Get data from a model easily
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 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.A tibble showing the values of the variable chosen in argument what.
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 ‘colindiag’:
"cormat" The correlation matrix betwen predictors."corlist" The correlations in a 'long' format"evalevet" The eigenvalue with associated eigenvectors"VIF" The Variance Inflation Factor"indicators" The colinearity indicatorsObjects 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-valuepval_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’:
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 plotObjects 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.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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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()
Tiago Olivoto tiagoolivoto@gmail.com
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