01 0.984 #> 8 H4 1.03 0.994 1.01 1.05 #> 9 H5 1.03 1.01 1.00 1.03 #> 10 H6 1.02 1.03 1.05 1.01 #> 11 H7 0.970 0.999 1.01 0.985 #> 12 H8 0.939 0.978 0.945 0.980 #> 13 H9 0.953 0.965 0.916 1.00
#################### Stability indexes ##################### stats_ge <- ge_stats(data_ge, ENV, GEN, REP, everything())
#> New names: #> * `` -> ...15
#> New names: #> * `` -> ...15
get_model_data(stats_ge)
#> Class of the model: ge_stats
#> Variable extracted: stats
#> # A tibble: 20 x 33 #> var gen Y CV Var Shukla Wi_g Wi_f Wi_u Ecoval bij #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 GY G1 2.60 35.2 10.9 0.0280 84.4 89.2 81.1 1.22 1.06 #> 2 GY G10 2.47 42.3 14.2 0.244 59.2 64.6 54.4 7.96 1.12 #> 3 GY G2 2.74 34.0 11.3 0.0861 82.8 95.3 75.6 3.03 1.05 #> 4 GY G3 2.96 29.9 10.1 0.0121 104. 99.7 107. 0.725 1.03 #> 5 GY G4 2.64 31.4 8.93 0.0640 85.9 79.5 91.9 2.34 0.937 #> 6 GY G5 2.54 30.6 7.82 0.0480 82.7 82.2 82.4 1.84 0.887 #> 7 GY G6 2.53 29.7 7.34 0.0468 83.0 83.7 81.8 1.81 0.861 #> 8 GY G7 2.74 27.4 7.33 0.122 83.9 77.6 93.4 4.16 0.819 #> 9 GY G8 3.00 30.4 10.8 0.0712 98.8 90.5 107. 2.57 1.03 #> 10 GY G9 2.51 42.4 14.7 0.167 68.8 68.9 70.3 5.56 1.19 #> 11 HM G1 47.1 8.47 207. 1.54 93.7 93.6 93.4 60.2 0.991 #> 12 HM G10 48.5 11.0 370. 6.96 92.3 99.9 88.2 229. 1.27 #> 13 HM G2 46.7 8.70 214. 4.03 90.6 90.1 90.7 138. 0.942 #> 14 HM G3 47.6 8.45 210. 1.37 95.0 94.4 95.4 55.0 1.00 #> 15 HM G4 48.0 7.86 185. 3.51 93.8 94.3 93.9 122. 0.880 #> 16 HM G5 49.3 7.20 164. 4.82 95.3 96.0 96.2 163. 0.788 #> 17 HM G6 48.7 8.55 226. 1.48 97.0 98.0 95.9 58.3 1.04 #> 18 HM G7 48.0 7.98 191. 2.89 93.9 95.3 92.8 102. 0.911 #> 19 HM G8 49.1 7.93 197. 2.06 97.2 97.5 97.2 76.5 0.951 #> 20 HM G9 47.9 10.1 306. 2.70 93.8 98.1 91.4 96.4 1.22 #> # ... with 22 more variables: Sij <dbl>, R2 <dbl>, ASV <dbl>, SIPC <dbl>, #> # EV <dbl>, ZA <dbl>, WAAS <dbl>, HMGV <dbl>, RPGV <dbl>, HMRPGV <dbl>, #> # Pi_a <dbl>, Pi_f <dbl>, Pi_u <dbl>, Gai <dbl>, S1 <dbl>, S2 <dbl>, #> # S3 <dbl>, S6 <dbl>, N1 <dbl>, N2 <dbl>, N3 <dbl>, N4 <dbl>
# }