R/stability.par.R
stability.par.RdThis procedure calculates the stability variations as well as the statistics of selection for the yield and the stability. The averages of the genotype through the different environment repetitions are required for the calculations. The mean square error must be calculated from the joint variance analysis.
stability.par( data, rep, MSerror, alpha = 0.1, main = NULL, cova = FALSE, name.cov = NULL, file.cov = 0, console = FALSE )
| data | matrix of averages, by rows the genotypes and columns the environment |
|---|---|
| rep | Number of repetitions |
| MSerror | Mean Square Error |
| alpha | Label significant |
| main | Title |
| cova | Covariable |
| name.cov | Name covariable |
| file.cov | Data covariable |
| console | logical, print output |
Analysis of variance
Statistics of the model
summary stability analysis
Stable (i) determines the contribution of each genotype to GE interaction by calculating var(i); (ii) assigns ranks to genotypes from highest to lowest yield receiving the rank of 1; (iii) calculates protected LSD for mean yield comparisons; (iv) adjusts yield rank according to LSD (the adjusted rank labeled Y); (v) determines significance of var(i) usign an aproximate F-test; (vi) assigns stability rating (S) as follows: -8, -4 and -2 for var(i) significant at the 0.01, 0.05 and 0.10 probability levels, and 0 for nonsignificant var(i) ( the higher the var(i), the less stable the genotype); (vii) sums adjusted yield rank, Y, and stability rating, S, for each genotype to determine YS(i) statistic; and (viii) calculates mean YS(i) and identifies genotypes (selection) with YS(i) > mean YS(i).
Kang, M. S. 1993. Simultaneous selection for yield and stability: Consequences for growers. Agron. J. 85:754-757. Manjit S. Kang and Robert Mangari. 1995. Stable: A basic program for calculating stability and yield-stability statistics. Agron. J. 87:276-277
library(agricolae) # example 1 # Experimental data, # replication rep= 4 # Mean square error, MSerror = 1.8 # 12 environment # 17 genotype = 1,2,3,.., 17 # yield averages of 13 genotypes in localities f <- system.file("external/dataStb.csv", package="agricolae") dataStb<-read.csv(f) stability.par(dataStb, rep=4, MSerror=1.8, alpha=0.1, main="Genotype",console=TRUE)#> #> INTERACTIVE PROGRAM FOR CALCULATING SHUKLA'S STABILITY VARIANCE AND KANG'S #> YIELD - STABILITY (YSi) STATISTICS #> Genotype #> Environmental index - covariate #> #> Analysis of Variance #> #> Df Sum Sq Mean Sq F value Pr(>F) #> Total 203 2964.1716 #> Genotypes 16 186.9082 11.6818 4.17 <0.001 #> Environments 11 2284.0116 207.6374 115.35 <0.001 #> Interaction 176 493.2518 2.8026 1.56 <0.001 #> Heterogeneity 16 44.8576 2.8036 1 0.459 #> Residual 160 448.3942 2.8025 1.56 <0.001 #> Pooled Error 576 1.8 #> #> Genotype. Stability statistics #> #> Mean Sigma-square . s-square . Ecovalence #> A 7.383333 2.474081 ns 2.449076 ns 25.826563 #> B 6.783333 1.600869 ns 1.434734 ns 17.351269 #> C 7.250000 0.567657 ns 0.633936 ns 7.323033 #> D 6.783333 2.611778 ns 2.134731 ns 27.163033 #> E 7.066667 1.862364 ns 2.047627 ns 19.889308 #> F 6.916667 3.575818 * 3.951442 * 36.519896 #> G 7.808333 3.580929 * 3.957319 * 36.569504 #> H 7.908333 2.723717 ns 2.118116 ns 28.249504 #> I 7.275000 4.248566 ** 3.936130 * 43.049504 #> J 7.083333 2.273838 ns 2.506382 ns 23.883033 #> K 6.433333 2.560384 ns 2.551518 ns 26.664210 #> L 6.891667 1.558061 ns 1.732557 ns 16.935779 #> M 6.791667 3.483879 * 3.275985 ns 35.627543 #> N 7.491667 5.164848 ** 4.875189 ** 51.942837 #> O 7.658333 2.380202 ns 2.635025 ns 24.915386 #> P 6.425000 3.445414 * 3.713885 * 35.254210 #> Q 6.158333 3.531232 * 3.688232 * 36.087151 #> #> #> Signif. codes: 0 '**' 0.01 '*' 0.05 'ns' 1 #> #> Simultaneous selection for yield and stability (++) #> #> Yield Rank Adj.rank Adjusted Stab.var Stab.rating YSi ... #> A 7.383333 13 1 14 2.474081 0 14 + #> B 6.783333 4 -1 3 1.600869 0 3 #> C 7.250000 11 1 12 0.567657 0 12 + #> D 6.783333 4 -1 3 2.611778 0 3 #> E 7.066667 9 1 10 1.862364 0 10 + #> F 6.916667 8 -1 7 3.575818 -4 3 #> G 7.808333 16 2 18 3.580929 -4 14 + #> H 7.908333 17 2 19 2.723717 0 19 + #> I 7.275000 12 1 13 4.248566 -8 5 #> J 7.083333 10 1 11 2.273838 0 11 + #> K 6.433333 3 -2 1 2.560384 0 1 #> L 6.891667 7 -1 6 1.558061 0 6 #> M 6.791667 6 -1 5 3.483879 -4 1 #> N 7.491667 14 1 15 5.164848 -8 7 + #> O 7.658333 15 2 17 2.380202 0 17 + #> P 6.425000 2 -2 0 3.445414 -4 -4 #> Q 6.158333 1 -3 -2 3.531232 -4 -6 #> #> Yield Mean: 7.065196 #> YS Mean: 6.823529 #> LSD (0.05): 0.4511874 #> - - - - - - - - - - - #> + selected genotype #> ++ Reference: Kang, M. S. 1993. Simultaneous selection for yield #> and stability: Consequences for growers. Agron. J. 85:754-757.#example 2 covariable. precipitation precipitation<- c(1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000,2100) stability.par(dataStb, rep=4, MSerror=1.8, alpha=0.1, main="Genotype", cova=TRUE, name.cov="Precipitation", file.cov=precipitation,console=TRUE)#> #> INTERACTIVE PROGRAM FOR CALCULATING SHUKLA'S STABILITY VARIANCE AND KANG'S #> YIELD - STABILITY (YSi) STATISTICS #> Genotype #> Precipitation - covariate #> #> Analysis of Variance #> #> Df Sum Sq Mean Sq F value Pr(>F) #> Total 203 2964.1716 #> Genotypes 16 186.9082 11.6818 4.17 <0.001 #> Environments 11 2284.0116 207.6374 115.35 <0.001 #> Interaction 176 493.2518 2.8026 1.56 <0.001 #> Heterogeneity 16 4.3577 0.2724 0.09 1 #> Residual 160 488.894 3.0556 1.7 <0.001 #> Pooled Error 576 1.8 #> #> Genotype. Stability statistics #> #> Mean Sigma-square . s-square . Ecovalence #> A 7.383333 2.474081 ns 2.714197 ns 25.826563 #> B 6.783333 1.600869 ns 1.758725 ns 17.351269 #> C 7.250000 0.567657 ns 0.624604 ns 7.323033 #> D 6.783333 2.611778 ns 2.852117 ns 27.163033 #> E 7.066667 1.862364 ns 2.030613 ns 19.889308 #> F 6.916667 3.575818 * 3.934039 * 36.519896 #> G 7.808333 3.580929 * 3.933173 * 36.569504 #> H 7.908333 2.723717 ns 2.960728 ns 28.249504 #> I 7.275000 4.248566 ** 4.656603 ** 43.049504 #> J 7.083333 2.273838 ns 2.473687 ns 23.883033 #> K 6.433333 2.560384 ns 2.791349 ns 26.664210 #> L 6.891667 1.558061 ns 1.713683 ns 16.935779 #> M 6.791667 3.483879 * 3.741592 * 35.627543 #> N 7.491667 5.164848 ** 5.683086 ** 51.942837 #> O 7.658333 2.380202 ns 2.611730 ns 24.915386 #> P 6.425000 3.445414 * 3.687730 * 35.254210 #> Q 6.158333 3.531232 * 3.777338 * 36.087151 #> #> #> Signif. codes: 0 '**' 0.01 '*' 0.05 'ns' 1 #> #> Simultaneous selection for yield and stability (++) #> #> Yield Rank Adj.rank Adjusted Stab.var Stab.rating YSi ... #> A 7.383333 13 1 14 2.474081 0 14 + #> B 6.783333 4 -1 3 1.600869 0 3 #> C 7.250000 11 1 12 0.567657 0 12 + #> D 6.783333 4 -1 3 2.611778 0 3 #> E 7.066667 9 1 10 1.862364 0 10 + #> F 6.916667 8 -1 7 3.575818 -4 3 #> G 7.808333 16 2 18 3.580929 -4 14 + #> H 7.908333 17 2 19 2.723717 0 19 + #> I 7.275000 12 1 13 4.248566 -8 5 #> J 7.083333 10 1 11 2.273838 0 11 + #> K 6.433333 3 -2 1 2.560384 0 1 #> L 6.891667 7 -1 6 1.558061 0 6 #> M 6.791667 6 -1 5 3.483879 -4 1 #> N 7.491667 14 1 15 5.164848 -8 7 + #> O 7.658333 15 2 17 2.380202 0 17 + #> P 6.425000 2 -2 0 3.445414 -4 -4 #> Q 6.158333 1 -3 -2 3.531232 -4 -6 #> #> Yield Mean: 7.065196 #> YS Mean: 6.823529 #> LSD (0.05): 0.4511874 #> - - - - - - - - - - - #> + selected genotype #> ++ Reference: Kang, M. S. 1993. Simultaneous selection for yield #> and stability: Consequences for growers. Agron. J. 85:754-757.