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[Stable]

Nonparametric stability analysis using the superiority index proposed by Lin & Binns (1988).

Usage

superiority(.data, env, gen, resp, 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.

resp

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

verbose

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

Value

An object of class superiority where each element is the result of one variable and contains the following items:

  • environments The mean for each environment, the environment index and classification as favorable and unfavorable environments.

  • index The superiority index computed for all (Pi_a), favorable (Pi_f) and unfavorable (Pi_u) environments.

References

Lin, C.S., and M.R. Binns. 1988. A superiority measure of cultivar performance for cultivar x location data. Can. J. Plant Sci. 68:193-198. doi:10.4141/cjps88-018

Author

Tiago Olivoto, tiagoolivoto@gmail.com

Examples

# \donttest{
library(metan)
out <- superiority(data_ge2, ENV, GEN, PH)
#> Evaluating trait PH |============================================| 100% 00:00:00 

print(out)
#> Variable PH 
#> ---------------------------------------------------------------------------
#> Superiority index considering all, favorable and unfavorable environments
#> ---------------------------------------------------------------------------
#> # A tibble: 13 × 8
#>    GEN       Y   Pi_a   R_a    Pi_f   R_f   Pi_u   R_u
#>    <chr> <dbl>  <dbl> <dbl>   <dbl> <dbl>  <dbl> <dbl>
#>  1 H1     2.62 0.0258     1 0.0113      8 0.0403     1
#>  2 H10    2.31 0.151     12 0.0213     12 0.280     12
#>  3 H11    2.39 0.116     10 0.0101      7 0.222     11
#>  4 H12    2.44 0.103      8 0.0174     11 0.189      8
#>  5 H13    2.54 0.0688     7 0.00832     4 0.129      7
#>  6 H2     2.60 0.0289     2 0.00839     5 0.0494     2
#>  7 H3     2.59 0.0425     6 0.00791     3 0.0771     4
#>  8 H4     2.58 0.0404     5 0.00279     2 0.0780     5
#>  9 H5     2.57 0.0403     4 0.00253     1 0.0781     6
#> 10 H6     2.56 0.0330     3 0.0128      9 0.0533     3
#> 11 H7     2.40 0.109      9 0.00947     6 0.208      9
#> 12 H8     2.33 0.161     13 0.0165     10 0.306     13
#> 13 H9     2.36 0.124     11 0.0278     13 0.221     10
#> ---------------------------------------------------------------------------
#> 
#> 
#> 
# }