Stability Parameters for Genotypes by Environment Interaction (GEI)

Source: R/stab_par.R

Stability Parameters for Genotypes by Environment Interaction (GEI)

stab_par(.data, .y, .rep, .gen, .env, alpha = 0.1, .envCov = NULL)

# S3 method for default
stab_par(.data, .y, .rep, .gen, .env, alpha = 0.1,
  .envCov = NULL)

Arguments

.data

data.frame
.y

Response Variable
.rep

Replication Factor
.gen

Genotypes Factor
.env

Environment Factor
alpha

Level of Significance, default is 0.1
.envCov

Environmental Covariate, default is NULL

Value

Stability Parameters

References

Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

Examples


data(ge_data)
Yield.StabPar <-
   stab_par(
            .data   = ge_data
          , .y      = Yield
          , .rep    = Rep
          , .gen    = Gen
          , .env    = Env
          , alpha   = 0.1
          , .envCov = NULL
)
#> Warning: ANOVA F-tests on an essentially perfect fit are unreliable

Yield.StabPar
#> $ANOVA
#>                    Df    Sum Sq  Mean Sq  F value    Pr(>F)    
#> Total             659 536939402                                
#> Gen                59  71662431  1214617   3.7143 4.441e-16 ***
#> Env                10 263940496 26394050 266.1568 < 2.2e-16 ***
#> Gen x Env         590 201336476   341248   3.4411 < 2.2e-16 ***
#>   Heterogeneity    59  27695329   469412   1.4355   0.02256 *  
#>   Residual        531 173641147   327008   3.2975 < 2.2e-16 ***
#> Pooled error    38291              99167                       
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> $StabPar
#> # A tibble: 60 x 12
#>    Gen    Mean  GenSS    Var    CV   Ecov  GE.SS GE.MSE  Sigma .         SP
#>  * <fct> <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <fct>  <dbl>
#>  1 013B… 4248. 1.47e6 1.47e5  6.39 4.73e5 5.03e7 85312. 4.75e4 *     5.27e4
#>  2 1225… 4062. 2.81e6 2.81e5  9.23 5.80e5 5.03e7 85312. 5.85e4 **    3.16e4
#>  3 1225… 4259. 1.96e6 1.96e5  7.36 9.00e5 5.03e7 85312. 9.16e4 **    1.02e5
#>  4 12B.… 4414. 3.75e6 3.75e5  9.81 1.25e6 5.03e7 85312. 1.28e5 **    9.06e4
#>  5 12FJ… 4057. 1.23e6 1.23e5  6.11 7.84e5 5.03e7 85312. 7.96e4 **    7.75e4
#>  6 14B.… 3995. 1.76e6 1.76e5  7.42 6.68e5 5.03e7 85312. 6.77e4 **    7.54e4
#>  7 14C0… 3943. 1.53e6 1.53e5  7.00 1.27e6 5.03e7 85312. 1.30e5 **    1.26e5
#>  8 14C0… 3825. 1.63e6 1.63e5  7.46 7.32e5 5.03e7 85312. 7.42e4 **    8.16e4
#>  9 9496  4072. 1.64e6 1.64e5  7.03 4.96e5 5.03e7 85312. 4.98e4 *     5.55e4
#> 10 AUR0… 3792. 2.29e6 2.29e5  8.93 6.17e5 5.03e7 85312. 6.24e4 **    6.09e4
#> # ... with 50 more rows, and 1 more variable: .. <fct>
#> 
#> $SimultSel
#> # A tibble: 60 x 9
#>    Gen       Mean  Rank Adjustment Adj.Rank   Sigma Stab.Rating   YSi Select
#>  * <fct>    <dbl> <int>      <dbl>    <dbl>   <dbl>       <dbl> <dbl> <fct> 
#>  1 013BT034 4248.    50          2       52  47497.          -4    48 +     
#>  2 122557   4062.    30          1       31  58507.          -8    23 -     
#>  3 122559   4259.    51          2       53  91647.          -8    45 +     
#>  4 12B.2511 4414.    57          3       60 127575.          -8    52 +     
#>  5 12FJ26   4057.    29         -1       28  79595.          -8    20 -     
#>  6 14B.1030 3995.    24         -1       23  67684.          -8    15 -     
#>  7 14C036   3943.    20         -1       19 130283.          -8    11 -     
#>  8 14C040   3825.     8         -2        6  74224.          -8    -2 -     
#>  9 9496     4072.    31          1       32  49799.          -4    28 +     
#> 10 AUR0809  3792.     7         -2        5  62391.          -8    -3 -     
#> # ... with 50 more rows
#>