Print the gamem 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 'gamem'
print(x, export = FALSE, file.name = NULL, digits = 4, ...)Arguments
- x
An object fitted with the function
gamem().- export
A logical argument. If
TRUE, a *.txt file is exported to the working directory- 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)
alpha <- gamem(data_alpha,
gen = GEN,
rep = REP,
block = BLOCK,
resp = YIELD
)
#> Evaluating trait YIELD |=========================================| 100% 00:00:00
#> Method: REML/BLUP
#> Random effects: GEN, BLOCK(REP)
#> Fixed effects: REP
#> Denominador DF: Satterthwaite's method
#> ---------------------------------------------------------------------------
#> P-values for Likelihood Ratio Test of the analyzed traits
#> ---------------------------------------------------------------------------
#> model YIELD
#> Complete NA
#> Genotype 1.18e-06
#> rep:block 3.35e-03
#> ---------------------------------------------------------------------------
#> All variables with significant (p < 0.05) genotype effect
print(alpha)
#> Variable YIELD
#> ---------------------------------------------------------------------------
#> Fixed-effect anova table
#> ---------------------------------------------------------------------------
#> # A tibble: 1 × 7
#> SOURCE `Sum Sq` `Mean Sq` NumDF DenDF `F value` `Pr(>F)`
#> <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 REP 1.381 0.6907 2 11.76 8.463 0.005287
#> ---------------------------------------------------------------------------
#> Variance components for random effects
#> ---------------------------------------------------------------------------
#> # A tibble: 3 × 2
#> Group Variance
#> <chr> <dbl>
#> 1 GEN 0.1429
#> 2 REP:BLOCK 0.07022
#> 3 Residual 0.08162
#> ---------------------------------------------------------------------------
#> Likelihood ratio test for random effects
#> ---------------------------------------------------------------------------
#> # A tibble: 3 × 7
#> model npar logLik AIC LRT Df `Pr(>Chisq)`
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Complete 6 -46.60 105.2 NA NA NA
#> 2 Genotype 5 -58.41 126.8 23.62 1 0.000001176
#> 3 rep:block 5 -50.90 111.8 8.606 1 0.003351
#> ---------------------------------------------------------------------------
#> Details of the analysis
#> ---------------------------------------------------------------------------
#> # A tibble: 6 × 2
#> Parameters Values
#> <chr> <chr>
#> 1 Ngen 24
#> 2 OVmean 4.4795
#> 3 Min 2.8873 (G03 in B6 of R3)
#> 4 Max 5.8757 (G05 in B1 of R1)
#> 5 MinGEN 3.3431 (G03)
#> 6 MaxGEN 5.1625 (G01)
#> ---------------------------------------------------------------------------
#> Genetic parameters
#> ---------------------------------------------------------------------------
#> # A tibble: 13 × 2
#> Parameters Values
#> <chr> <dbl>
#> 1 Gen_var 0.1429
#> 2 Gen (%) 48.48
#> 3 rep:block_var 0.07022
#> 4 rep:block (%) 23.82
#> 5 Res_var 0.08162
#> 6 Res (%) 27.69
#> 7 Phen_var 0.2947
#> 8 H2 0.4848
#> 9 h2mg 0.7980
#> 10 Accuracy 0.8933
#> 11 CVg 8.439
#> 12 CVr 6.378
#> 13 CV ratio 1.323
#>
#>
#>
# }