metan (version 1.2.1)

get_model_data: Get data from a model easily

Description

Easily get data from some objects generated in the metan package such as the WAASB and WAASBY indexes (Olivoto et al., 2019a, 2019b) BLUPs, variance components, details of AMMI models and AMMI-based stability statistics.

Usage

get_model_data(x, what = NULL, type = "GEN")

Arguments

x

An object created with the functions AMMI_indexes, ecovalence, Fox, gai, gamem, ge_means, ge_reg, performs_ammi, Resende_indexes, Shukla, superiority, waas or waasb.

what

What should be captured from the model. See more in section Details.

type

Chose if the statistics must be show by genotype (type = "GEN", default) or environment (type = "ENV"), when possible.

Value

A tibble showing the values of the variable chosen in argument what.

Details

Bellow are listed the options allowed in the argument what depending on the class of the object

Objects of class AMMI_indexes:

• "ASV" AMMI stability value.

• "EV" Averages of the squared eigenvector values.

• "SIPC" Sums of the absolute value of the IPCA scores.

• "WAAS" Weighted average of absolute scores (default).

• "ZA" Absolute value of the relative contribution of IPCAs to the interaction.

Objects of class Annicchiarico and Schmildt:

• "Sem_rp" The standard error of the relative mean performance (Schmildt).

• "Mean_rp" The relative performance of the mean.

• "rank" The rank for genotypic confidence index.

• "Wi" The genotypic confidence index.

Objects of class ecovalence:

• "Ecoval" Ecovalence value (default).

• "Ecov_perc" Ecovalence in percentage value.

• "rank" Rank for ecovalence.

Objects of class ge_reg:

• "deviations" The deviations from regression.

• "RMSE" The Root Mean Square Error.

• "R2" The r-square of the regression.

• "slope" The sloop of the regression (default).

Objects of class ge_effects:

• For objects of class ge_effects no argument what is required.

Objects of class ge_means:

• "ge_means" Genotype-environment interaction means (default).

• "env_means" Environment means.

• "gen_means" Genotype means.

Objects of class Shukla:

• "rMean" Rank for the mean.

• "ShuklaVar" Shukla's stablity variance (default).

• "rShukaVar" Rank for Shukla's stablity variance.

• "ssiShukaVar" Simultaneous selection index.

Objects of class Fox:

• "TOP" The proportion of locations at which the genotype occurred in the top third (default).

Objects of class gai:

• "GAI" The geometric adaptability index (default).

• "GAI_R" The rank for the GAI values.

Objects of class superiority:

• "Pi_a" The superiority measure for all environments (default).

• "R_a" The rank for Pi_a.

• "Pi_f" The superiority measure for favorable environments.

• "R_f" The rank for Pi_f.

• "Pi_u" The superiority measure for unfavorable environments.

• "R_u" The rank for Pi_u.

Objects of class Huehn:

• "S1" Mean of the absolute rank differences of a genotype over the n environments (default).

• "S2" variance among the ranks over the k environments.

• "S3" Sum of the absolute deviations.

• "S6" Relative sum of squares of rank for each genotype.

• "S1_R", "S2_R", "S3_R", and "S6_R", the ranks for S1, S2, S3, and S6, respectively.

Objects of class Thennarasu:

• "N1" First statistic (default).

• "N2" Second statistic.

• "N3" Third statistic.

• "N4" Fourth statistic.

• "N1_R", "N2_R", "N3_R", and "N4_R", The ranks for the statistics.

Objects of class performs_ammi:

• "PC1", "PC2", ..., "PCn" The values for the nth interaction principal component axis.

• "ipca_ss" Sum of square for each IPCA.

• "ipca_ms" Mean square for each IPCA.

• "ipca_fval" F value for each IPCA.

• "ipca_pval" P-value for for each IPCA.

• "ipca_expl" Explained sum of square for each IPCA (default).

• "ipca_accum" Accumulated explained sum of square.

Objects of class waas, waas_means, and waasb:

• "PC1", "PC2", ..., "PCn" The values for the nth interaction principal component axis.

• "WAASB" The weighted average of the absolute scores (default for objects of class waas).

• "PctResp" The rescaled values of the response variable.

• "PctWAASB" The rescaled values of the WAASB.

• "wResp" The weight for the response variable.

• "wWAASB" The weight for the stability.

• "OrResp" The ranking regarding the response variable.

• "OrWAASB" The ranking regarding the WAASB.

• "OrPC1" The ranking regarding the first principal component axix.

• "WAASBY" The superiority index WAASBY.

• "OrWAASBY" The ranking regarding the superiority index.

Objects of class waasb or gamem:

• "blupg" For genotype's predicted mean.

• "blupge" for genotype-vs-environment's predicted mean (only for objects of class waasb).

• "genpar" Genetic parameters (default).

• "lrt" The statistic for the likelihood-ratio test for random effects.

• "pval_lrt" The p-values for the likelihood-ratio test.

• "vcomp" The variance components for random effects.

Objects of class Res_ind

• "HMGV" For harmonic mean of genotypic values.

• "RPGV or RPGV_Y" For relative performance of genotypic values

• "HMRPGV" For harmonic mean of relative performance of genotypic values

References

Annicchiarico, P. 1992. Cultivar adaptation and recommendation from alfalfa trials in Northern Italy. J. Genet. Breed. 46:269-278.

Dias, P.C., A. Xavier, M.D.V. de Resende, M.H.P. Barbosa, F.A. Biernaski, R.A. Estopa. 2018. Genetic evaluation of Pinus taeda clones from somatic embryogenesis and their genotype x environment interaction. Crop Breed. Appl. Biotechnol. 18:55-64. doi:10.1590/1984-70332018v18n1a8

Azevedo Peixoto, L. de, P.E. Teodoro, L.A. Silva, E.V. Rodrigues, B.G. Laviola, and L.L. Bhering. 2018. Jatropha half-sib family selection with high adaptability and genotypic stability. PLoS One 13:e0199880. doi:10.1371/journal.pone.0199880

Eberhart, S.A., and W.A. Russell. 1966. Stability parameters for comparing Varieties. Crop Sci. 6:36-40. doi:10.2135/cropsci1966.0011183X000600010011x.

Fox, P.N., B. Skovmand, B.K. Thompson, H.J. Braun, and R. Cormier. 1990. Yield and adaptation of hexaploid spring triticale. Euphytica 47:57-64. doi:10.1007/BF00040364.

Huehn, V.M. 1979. Beitrage zur erfassung der phanotypischen stabilitat. EDV Med. Biol. 10:112.

Olivoto, T., A.D.C. L\'ucio, J.A.G. da silva, V.S. Marchioro, V.Q. de Souza, and E. Jost. 2019a. 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

Olivoto, T., A.D.C. L\'ucio, J.A.G. da silva, B.G. Sari, and M.I. Diel. 2019b. Mean performance and stability in multi-environment trials II: Selection based on multiple traits. Agron. J. 111:2961-2969. doi:10.2134/agronj2019.03.0221

Purchase, J.L., H. Hatting, and C.S. van Deventer. 2000. Genotype vs environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. South African J. Plant Soil 17:101-107. doi:10.1080/02571862.2000.10634878

Resende MDV (2007) Matematica e estatistica na analise de experimentos e no melhoramento genetico. Embrapa Florestas, Colombo

Sneller, C.H., L. Kilgore-Norquest, and D. Dombek. 1997. Repeatability of Yield Stability Statistics in Soybean. Crop Sci. 37:383-390. doi:10.2135/cropsci1997.0011183X003700020013x

Shahbazi, E. 2019. Genotype selection and stability analysis for seed yield of Nigella sativa using parametric and non-parametric statistics. Sci. Hortic. (Amsterdam). 253:172-179. doi:10.1016/j.scienta.2019.04.047.

Wricke, G. 1965. Zur berechnung der okovalenz bei sommerweizen und hafer. Z. Pflanzenzuchtg 52:127-138.

Zali, H., E. Farshadfar, S.H. Sabaghpour, and R. Karimizadeh. 2012. Evaluation of genotype vs environment interaction in chickpea using measures of stability from AMMI model. Ann. Biol. Res. 3:3126-3136. http://eprints.icrisat.ac.in/id/eprint/7173

Examples

# NOT RUN {
library(metan)

#################### joint-regression analysis #####################
ge_r <- ge_reg(data_ge2, ENV, GEN, REP,
               resp = c(PH, EH, CD, CL, ED))
get_model_data(ge_r)
get_model_data(ge_r, "deviations")


#################### AMMI model #####################
# Fit an AMMI model for 7 variables.
AMMI <- data_ge2 %>%
 performs_ammi(ENV, GEN, REP,
               resp = c(PH, ED, TKW, NKR, CD, CL, CW))

# Sum of squares
get_model_data(AMMI, "ipca_ss")

# Mean squares
get_model_data(AMMI, "ipca_ms")

# Examine the significance (p-value) of the IPCAs
get_model_data(AMMI, "ipca_pval")

# Explained sum of square for each IPCA
get_model_data(AMMI)

# Accumulated sum of square
get_model_data(AMMI, "ipca_accum")

### AMMI-based stability statistics ###
# Get the AMMI stability value
AMMI %>%
AMMI_indexes() %>%
get_model_data("ASV")


#################### WAASB model #####################
# Fitting the WAAS index
AMMI <- waas(data_ge2, ENV, GEN, REP,
             resp = c(PH, ED, TKW, NKR))

# Getting the weighted average of absolute scores
get_model_data(AMMI, what = "WAASB")

# And the rank for the WAASB index.
get_model_data(AMMI, what = "OrWAASB")


#################### BLUP model #####################
# Fitting a mixed-effect model
blup <- waasb(data_ge2, ENV, GEN, REP,
              resp = c(PH, ED, TKW, NKR))

# Getting p-values for likelihood-ratio test
get_model_data(blup, what = "pval_lrt")

# Getting the variance components
get_model_data(blup, what = "vcomp")

# Getting the genetic parameters
get_model_data(blup)

### BLUP-based stability indexes ###
blup %>%
Resende_indexes() %>%
get_model_data()


#################### Stability indexes #####################
stats_ge <- ge_stats(data_ge, ENV, GEN, REP, everything())
get_model_data(stats_ge)
# }
# NOT RUN {
# }