Print a waasb
object in two ways. By default, the results are shown in
the R console. The results can also be exported to the directory.
# S3 method for waasb print(x, export = FALSE, blup = FALSE, file.name = NULL, digits = 4, ...)
x | An object of class |
---|---|
export | A logical argument. If |
blup | A logical argument. If |
file.name | The name of the file if |
digits | The significant digits to be shown. |
... | Options used by the tibble package to format the output. See
|
Tiago Olivoto tiagoolivoto@gmail.com
# \donttest{ library(metan) model <- waasb(data_ge, resp = c(GY, HM), gen = GEN, env = ENV, rep = REP )#>#>#>#>#> --------------------------------------------------------------------------- #> P-values for Likelihood Ratio Test of the analyzed traits #> --------------------------------------------------------------------------- #> model GY HM #> COMPLETE NA NA #> GEN 1.11e-05 5.07e-03 #> GEN:ENV 2.15e-11 2.27e-15 #> --------------------------------------------------------------------------- #> All variables with significant (p < 0.05) genotype-vs-environment interactionprint(model)#> Variable GY #> --------------------------------------------------------------------------- #> Individual fixed-model analysis of variance #> --------------------------------------------------------------------------- #> NULL #> --------------------------------------------------------------------------- #> Fixed effects #> --------------------------------------------------------------------------- #> # A tibble: 2 x 7 #> SOURCE `Sum Sq` `Mean Sq` NumDF DenDF `F value` `Pr(>F)` #> <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl> #> 1 ENV 66.09 5.084 13 279.8 52.58 2.954e-67 #> 2 ENV:REP 9.662 0.3451 28 252.0 3.569 3.593e- 8 #> --------------------------------------------------------------------------- #> Random effects #> --------------------------------------------------------------------------- #> # A tibble: 3 x 3 #> Group Variance Percent #> <chr> <dbl> <dbl> #> 1 GEN 0.02803 15.45 #> 2 GEN:ENV 0.05671 31.26 #> 3 Residual 0.09669 53.29 #> --------------------------------------------------------------------------- #> Likelihood ratio test #> --------------------------------------------------------------------------- #> model npar logLik AIC LRT Df Pr(>Chisq) #> [1,] 1 45 -214.72 519.43 #> [2,] 2 44 -224.38 536.75 19.315 1 1.108e-05 *** #> [3,] 3 44 -237.13 562.27 44.832 1 2.147e-11 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> --------------------------------------------------------------------------- #> Variance components and genetic parameters #> --------------------------------------------------------------------------- #> # A tibble: 9 x 2 #> Parameters Values #> <chr> <dbl> #> 1 Phenotypic variance 0.1814 #> 2 Heritability 0.1545 #> 3 GEIr2 0.3126 #> 4 h2mg 0.8152 #> 5 Accuracy 0.9029 #> 6 rge 0.3697 #> 7 CVg 6.260 #> 8 CVr 11.63 #> 9 CV ratio 0.5384 #> --------------------------------------------------------------------------- #> Principal component analysis of the G x E interaction matrix #> --------------------------------------------------------------------------- #> # A tibble: 9 x 4 #> PC Eigenvalue Proportion Accumulated #> <chr> <dbl> <dbl> <dbl> #> 1 PC1 1.472 34.31 34.31 #> 2 PC2 1.347 31.38 65.69 #> 3 PC3 0.5479 12.77 78.46 #> 4 PC4 0.4167 9.710 88.17 #> 5 PC5 0.2126 4.955 93.12 #> 6 PC6 0.1397 3.256 96.38 #> 7 PC7 0.07912 1.844 98.22 #> 8 PC8 0.05673 1.322 99.55 #> 9 PC9 0.01947 0.4537 100. #> --------------------------------------------------------------------------- #> Some information regarding the analysis #> --------------------------------------------------------------------------- #> # A tibble: 14 x 2 #> Parameters Values #> <chr> <chr> #> 1 Mean "2.67" #> 2 SE "0.05" #> 3 SD "0.92" #> 4 CV "34.56" #> 5 Min "0.67 (G10 in E11)" #> 6 Max "5.09 (G8 in E5)" #> 7 MinENV "E11 (1.37)" #> 8 MaxENV "E3 (4.06)" #> 9 MinGEN "G10 (2.47) " #> 10 MaxGEN "G8 (3) " #> 11 wresp "50" #> 12 mresp "100" #> 13 Ngen "10" #> 14 Nenv "14" #> #> #> #> Variable HM #> --------------------------------------------------------------------------- #> Individual fixed-model analysis of variance #> --------------------------------------------------------------------------- #> NULL #> --------------------------------------------------------------------------- #> Fixed effects #> --------------------------------------------------------------------------- #> # A tibble: 2 x 7 #> SOURCE `Sum Sq` `Mean Sq` NumDF DenDF `F value` `Pr(>F)` #> <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl> #> 1 ENV 1166. 89.69 13 257.4 31.58 5.708e-46 #> 2 ENV:REP 214.9 7.676 28 252.0 2.703 2.197e- 5 #> --------------------------------------------------------------------------- #> Random effects #> --------------------------------------------------------------------------- #> # A tibble: 3 x 3 #> Group Variance Percent #> <chr> <dbl> <dbl> #> 1 GEN 0.4898 8.874 #> 2 GEN:ENV 2.189 39.67 #> 3 Residual 2.840 51.46 #> --------------------------------------------------------------------------- #> Likelihood ratio test #> --------------------------------------------------------------------------- #> model npar logLik AIC LRT Df Pr(>Chisq) #> [1,] 1 45 -862.67 1815.3 #> [2,] 2 44 -866.59 1821.2 7.855 1 0.005068 ** #> [3,] 3 44 -894.08 1876.2 62.819 1 2.266e-15 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> --------------------------------------------------------------------------- #> Variance components and genetic parameters #> --------------------------------------------------------------------------- #> # A tibble: 9 x 2 #> Parameters Values #> <chr> <dbl> #> 1 Phenotypic variance 5.519 #> 2 Heritability 0.08874 #> 3 GEIr2 0.3967 #> 4 h2mg 0.6862 #> 5 Accuracy 0.8284 #> 6 rge 0.4353 #> 7 CVg 1.455 #> 8 CVr 3.504 #> 9 CV ratio 0.4153 #> --------------------------------------------------------------------------- #> Principal component analysis of the G x E interaction matrix #> --------------------------------------------------------------------------- #> # A tibble: 9 x 4 #> PC Eigenvalue Proportion Accumulated #> <chr> <dbl> <dbl> <dbl> #> 1 PC1 61.96 33.83 33.83 #> 2 PC2 51.91 28.34 62.18 #> 3 PC3 19.82 10.82 73.00 #> 4 PC4 13.90 7.588 80.59 #> 5 PC5 11.50 6.282 86.87 #> 6 PC6 10.38 5.665 92.53 #> 7 PC7 8.392 4.582 97.11 #> 8 PC8 4.583 2.502 99.62 #> 9 PC9 0.7016 0.3831 100 #> --------------------------------------------------------------------------- #> Some information regarding the analysis #> --------------------------------------------------------------------------- #> # A tibble: 14 x 2 #> Parameters Values #> <chr> <chr> #> 1 Mean "48.09" #> 2 SE "0.21" #> 3 SD "4.37" #> 4 CV "9.09" #> 5 Min "38 (G2 in E14)" #> 6 Max "58 (G8 in E11)" #> 7 MinENV "E14 (41.03)" #> 8 MaxENV "E11 (54.2)" #> 9 MinGEN "G2 (46.66) " #> 10 MaxGEN "G5 (49.3) " #> 11 wresp "50" #> 12 mresp "100" #> 13 Ngen "10" #> 14 Nenv "14" #> #> #># }