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Genotype-by-environment means and rankings

Source: R/ge_means.R
ge_means.Rd

ge_means() tabulates the mean of every genotype in every environment, the genotype and environment means, and the ranking of the genotypes within each environment, which shows at a glance whether the best genotypes change from one environment to another (crossover interaction).

Usage

ge_means(.data, .y, .gen, .env)

# Default S3 method
ge_means(.data, .y, .gen, .env)

Arguments

.data

a data frame (or data.table) with one row per plot.

.y

response: a column of .data given by name (.y = Yield or .y = "Yield") or a numeric vector of length nrow(.data).

.gen

genotype: a column of .data or a vector, as for .y.

.env

environment: a column of .data or a vector, as for .y.

Value

An object of class c("stab_ge_means", "stab_result", "list") with the components of earlier versions:

ge_means

data.table, genotypes in rows and environments in columns.

ge_means_mat

the same as a matrix.

g_means, e_means

data.tables of genotype and environment means.

ge_ranks

character matrix, environments in rows: the genotypes in decreasing order of mean.

and a new one:

rank_cor

matrix of Spearman's rank correlations between environments.

Methods: print(), summary(), plot() (which = c("heatmap", "ranks")), stab_interpret().

Details

\(\bar{y}_{ij}\) is the mean of genotype \(i\) in environment \(j\); \(\bar{y}_{i\cdot}\) and \(\bar{y}_{\cdot j}\) are the means of the table's rows and columns (with missing plots, the means of the cell means). Row \(j\) of ge_ranks lists the genotypes of environment \(j\) from the highest mean down. The new component rank_cor holds Spearman's rank correlations of the genotype means between pairs of environments; low or negative correlations indicate crossover interaction.

Change from the earlier version. g_means and e_means were in the order of the data; they are now in the order of the levels (as the rows and columns of ge_means_mat).

See also

ge_effects, stab_fox, stab_interpret

Other stability analyses: add_anova(), ammi(), ammi_biplot(), er_anova(), ge_effects(), gge_biplot(), indiv_anova(), stab_asv(), stab_dist(), stab_eskridge(), stab_fox(), stab_huehn(), stab_kang(), stab_masv(), stab_measures(), stab_par(), stab_reg(), stab_reliability(), stab_superiority()

Author

Muhammad Yaseen (myaseen208@gmail.com), Kent M. Eskridge (keskridge1@unl.edu)

Examples

data(ge_data)
fit <-
  ge_means(
      .data = ge_data
    , .y    = Yield
    , .gen  = Gen
    , .env  = Env
    )
fit$ge_ranks[, 1:5]
#>              1               2              3            4          
#> FSD          "12B.2511"      "V14170"       "DN126"      "PR120"    
#> FSDR         "V14170"        "PR119"        "PR118"      "QS3"      
#> Okara        "NIBGE GANDUM3" "V13348"       "PR118"      "122559"   
#> Sargodha     "NW181838"      "12B.2511"     "NRL1123"    "NR491"    
#> Gujranwala   "IVII"          "FAISALABAD08" "PAKISTAN13" "V14168"   
#> KotNaina     "PR115"         "V14170"       "WV1038"     "UOS1"     
#> KSK          "NR448"         "12FJ26"       "AZRC11"     "BARDC1016"
#> Khanewal     "LOCAL CHECK"   "DN126"        "UOS1"       "V14170"   
#> Sahiwal      "V14168"        "DN126"        "122559"     "MSH3"     
#> Bhakkar      "V14225"        "013BT034"     "BARDC1016"  "14C036"   
#> Bahawalnagar "V14154"        "KT335"        "V14225"     "PR115"    
#>              5             
#> FSD          "FAISALABAD08"
#> FSDR         "LOCAL CHECK" 
#> Okara        "BARDC1016"   
#> Sargodha     "AUR0810"     
#> Gujranwala   "PR115"       
#> KotNaina     "PR121"       
#> KSK          "CT12176"     
#> Khanewal     "PR115"       
#> Sahiwal      "NR491"       
#> Bhakkar      "DN126"       
#> Bahawalnagar "TWS12464"    
plot(x = fit, which = c("heatmap", "ranks")[1])