ammi_indexes()![[Stable]](figures/lifecycle-stable.svg)
computes several AMMI-based stability statistics. See
Details for a detailed overview.AMMI_indexes()![[Deprecated]](figures/lifecycle-deprecated.svg)
use ammi_indexes()instead.
Arguments
- .data
An object of class
waasorperforms_ammi- order.y
A vector of the same length of
xused to order the response variable. Each element of the vector must be one of the'h'or'l'. If'h'is used, the response variable will be ordered from maximum to minimum. If'l'is used then the response variable will be ordered from minimum to maximum. Use a comma-separated vector of names. For example,order.y = c("h, h, l, h, l").- level
The confidence level. Defaults to 0.95.
Details
First, let's define some symbols: N' is the number of significant interation principal component axis (IPCs) that were retained in the AMMI model via F tests); _n is the singular value for th IPC and correspondingly _n^2 its eigen value; _in is the eigenvector value for ith genotype; _jn is the eigenvector value for the th environment. PC_1, PC_2, and PC_n are the scores of 1st, 2nd, and nth IPC; respectively; _1, _2, and _n are percentage sum of squares explained by the 1st, 2nd, and nth IPC, respectively.
AMMI Based Stability Parameter (ASTAB) (Rao and Prabhakaran 2005). ASTAB = _n=1^N'_n_in^2
AMMI Stability Index (ASI) (Jambhulkar et al. 2017) ASI = [ PC_1^2 _1^2 ]+[ PC_2^2 _2^2 ]
AMMI-stability value (ASV) (Purchase et al., 2000). ASV_i=SS_IPCA1SS_IPCA2(IPC A 1)^2+(IPCA 2)^2
Sum Across Environments of Absolute Value of GEI Modelled by AMMI (AVAMGE) (Zali et al. 2012) AV_(AMGE) = _j=1^E _n=1^N' |_n_in _jn |
Annicchiarico's D Parameter values (Da) (Annicchiarico 1997) D_a = _n=1^N'(_n_in)^2
Zhang's D Parameter (Dz) (Zhang et al. 1998) D_z = _n=1^N'_in^2
Sums of the Averages of the Squared Eigenvector Values (EV) (Zobel 1994) EV = _n=1^N'_in^2N'
Stability Measure Based on Fitted AMMI Model (FA) (Raju 2002) FA = _n=1^N'_n^2_in^2
Modified AMMI Stability Index (MASI) (Ajay et al. 2018) MASI = _n=1^N' PC_n^2 _n^2
Modified AMMI Stability Value (MASV) (Ajay et al. 2019) MASV = _n=1^N'-1 (SSIPC_nSSIPC_n+1 ) (PC_n)^2 + (PC_N' )^2
Sums of the Absolute Value of the IPC Scores (SIPC) (Sneller et al. 1997) SIPC = _n=1^N' | _n^0.5_in|
Absolute Value of the Relative Contribution of IPCs to the Interaction (Za) (Zali et al. 2012) Za = _i=1^N' | _n_in |
Weighted average of absolute scores (WAAS) (Olivoto et al. 2019) WAAS_i = _k = 1^p |IPCA_ik _k/ _k = 1^p_k
For all the statistics, simultaneous selection indexes (SSI) are also computed by summation of the ranks of the stability and mean performance, Y_R, (Farshadfar, 2008).
References
Ajay BC, Aravind J, Abdul Fiyaz R, Bera SK, Kumar N, Gangadhar K, Kona P (2018). “Modified AMMI Stability Index (MASI) for stability analysis.” ICAR-DGR Newsletter, 18, 4–5.
Ajay BC, Aravind J, Fiyaz RA, Kumar N, Lal C, Gangadhar K, Kona P, Dagla MC, Bera SK (2019). “Rectification of modified AMMI stability value (MASV).” Indian Journal of Genetics and Plant Breeding (The), 79, 726–731. https://www.isgpb.org/article/rectification-of-modified-ammi-stability-value-masv.
Annicchiarico P (1997). “Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy.” Euphytica, 94(1), 53–62. doi:10.1023/A:1002954824178
Farshadfar E (2008) Incorporation of AMMI stability value and grain yield in a single non-parametric index (GSI) in bread wheat. Pakistan J Biol Sci 11:1791–1796. doi:10.3923/pjbs.2008.1791.1796
Jambhulkar NN, Rath NC, Bose LK, Subudhi HN, Biswajit M, Lipi D, Meher J (2017). “Stability analysis for grain yield in rice in demonstrations conducted during rabi season in India.” Oryza, 54(2), 236–240. doi:10.5958/2249-5266.2017.00030.3
Olivoto T, LUcio ADC, Silva JAG, et al (2019) Mean Performance and Stability in Multi-Environment Trials I: Combining Features of AMMI and BLUP Techniques. Agron J 111:2949–2960. doi:10.2134/agronj2019.03.0220
Raju BMK (2002). “A study on AMMI model and its biplots.” Journal of the Indian Society of Agricultural Statistics, 55(3), 297–322.
Rao AR, Prabhakaran VT (2005). “Use of AMMI in simultaneous selection of genotypes for yield and stability.” Journal of the Indian Society of Agricultural Statistics, 59, 76–82.
Sneller CH, Kilgore-Norquest L, Dombek D (1997). “Repeatability of yield stability statistics in soybean.” Crop Science, 37(2), 383–390. doi:10.2135/cropsci1997.0011183X003700020013x
Zali H, Farshadfar E, Sabaghpour SH, Karimizadeh R (2012). “Evaluation of genotype × environment interaction in chickpea using measures of stability from AMMI model.” Annals of Biological Research, 3(7), 3126–3136.
Zhang Z, Lu C, Xiang Z (1998). “Analysis of variety stability based on AMMI model.” Acta Agronomica Sinica, 24(3), 304–309. http://zwxb.chinacrops.org/EN/Y1998/V24/I03/304.
Zobel RW (1994). “Stress resistance and root systems.” In Proceedings of the Workshop on Adaptation of Plants to Soil Stress. 1-4 August, 1993. INTSORMIL Publication 94-2, 80–99. Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln.
Author
Tiago Olivoto tiagoolivoto@gmail.com
Examples
# \donttest{
library(metan)
model <-
performs_ammi(data_ge,
env = ENV,
gen = GEN,
rep = REP,
resp = c(GY, HM))
#> variable GY
#> ---------------------------------------------------------------------------
#> AMMI analysis table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F) Proportion Accumulated
#> ENV 13 279.574 21.5057 62.33 0.00e+00 NA NA
#> REP(ENV) 28 9.662 0.3451 3.57 3.59e-08 NA NA
#> GEN 9 12.995 1.4439 14.93 2.19e-19 NA NA
#> GEN:ENV 117 31.220 0.2668 2.76 1.01e-11 NA NA
#> PC1 21 10.749 0.5119 5.29 0.00e+00 34.4 34.4
#> PC2 19 9.924 0.5223 5.40 0.00e+00 31.8 66.2
#> PC3 17 4.039 0.2376 2.46 1.40e-03 12.9 79.2
#> PC4 15 3.074 0.2049 2.12 9.60e-03 9.8 89.0
#> PC5 13 1.446 0.1113 1.15 3.18e-01 4.6 93.6
#> PC6 11 0.932 0.0848 0.88 5.61e-01 3.0 96.6
#> PC7 9 0.567 0.0630 0.65 7.53e-01 1.8 98.4
#> PC8 7 0.362 0.0518 0.54 8.04e-01 1.2 99.6
#> PC9 5 0.126 0.0252 0.26 9.34e-01 0.4 100.0
#> Residuals 252 24.367 0.0967 NA NA NA NA
#> Total 536 389.036 0.7258 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> variable HM
#> ---------------------------------------------------------------------------
#> AMMI analysis table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F) Proportion Accumulated
#> ENV 13 5710.32 439.255 57.22 1.11e-16 NA NA
#> REP(ENV) 28 214.93 7.676 2.70 2.20e-05 NA NA
#> GEN 9 269.81 29.979 10.56 7.41e-14 NA NA
#> GEN:ENV 117 1100.73 9.408 3.31 1.06e-15 NA NA
#> PC1 21 381.13 18.149 6.39 0.00e+00 34.6 34.6
#> PC2 19 319.43 16.812 5.92 0.00e+00 29.0 63.6
#> PC3 17 114.26 6.721 2.37 2.10e-03 10.4 74.0
#> PC4 15 81.96 5.464 1.92 2.18e-02 7.4 81.5
#> PC5 13 68.11 5.240 1.84 3.77e-02 6.2 87.7
#> PC6 11 59.07 5.370 1.89 4.10e-02 5.4 93.0
#> PC7 9 46.69 5.188 1.83 6.33e-02 4.2 97.3
#> PC8 7 26.65 3.808 1.34 2.32e-01 2.4 99.7
#> PC9 5 3.41 0.682 0.24 9.45e-01 0.3 100.0
#> Residuals 252 715.69 2.840 NA NA NA NA
#> Total 536 9112.21 17.000 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> All variables with significant (p < 0.05) genotype-vs-environment interaction
#> Done!
model_indexes <- ammi_indexes(model)
# Alternatively (and more intuitively) using %>%
# If resp is not declared, all traits are analyzed
res_ind <- data_ge %>%
performs_ammi(ENV, GEN, REP, verbose = FALSE) %>%
ammi_indexes()
rbind_fill_id(res_ind, .id = "TRAIT")
#> # A tibble: 20 × 43
#> TRAIT GEN Y Y_R ASTAB ASTAB_R ssiASTAB ASI ASI_R ASI_SSI ASV
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 GY G1 2.60 6 0.108 2 8 0.110 4 10 0.346
#> 2 GY G10 2.47 10 1.47 10 20 0.389 10 20 1.23
#> 3 GY G2 2.74 3 0.820 7 10 0.0792 2 5 0.249
#> 4 GY G3 2.96 2 0.0959 1 3 0.0359 1 3 0.113
#> 5 GY G4 2.64 5 0.363 4 9 0.189 7 12 0.594
#> 6 GY G5 2.54 7 0.259 3 10 0.137 5 12 0.430
#> 7 GY G6 2.53 8 0.440 6 14 0.0843 3 11 0.265
#> 8 GY G7 2.74 4 0.971 9 13 0.211 8 12 0.663
#> 9 GY G8 3.00 1 0.416 5 6 0.182 6 7 0.574
#> 10 GY G9 2.51 9 0.947 8 17 0.312 9 18 0.983
#> 11 HM G1 47.1 9 3.49 5 14 0.166 2 11 0.571
#> 12 HM G10 48.5 4 7.51 10 14 0.819 10 14 2.83
#> 13 HM G2 46.7 10 5.35 8 18 0.564 8 18 1.95
#> 14 HM G3 47.6 8 2.86 3 11 0.132 1 9 0.455
#> 15 HM G4 48.0 5 4.26 6 11 0.538 7 12 1.86
#> 16 HM G5 49.3 1 6.35 9 10 0.623 9 10 2.15
#> 17 HM G6 48.7 3 2.22 1 4 0.202 3 6 0.698
#> 18 HM G7 48.0 6 3.39 4 10 0.443 6 12 1.53
#> 19 HM G8 49.1 2 2.42 2 4 0.360 4 6 1.24
#> 20 HM G9 47.9 7 4.36 7 14 0.372 5 12 1.28
#> # ℹ 32 more variables: ASV_R <dbl>, ASV_SSI <dbl>, AVAMGE <dbl>,
#> # AVAMGE_R <dbl>, AVAMGE_SSI <dbl>, DA <dbl>, DA_R <dbl>, DA_SSI <dbl>,
#> # DZ <dbl>, DZ_R <dbl>, DZ_SSI <dbl>, EV <dbl>, EV_R <dbl>, EV_SSI <dbl>,
#> # FA <dbl>, FA_R <dbl>, FA_SSI <dbl>, MASI <dbl>, MASI_R <dbl>,
#> # MASI_SSI <dbl>, MASV <dbl>, MASV_R <dbl>, MASV_SSI <dbl>, SIPC <dbl>,
#> # SIPC_R <dbl>, SIPC_SSI <dbl>, ZA <dbl>, ZA_R <dbl>, ZA_SSI <dbl>,
#> # WAAS <dbl>, WAAS_R <dbl>, WAAS_SSI <dbl>
# }