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.
Usage
# S3 method for class 'waasb'
print(x, export = FALSE, blup = FALSE, file.name = NULL, digits = 4, ...)Arguments
- x
An object of class
waasb.- export
A logical argument. If
TRUE|T, a *.txt file is exported to the working directory- blup
A logical argument. If
TRUE|T, the blups are shown.- file.name
The name of the file if
export = TRUE- digits
The significant digits to be shown.
- ...
Options used by the tibble package to format the output. See
tibble::print()for more details.
Author
Tiago Olivoto tiagoolivoto@gmail.com
Examples
# \donttest{
library(metan)
model <- waasb(data_ge,
resp = c(GY, HM),
gen = GEN,
env = ENV,
rep = REP
)
#> Evaluating trait GY |====================== | 50% 00:00:01
Evaluating trait HM |============================================| 100% 00:00:02
#> Method: REML/BLUP
#> Random effects: GEN, GEN:ENV
#> Fixed effects: ENV, REP(ENV)
#> Denominador DF: Satterthwaite's method
#> ---------------------------------------------------------------------------
#> 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 interaction
print(model)
#> Variable GY
#> ---------------------------------------------------------------------------
#> Individual fixed-model analysis of variance
#> ---------------------------------------------------------------------------
#> NULL
#> ---------------------------------------------------------------------------
#> Fixed effects
#> ---------------------------------------------------------------------------
#> # A tibble: 2 × 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 × 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)
#> <none> 1 45 -214.72 519.43
#> (1 | GEN) 2 44 -224.38 536.75 19.315 1 1.108e-05 ***
#> (1 | GEN:ENV) 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 × 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 × 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 × 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 × 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 × 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)
#> <none> 1 45 -862.67 1815.3
#> (1 | GEN) 2 44 -866.59 1821.2 7.855 1 0.005068 **
#> (1 | GEN:ENV) 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 × 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 × 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 × 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"
#>
#>
#>
# }