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ge_stats() computes parametric and non-parametric stability statistics given a data set with environment, genotype, and block factors.


ge_stats(.data, env, gen, rep, resp, verbose = TRUE, prob = 0.05)



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


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


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


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


The response variable(s). To analyze multiple variables in a single procedure use, for example, resp = c(var1, var2, var3).


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


The probability error assumed.


An object of class ge_stats which is a list with one data frame for each variable containing the computed indexes.


The function computes the statistics and ranks for the following stability indexes.

  • "Y" (Response variable),

  • "CV" (coefficient of variation)

  • "ACV" (adjusted coefficient of variation calling ge_acv() internally)

  • POLAR (Power Law Residuals, calling ge_polar() internally)

  • "Var" (Genotype's variance)

  • "Shukla" (Shukla's variance, calling Shukla() internally)

  • "Wi_g", "Wi_f", "Wi_u" (Annichiarrico's genotypic confidence index for all, favorable and unfavorable environments, respectively, calling Annicchiarico() internally )

  • "Ecoval" (Wricke's ecovalence, ecovalence() internally)

  • "Sij" (Deviations from the joint-regression analysis) and "R2" (R-squared from the joint-regression analysis, calling ge_reg() internally)

  • "ASTAB" (AMMI Based Stability Parameter), "ASI" (AMMI Stability Index), "ASV" (AMMI-stability value), "AVAMGE" (Sum Across Environments of Absolute Value of GEI Modelled by AMMI ), "Da" (Annicchiarico's D Parameter values), "Dz" (Zhang's D Parameter), "EV" (Sums of the Averages of the Squared Eigenvector Values), "FA" (Stability Measure Based on Fitted AMMI Model), "MASV" (Modified AMMI Stability Value), "SIPC" (Sums of the Absolute Value of the IPC Scores), "Za" (Absolute Value of the Relative Contribution of IPCs to the Interaction), "WAAS" (Weighted average of absolute scores), calling ammi_indexes() internally

  • "HMGV" (Harmonic mean of the genotypic value), "RPGV" (Relative performance of the genotypic values), "HMRPGV" (Harmonic mean of the relative performance of the genotypic values), by calling blup_indexes() internally

  • "Pi_a", "Pi_f", "Pi_u" (Superiority indexes for all, favorable and unfavorable environments, respectively, calling superiority() internally)

  • "Gai" (Geometric adaptability index, calling gai() internally)

  • "S1" (mean of the absolute rank differences of a genotype over the n environments), "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), by calling Huehn() internally

  • "N1", "N2", "N3", "N4" (Thennarasu"s statistics, calling Thennarasu() internally ).


Annicchiarico, P. 1992. Cultivar adaptation and recommendation from alfalfa trials in Northern Italy. Journal of Genetic & Breeding, 46:269-278

Ajay BC, Aravind J, Abdul Fiyaz R, Bera SK, Kumar N, Gangadhar K, Kona P (2018). “Modified AMMI Stability Index (MASI) for stability analysis.” ICAR-DGR Newsletter, 18, 4–5.

Ajay BC, Aravind J, Fiyaz RA, Kumar N, Lal C, Gangadhar K, Kona P, Dagla MC, Bera SK (2019). “Rectification of modified AMMI stability value (MASV).” Indian Journal of Genetics and Plant Breeding (The), 79, 726–731.

Annicchiarico P (1997). “Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy.” Euphytica, 94(1), 53–62. doi:10.1023/A:1002954824178

Doring, T.F., and M. Reckling. 2018. Detecting global trends of cereal yield stability by adjusting the coefficient of variation. Eur. J. Agron. 99: 30-36. doi:10.1016/j.eja.2018.06.007

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Tiago Olivoto


# \donttest{
model <- ge_stats(data_ge, ENV, GEN, REP, GY)
#> Evaluating trait GY |============================================| 100% 00:00:21 

get_model_data(model, "stats")
#> Class of the model: ge_stats
#> Variable extracted: stats
#> # A tibble: 10 × 44
#>    var   GEN       Y    CV   ACV   POLAR   Var Shukla  Wi_g  Wi_f  Wi_u Ecoval
#>    <chr> <chr> <dbl> <dbl> <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>
#>  1 GY    G1     2.60  35.2  34.1  0.0298 10.9  0.0280  84.4  89.2  81.1  1.22 
#>  2 GY    G10    2.47  42.3  38.6  0.136  14.2  0.244   59.2  64.6  54.4  7.96 
#>  3 GY    G2     2.74  34.0  35.2  0.0570 11.3  0.0861  82.8  95.3  75.6  3.03 
#>  4 GY    G3     2.96  29.9  33.8  0.0216 10.1  0.0121 104.   99.7 107.   0.725
#>  5 GY    G4     2.64  31.4  31.0 -0.0537  8.93 0.0640  85.9  79.5  91.9  2.34 
#>  6 GY    G5     2.54  30.6  28.8 -0.119   7.82 0.0480  82.7  82.2  82.4  1.84 
#>  7 GY    G6     2.53  29.7  27.8 -0.147   7.34 0.0468  83.0  83.7  81.8  1.81 
#>  8 GY    G7     2.74  27.4  28.3 -0.133   7.33 0.122   83.9  77.6  93.4  4.16 
#>  9 GY    G8     3.00  30.4  35.1  0.0531 10.8  0.0712  98.8  90.5 107.   2.57 
#> 10 GY    G9     2.51  42.4  39.4  0.154  14.7  0.167   68.8  68.9  70.3  5.56 
#> # … with 32 more variables: bij <dbl>, Sij <dbl>, R2 <dbl>, ASTAB <dbl>,
#> #   ASI <dbl>, ASV <dbl>, AVAMGE <dbl>, DA <dbl>, DZ <dbl>, EV <dbl>, FA <dbl>,
#> #   MASI <dbl>, MASV <dbl>, SIPC <dbl>, ZA <dbl>, WAAS <dbl>, WAASB <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>
# }