get_model_data: Get data from a model easily

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/get_model_data.R

Description

[Experimental]

Usage

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

gmd(x, what = NULL, type = "GEN", verbose = TRUE)

sel_gen(x)

Arguments

x

An object created with the functions ammi_indexes(), anova_ind(), anova_joint(), can_corr() ecovalence(), Fox(), gai(), gamem(),gafem(), ge_acv(), ge_means(), ge_reg(), gytb(), mgidi(), performs_ammi(), blup_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.

verbose

Logical argument. If verbose = FALSE the code will run silently.

Details

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

Objects of class ammi_indexes:

Objects of class anova_ind:

Objects of class anova_joint or gafem:

Objects of class Annicchiarico and Schmildt:

Objects of class can_corr:

Objects of class ecovalence:

Objects of class fai_blup: See the Value section of fai_blup() to see valid options for what argument.

Objects of class ge_acv:

Objects of class ge_polar:

Objects of class ge_reg:

Objects of class ge_effects:

Objects of class ge_means:

Objects of class gge:

Objects of class gytb:

Objects of class mgidi: See the Value section of mgidi() to see valid options for what argument.

Objects of class mtsi: See the Value section of mtsi() to see valid options for what argument.

Objects of class Shukla:

Objects of class sh: See the Value section of Smith_Hazel() to see valid options for what argument.

Objects of class Fox:

Objects of class gai:

Objects of class superiority:

Objects of class Huehn:

Objects of class Thennarasu:

Objects of class performs_ammi:

Objects of class waas, waas_means, and waasb:

Objects of class gamem and waasb:

Objects of class blup_ind

Value

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

Author(s)

Tiago Olivoto tiagoolivoto@gmail.com

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

Mohammadi, R., & Amri, A. (2008). Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica, 159(3), 419-432. doi: 10.1007/s10681-007-9600-6

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.

See Also

ammi_indexes(), anova_ind(), anova_joint(), ecovalence(), Fox(), gai(), gamem(), gafem(), ge_acv(), ge_polar() ge_means(), ge_reg(), mgidi(), mtsi(), mps(), mtmps(), performs_ammi(), blup_indexes(), Shukla(), superiority(), waas(), waasb()

Examples

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library(metan)

#################### joint-regression analysis #####################
ge_r <- ge_reg(data_ge2,
               env = ENV,
               gen = GEN,
               rep =  REP,
               resp = c(PH, EH, CD, CL, ED))
get_model_data(ge_r)
# Significance of deviations from the regression
# Use gmd(), a shortcut for get_model_data
gmd(ge_r, "pval_f")


#################### WAASB index #####################
# Fitting the WAAS index
AMMI <- waasb(data_ge2,
              env = ENV,
              gen = GEN,
              rep = REP,
              resp = c(PH, ED, TKW, NKR))

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

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


#################### BLUP model #####################
# Fitting a mixed-effect model
# Genotype and interaction as random
blup <- gamem_met(data_ge2,
                  env = ENV,
                  gen = GEN,
                  rep = REP,
                  resp = c(PH, ED, TKW, NKR))

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

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

# Getting the genetic parameters
gmd(blup)

### BLUP-based stability indexes ###
blup %>%
blup_indexes() %>%
gmd("HMRPGV_R")

metan documentation built on Nov. 10, 2021, 9:11 a.m.