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Individual Regression for each Genotype

Source: R/stab_reg.R
stab_reg.Rd

Individual Regression for each Genotype in Genotypes by Environment Interaction (GEI)

Usage

stab_reg(.data, .y, .rep, .gen, .env)

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

Arguments

.data

data.frame

.y

Response Variable

.rep

Replication Factor

.gen

Genotypes Factor

.env

Environment Factor

Value

Additive ANOVA

References

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

Author

  1. Muhammad Yaseen (myaseen208@gmail.com)

  2. Kent M. Edkridge (keskridge1@unl.edu)

Examples


data(ge_data)
Yield.StabReg <-
        stab_reg(
            .data = ge_data
          , .y    = Yield
          , .rep  = Rep
          , .gen  = Gen
          , .env  = Env
          )

Yield.StabReg
#> $StabIndvReg
#> # A tibble: 60 × 9
#>    Gen       Mean Slope    LCI   UCI R.Sqr  RMSE      SSE    Delta
#>    <fct>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
#>  1 013BT034 4248. 0.954  0.460  1.45 0.680  324.  942048.  471024.
#>  2 122557   4062. 1.52   1.13   1.90 0.898  253.  574068.  287034.
#>  3 122559   4259. 0.983  0.301  1.67 0.542  447. 1799675.  899838.
#>  4 12B.2511 4414. 1.64   0.994  2.28 0.787  422. 1600984.  800492.
#>  5 12FJ26   4057. 0.703  0.107  1.30 0.442  391. 1372720.  686360.
#>  6 14B.1030 3995. 0.995  0.407  1.58 0.620  385. 1336946.  668473.
#>  7 14C036   3943. 0.615 -0.143  1.37 0.272  497. 2220530. 1110265.
#>  8 14C040   3825. 0.909  0.297  1.52 0.557  401. 1445024.  722512.
#>  9 9496     4072. 1.02   0.513  1.53 0.698  332.  990414.  495207.
#> 10 AUR0809  3792. 1.26   0.732  1.79 0.764  347. 1083957.  541979.
#> # ℹ 50 more rows
#> 
#> $MeanSlopePlot

#>