View source: R/get_model_data.R
get_model_data | R Documentation |
get_model_data(x, what = NULL, type = "GEN", verbose = TRUE) gmd(x, what = NULL, type = "GEN", verbose = TRUE) sel_gen(x)
x |
An object created with the functions |
what |
What should be captured from the model. See more in section Details. |
type |
Chose if the statistics must be show by genotype ( |
verbose |
Logical argument. If |
get_model_data()
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.
gmd()
Is a shortcut to get_model_data
.
sel_gen()
Extracts the selected genotypes by a given index.
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 anova_ind
:
"MEAN"
The mean value of the variable
"DFG", "DFB", "DFCR", "DFIB_R", "DFE"
. The degree of freedom for
genotypes, blocks (randomized complete block design), complete replicates,
incomplete blocks within replicates (alpha-lattice design), and error,
respectively.
"MSG", "FCG", "PFG"
The mean square, F-calculated and P-values for
genotype effect, respectively.
"MSB", "FCB", "PFB"
The mean square, F-calculated and P-values for
block effect in randomized complete block design.
"MSCR", "FCR", "PFCR"
The mean square, F-calculated and P-values for
complete replicates in alpha lattice design.
"MSIB_R", "FCIB_R", "PFIB_R"
The mean square, F-calculated and
P-values for incomplete blocks within complete replicates, respectively (for
alpha lattice design only).
"MSE"
The mean square of error.
"CV"
The coefficient of variation.
"h2"
The broad-sence heritability
"AS"
The accucary of selection (square root of h2).
"FMAX"
The Hartley's test (the ratio of the largest MSE to the smallest
MSE).
Objects of class anova_joint
or gafem
:
"Y"
The observed values.
"h2"
The broad-sense heritability.
"Sum Sq"
Sum of squares.
"Mean Sq"
Mean Squares.
"F value"
F-values.
"Pr(>F)"
P-values.
".fitted"
Fitted values (default).
".resid"
Residuals.
".stdresid"
Standardized residuals.
".se.fit"
Standard errors of the fitted values.
"details"
Details.
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 can_corr
:
"coefs"
The canonical coefficients (default).
"loads"
The canonical loadings.
"crossloads"
The canonical cross-loadings.
"canonical"
The canonical correlations and hypothesis testing.
Objects of class ecovalence
:
"Ecoval"
Ecovalence value (default).
"Ecov_perc"
Ecovalence in percentage value.
"rank"
Rank for 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
:
"ACV"
The adjusted coefficient of variation (default).
"ACV_R"
The rank for adjusted coefficient of variation.
Objects of class ge_polar
:
"POLAR"
The Power Law Residuals (default).
"POLAR_R"
The rank for Power Law Residuals.
Objects of class ge_reg
:
GEN
: the genotypes.
b0
and b1
(default): the intercept and slope of the regression,
respectively.
t(b1=1)
: the calculated t-value
pval_t
: the p-value for the t test.
s2di
the deviations from the regression (stability parameter).
F(s2di=0)
: the F-test for the deviations.
pval_f
: the p-value for the F test;
RMSE
the root-mean-square error.
R2
the determination coefficient of the regression.
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 gge
:
"scores"
The scores for genotypes and environments for all the
analyzed traits (default).
"exp_var"
The eigenvalues and explained variance.
"projection"
The projection of each genotype in the AEC coordinates in
the stability GGE plot
Objects of class gytb
:
"gyt"
Genotype by yield*trait table (Default).
"stand_gyt"
The standardized (zero mean and unit variance) Genotype by yield*trait table.
"si"
The superiority index (sum standardized value across all yield*trait combinations).
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 path_coeff
"coef"
Path coefficients
"eigenval"
Eigenvalues and eigenvectors.
"vif "
Variance Inflation Factor
**Objects of class path_coeff_seq
"resp_fc"
Coefficients of primary predictors and response
"resp_sc"
Coefficients of secondary predictors and response
"resp_sc2"
contribution to the total effects through primary traits
"fc_sc_coef"
Coefficients of secondary predictors and primary predictors.
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 sh
: See the Value section of
Smith_Hazel()
to see valid options for what
argument.
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 gamem
and waasb
:
"blupge"
Best Linear Unbiased Prediction for genotype-environment
interaction (mixed-effect model, class waasb
).
"blupg"
Best Linear Unbiased Prediction for genotype effect.
"bluege"
Best Linear Unbiased Estimation for genotype-environment
interaction (fixed-effect model, class waasb
).
"blueg"
Best Linear Unbiased Estimation for genotype effect (fixed
model).
"data"
The data used.
"details"
The details of the trial.
"genpar"
Genetic parameters (default).
"gcov"
The genotypic variance-covariance matrix.
"pcov"
The phenotypic variance-covariance matrix.
"gcor"
The genotypic correlation matrix.
"pcor"
The phenotypic correlation matrix.
"h2"
The broad-sense heritability.
"lrt"
The likelihood-ratio test for random effects.
"vcomp"
The variance components for random effects.
"ranef"
Random effects.
Objects of class blup_ind
"HMGV","HMGV_R"
For harmonic mean of genotypic values or its ranks.
"RPGV", RPGV_Y"
For relative performance of genotypic values or its
ranks.
"HMRPGV", "HMRPGV_R"
For harmonic mean of relative performance of
genotypic values or its ranks.
"WAASB", "WAASB_R"
For the weighted average of absolute scores from the
singular or its ranks. value decomposition of the BLUPs for GxE interaction
or its ranks.
A tibble showing the values of the variable chosen in argument
what
.
Tiago Olivoto tiagoolivoto@gmail.com
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.
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()
library(metan) #################### 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") #################### 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)) # Getting p-values for likelihood-ratio test gmd(blup, what = "lrt") # Getting the variance components gmd(blup, what = "vcomp")
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