A method based on the statistical ranges of the study variable per environment for the stability analysis.
stability.nonpar(data, variable = NULL, ranking = FALSE, console = FALSE)
| data | First column the genotypes following environment |
|---|---|
| variable | Name of variable |
| ranking | logical, print ranking |
| console | logical, print output |
data frame
Statistical analysis chi square test
Haynes K G, Lambert D H, Christ B J, Weingartner D P, Douches D S, Backlund J E, Fry W and Stevenson W. 1998. Phenotypic stability of resistance to late blight in potato clones evaluated at eight sites in the United States American Journal Potato Research 75, pag 211-217.
#> #> Nonparametric Method for Stability Analysis #> ------------------------------------------- #> #> Estimation and test of nonparametric measures #> Variable: AUDPC #> #> Ranking... #> FL MI ME MN ND NY PA WI #> A84118-3 7 11 11 14 8 14.0 12 11 #> AO80432-1 6 9 13 13 12 12.0 15 14 #> AO84275-3 10 10 12 8 9 7.0 11 12 #> AWN86514-2 3 3 3 1 3 3.0 2 1 #> B0692-4 1 8 4 3 2 2.0 1 3 #> B0718-3 2 1 2 2 4 4.0 3 4 #> B0749-2F 15 12 16 10 13 13.0 13 8 #> B0767-2 4 2 1 4 1 1.0 4 2 #> Bertita 8 7 7 6 10 8.5 10 9 #> Bzura 9 4 5 5 6 5.0 5 5 #> C0083008-1 11 15 14 15 15 15.0 14 15 #> Elba 13 13 10 9 14 11.0 7 13 #> Greta 5 5 8 7 5 6.0 6 7 #> Krantz 14 16 15 16 16 16.0 16 16 #> Libertas 12 6 6 12 7 8.5 8 6 #> Stobrawa 16 14 9 11 11 10.0 9 10 #> #> Statistics... #> Mean Rank s1 Z1 s2 Z2 #> A84118-3 741.62 13 4.82 0.22 16.70 0.34 #> AO80432-1 734.38 12 6.21 0.73 26.57 0.47 #> AO84275-3 635.88 9 6.20 0.70 28.53 0.87 #> AWN86514-2 176.88 2 5.71 0.15 23.64 0.09 #> B0692-4 224.50 4 3.11 4.37 7.12 3.28 #> B0718-3 192.50 3 6.64 1.59 30.57 1.43 #> B0749-2F 772.88 14 5.07 0.05 19.14 0.07 #> B0767-2 153.00 1 5.79 0.20 23.07 0.05 #> Bertita 502.12 8 3.57 2.73 9.43 2.30 #> Bzura 331.75 5 6.04 0.47 26.55 0.46 #> C0083008-1 1022.12 15 7.11 2.90 38.84 5.09 #> Elba 719.00 11 6.57 1.43 29.71 1.18 #> Greta 412.88 6 4.59 0.47 14.71 0.70 #> Krantz 1169.62 16 7.04 2.67 42.84 7.67 #> Libertas 500.88 7 4.50 0.59 14.00 0.87 #> Stobrawa 693.38 10 4.36 0.82 13.64 0.95 #> ------------------------ #> Sum of Z1: 20.08986 #> Sum of Z2: 25.84532 #> ------------------------ #> #> Test... #> The Z-statistics are measures of stability. The test for the significance #> of the sum of Z1 or Z2 are compared to a Chi-Square value of chi.sum. #> individual Z1 or Z2 are compared to a Chi-square value of chi.ind. #> #> MEAN es1 es2 vs1 vs2 chi.ind chi.sum #> 1 561.4609 5.3125 21.25 1.111905 60.75223 8.733011 26.29623 #> --- #> expectation and variance: es1, es2, vs1, vs2# Example 2 data(CIC) data1<-CIC$comas[,c(1,6,7,17,18)] data2<-CIC$oxapampa[,c(1,6,7,19,20)] cic <- rbind(data1,data2) means <- by(cic[,5], cic[,c(2,1)], function(x) mean(x,na.rm=TRUE)) means <-as.data.frame(means[,]) cic.mean<-data.frame(genotype=row.names(means),means) cic.mean<-delete.na(cic.mean,"greater") out<-stability.nonpar(cic.mean) out$ranking#> Mean Rank s1 Z1 s2 Z2 #> 762616.258 0.21156667 49 100.0 6.1406218415 5000.000 1.171984e+01 #> 762619.251 0.09073333 3 58.0 0.7113006387 1682.000 3.490158e-01 #> Atzimba 0.26909167 70 106.0 7.3524926275 5618.000 1.561060e+01 #> CC2.1 0.22568333 57 18.0 0.5089608533 162.000 4.996819e-01 #> CC2.10 0.28998333 79 68.0 1.5192700839 2312.000 1.273784e+00 #> CC2.101 0.31256667 88 12.0 0.8966861289 72.000 6.142120e-01 #> CC2.102 0.17503333 38 80.0 2.8887324335 3200.000 3.559733e+00 #> CC2.103 0.13860000 15 24.0 0.2302989456 288.000 3.591759e-01 #> CC2.104 0.28131667 76 62.0 0.9981339608 1922.000 6.330890e-01 #> CC2.105 0.43278333 104 74.0 2.1494695748 2738.000 2.226977e+00 #> CC2.106 0.19058333 45 8.0 1.2157604058 32.000 6.689042e-01 #> CC2.107 0.23630000 61 30.0 0.0607004057 450.000 2.125301e-01 #> CC2.108 0.15071667 22 46.0 0.1416363198 1058.000 3.370162e-03 #> CC2.109 0.17981667 42 2.0 1.7852579611 2.000 7.114540e-01 #> CC2.11 0.21800000 51 84.0 3.4421650993 3528.000 4.694790e+00 #> CC2.110 0.25455000 66 26.0 0.1616479470 338.000 3.098333e-01 #> CC2.111 0.27001667 71 92.0 4.6944482545 4232.000 7.660532e+00 #> CC2.112 0.26415000 68 4.0 1.5833072907 8.000 7.028391e-01 #> CC2.113 0.37413333 96 48.0 0.2062849930 1152.000 1.912771e-02 #> CC2.114 0.26607500 69 76.0 2.3837723757 2888.000 2.625580e+00 #> CC2.115 0.13686667 13 32.0 0.0284038630 512.000 1.665286e-01 #> CC2.116 0.08695000 2 38.0 0.0042231469 722.000 5.234829e-02 #> CC2.117 0.22551667 56 2.0 1.7852579611 2.000 7.114540e-01 #> CC2.118 0.28343333 77 78.0 2.6301933286 3042.000 3.068935e+00 #> CC2.119 0.12976667 9 22.0 0.3110680962 242.000 4.077896e-01 #> CC2.12 0.32376667 90 64.0 1.1597278499 2048.000 8.158386e-01 #> CC2.120 0.26336667 67 58.0 0.7113006387 1682.000 3.490158e-01 #> CC2.14 0.21693333 50 12.5 0.8602100745 78.125 6.060431e-01 #> CC2.15 0.12771667 8 28.0 0.1051151003 392.000 2.606365e-01 #> CC2.16 0.46028333 105 88.0 4.0440703729 3872.000 6.053700e+00 #> CC2.17 0.25080000 65 43.0 0.0673848450 924.500 3.126911e-03 #> CC2.18 0.39961667 103 30.0 0.0607004057 450.000 2.125301e-01 #> CC2.19 0.15001667 21 70.0 1.7172184289 2450.000 1.553598e+00 #> CC2.2 0.36513333 94 18.0 0.5089608533 162.000 4.996819e-01 #> CC2.20 0.37440000 97 84.0 3.4421650993 3528.000 4.694790e+00 #> CC2.21 0.29431667 80 0.0 1.9993267835 0.000 7.143373e-01 #> CC2.22 0.30085000 83 66.0 1.3334398909 2178.000 1.028646e+00 #> CC2.23 0.30753333 86 52.0 0.3719367953 1352.000 9.551027e-02 #> CC2.24 0.16428333 31 76.0 2.3837723757 2888.000 2.625580e+00 #> CC2.26 0.27376667 73 34.0 0.0082254723 578.000 1.237164e-01 #> CC2.27 0.16003333 27 32.0 0.0284038630 512.000 1.665286e-01 #> CC2.28 0.16395000 30 22.0 0.3110680962 242.000 4.077896e-01 #> CC2.3 0.30043333 82 106.0 7.3524926275 5618.000 1.561060e+01 #> CC2.30 0.30310000 84 102.0 6.5324606182 5202.000 1.293033e+01 #> CC2.31 0.36036667 92 94.0 5.0378144233 4418.000 8.564747e+00 #> CC2.32 0.39414167 101 38.0 0.0042231469 722.000 5.234829e-02 #> CC2.33 0.13773333 14 26.0 0.1616479470 338.000 3.098333e-01 #> CC2.34 0.13273333 11 6.0 1.3934747723 18.000 6.885975e-01 #> CC2.35 0.36385000 93 86.0 3.7370586601 3698.000 5.344785e+00 #> CC2.36 0.23273333 59 46.0 0.1416363198 1058.000 3.370162e-03 #> CC2.37 0.27213333 72 102.0 6.5324606182 5202.000 1.293033e+01 #> CC2.38 0.21893333 52 94.0 5.0378144233 4418.000 8.564747e+00 #> CC2.39 0.39881667 102 100.0 6.1406218415 5000.000 1.171984e+01 #> CC2.4 0.29800000 81 104.0 6.9364175469 5408.000 1.422605e+01 #> CC2.40 0.20903333 48 14.0 0.7553262184 98.000 5.799127e-01 #> CC2.41 0.15696667 26 74.0 2.1494695748 2738.000 2.226977e+00 #> CC2.43 0.49376667 106 50.0 0.2830518182 1250.000 4.926966e-02 #> CC2.44 0.11718333 4 70.0 1.7172184289 2450.000 1.553598e+00 #> CC2.46 0.32461667 91 8.0 1.2157604058 32.000 6.689042e-01 #> CC2.47 0.14420000 16 16.0 0.6260844598 128.000 5.415611e-01 #> CC2.48 0.23183333 58 42.0 0.0486934294 882.000 8.501204e-03 #> CC2.49 0.27411667 74 56.0 0.5860612056 1568.000 2.434942e-01 #> CC2.5 0.31270000 89 62.0 0.9981339608 1922.000 6.330890e-01 #> CC2.50 0.24668333 64 50.0 0.2830518182 1250.000 4.926966e-02 #> CC2.51 0.13461667 12 4.0 1.5833072907 8.000 7.028391e-01 #> CC2.52 0.11930000 7 13.5 0.7895301192 91.125 5.888864e-01 #> CC2.53 0.24228333 63 54.0 0.4729399245 1458.000 1.596337e-01 #> CC2.54 0.38315000 100 96.0 5.3932987441 4608.000 9.540476e+00 #> CC2.55 0.17501667 37 40.0 0.0203992122 800.000 2.631117e-02 #> CC2.56 0.18508333 43 10.0 1.0501641913 50.000 6.440041e-01 #> CC2.58 0.23298333 60 82.0 3.1593896904 3362.000 4.100740e+00 #> CC2.6 0.16106667 28 96.0 5.3932987441 4608.000 9.540476e+00 #> CC2.60 0.17676667 40 92.0 4.6944482545 4232.000 7.660532e+00 #> CC2.61 0.14451667 17 36.0 0.0001652336 648.000 8.524818e-02 #> CC2.62 0.16963333 33 10.0 1.0501641913 50.000 6.440041e-01 #> CC2.63 0.38006667 99 86.0 3.7370586601 3698.000 5.344785e+00 #> CC2.64 0.18548333 44 20.0 0.4039553987 200.000 4.548698e-01 #> CC2.65 0.16696667 32 82.0 3.1593896904 3362.000 4.100740e+00 #> CC2.66 0.31165000 87 104.0 6.9364175469 5408.000 1.422605e+01 #> CC2.68 0.14458333 18 44.0 0.0891057986 968.000 3.527591e-04 #> CC2.69 0.11881667 5 28.0 0.1051151003 392.000 2.606365e-01 #> CC2.7 0.52773333 108 60.0 0.8486582238 1800.000 4.781927e-01 #> CC2.70 0.17510000 39 90.0 4.3632002378 4050.000 6.824577e+00 #> CC2.71 0.22300000 54 108.0 7.7806858602 5832.000 1.708766e+01 #> CC2.72 0.17151667 34 68.0 1.5192700839 2312.000 1.273784e+00 #> CC2.73 0.17363333 36 66.0 1.3334398909 2178.000 1.028646e+00 #> CC2.74 0.20510000 47 24.0 0.2302989456 288.000 3.591759e-01 #> CC2.75 0.30496667 85 34.0 0.0082254723 578.000 1.237164e-01 #> CC2.76 0.19255000 46 98.0 5.7609012168 4802.000 1.059104e+01 #> CC2.77 0.17205000 35 72.0 1.9272849258 2592.000 1.870501e+00 #> CC2.8 0.17890000 41 72.0 1.9272849258 2592.000 1.870501e+00 #> CC2.83 0.15110000 23 56.0 0.5860612056 1568.000 2.434942e-01 #> CC2.84 0.22048333 53 48.0 0.2062849930 1152.000 1.912771e-02 #> CC2.85 0.15310000 24 52.0 0.3719367953 1352.000 9.551027e-02 #> CC2.86 0.13200000 10 6.0 1.3934747723 18.000 6.885975e-01 #> CC2.88 0.14791667 20 54.0 0.4729399245 1458.000 1.596337e-01 #> CC2.9 0.27691667 75 40.0 0.0203992122 800.000 2.631117e-02 #> CC2.90 0.37865000 98 60.0 0.8486582238 1800.000 4.781927e-01 #> CC2.91 0.14760000 19 64.0 1.1597278499 2048.000 8.158386e-01 #> CC2.92 0.11891667 6 20.0 0.4039553987 200.000 4.548698e-01 #> CC2.94 0.16336667 29 36.0 0.0001652336 648.000 8.524818e-02 #> CC2.96 0.23653333 62 43.0 0.0673848450 924.500 3.126911e-03 #> CC2.97 0.15518333 25 78.0 2.6301933286 3042.000 3.068935e+00 #> CC2.98 0.36918333 95 88.0 4.0440703729 3872.000 6.053700e+00 #> CC2.99 0.22388333 55 16.0 0.6260844598 128.000 5.415611e-01 #> Chata_Blanca 0.70826667 109 80.0 2.8887324335 3200.000 3.559733e+00 #> Lbr_40 0.08095000 1 108.0 7.7806858602 5832.000 1.708766e+01 #> Monserrate 0.28824167 78 90.0 4.3632002378 4050.000 6.824577e+00 #> Yungay 0.51476667 107 98.0 5.7609012168 4802.000 1.059104e+01out$statistics#> MEAN es1 es2 vs1 vs2 chi.ind chi.sum #> 1 0.2425566 36.33028 990 660.1667 1372041 12.27643 134.3688