Print the performs_ammi 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 performs_ammi
print(x, export = FALSE, file.name = NULL, digits = 4, ...)

Arguments

x

An object of class performs_ammi.

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) model <- performs_ammi(data_ge, ENV, GEN, REP, resp = c(GY, HM))
#> variable GY #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Proportion Accumulated #> ENV 13 279.574 21.5057 62.33 0.00e+00 . . #> REP(ENV) 28 9.662 0.3451 3.57 3.59e-08 . . #> GEN 9 12.995 1.4439 14.93 2.19e-19 . . #> GEN:ENV 117 31.220 0.2668 2.76 1.01e-11 . . #> PC1 21 10.749 0.5119 5.29 0.00e+00 34.4 34.4 #> PC2 19 9.924 0.5223 5.40 0.00e+00 31.8 66.2 #> PC3 17 4.039 0.2376 2.46 1.40e-03 12.9 79.2 #> PC4 15 3.074 0.2049 2.12 9.60e-03 9.8 89 #> PC5 13 1.446 0.1113 1.15 3.18e-01 4.6 93.6 #> PC6 11 0.932 0.0848 0.88 5.61e-01 3 96.6 #> PC7 9 0.567 0.0630 0.65 7.53e-01 1.8 98.4 #> PC8 7 0.362 0.0518 0.54 8.04e-01 1.2 99.6 #> PC9 5 0.126 0.0252 0.26 9.34e-01 0.4 100 #> Residuals 252 24.367 0.0967 NA NA . . #> Total 536 389.036 0.7258 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #> #> variable HM #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Proportion Accumulated #> ENV 13 5710.32 439.255 57.22 1.11e-16 . . #> REP(ENV) 28 214.93 7.676 2.70 2.20e-05 . . #> GEN 9 269.81 29.979 10.56 7.41e-14 . . #> GEN:ENV 117 1100.73 9.408 3.31 1.06e-15 . . #> PC1 21 381.13 18.149 6.39 0.00e+00 34.6 34.6 #> PC2 19 319.43 16.812 5.92 0.00e+00 29 63.6 #> PC3 17 114.26 6.721 2.37 2.10e-03 10.4 74 #> PC4 15 81.96 5.464 1.92 2.18e-02 7.4 81.5 #> PC5 13 68.11 5.240 1.84 3.77e-02 6.2 87.7 #> PC6 11 59.07 5.370 1.89 4.10e-02 5.4 93 #> PC7 9 46.69 5.188 1.83 6.33e-02 4.2 97.3 #> PC8 7 26.65 3.808 1.34 2.32e-01 2.4 99.7 #> PC9 5 3.41 0.682 0.24 9.45e-01 0.3 100 #> Residuals 252 715.69 2.840 NA NA . . #> Total 536 9112.21 17.000 NA NA <NA> <NA> #> --------------------------------------------------------------------------- #> #> All variables with significant (p < 0.05) genotype-vs-environment interaction #> Done!
print(model)
#> Variable GY #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Proportion #> 1 ENV 13 279.573552 21.50565785 62.325457 0.000000e+00 . #> 2 REP(ENV) 28 9.661516 0.34505416 3.568548 3.593191e-08 . #> 3 GEN 9 12.995044 1.44389374 14.932741 2.190118e-19 . #> 4 GEN:ENV 117 31.219565 0.26683389 2.759595 1.005191e-11 . #> 5 PC1 21 10.749140 0.51186000 5.290000 0.000000e+00 34.4 #> 6 PC2 19 9.923920 0.52231000 5.400000 0.000000e+00 31.8 #> 7 PC3 17 4.039180 0.23760000 2.460000 1.400000e-03 12.9 #> 8 PC4 15 3.073770 0.20492000 2.120000 9.600000e-03 9.8 #> 9 PC5 13 1.446440 0.11126000 1.150000 3.176000e-01 4.6 #> 10 PC6 11 0.932240 0.08475000 0.880000 5.606000e-01 3 #> 11 PC7 9 0.566700 0.06297000 0.650000 7.535000e-01 1.8 #> 12 PC8 7 0.362320 0.05176000 0.540000 8.037000e-01 1.2 #> 13 PC9 5 0.125860 0.02517000 0.260000 9.345000e-01 0.4 #> 51 Residuals 252 24.366674 0.09669315 NA NA . #> 14 Total 536 389.035920 0.72581328 NA NA <NA> #> Accumulated #> 1 . #> 2 . #> 3 . #> 4 . #> 5 34.4 #> 6 66.2 #> 7 79.2 #> 8 89 #> 9 93.6 #> 10 96.6 #> 11 98.4 #> 12 99.6 #> 13 100 #> 51 . #> 14 <NA> #> --------------------------------------------------------------------------- #> Scores for genotypes and environments #> --------------------------------------------------------------------------- #> # A tibble: 24 x 12 #> type Code Y PC1 PC2 PC3 PC4 PC5 PC6 #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 GEN G1 2.604 0.3166 -0.04417 -0.03600 -0.06595 -0.3125 0.4272 #> 2 GEN G10 2.471 -1.001 -0.5718 -0.1652 -0.3309 -0.1243 -0.1064 #> 3 GEN G2 2.744 0.1390 0.1988 -0.7331 0.4735 -0.04816 -0.2841 #> 4 GEN G3 2.955 0.04340 -0.1028 0.2284 0.1769 -0.1270 -0.1400 #> 5 GEN G4 2.642 -0.3251 0.4782 -0.09073 0.1417 -0.1924 0.3550 #> 6 GEN G5 2.537 -0.3260 0.2461 0.2452 0.1794 0.4662 0.03315 #> 7 GEN G6 2.534 -0.09836 0.2429 0.5607 0.2377 0.05094 -0.1011 #> 8 GEN G7 2.741 0.2849 0.5871 -0.2068 -0.7085 0.2315 -0.08406 #> 9 GEN G8 3.004 0.4995 -0.1916 0.3191 -0.1676 -0.3261 -0.2886 #> 10 GEN G9 2.510 0.4668 -0.8427 -0.1217 0.06385 0.3819 0.1889 #> # ... with 14 more rows, and 3 more variables: PC7 <dbl>, PC8 <dbl>, PC9 <dbl> #> #> #> #> Variable HM #> --------------------------------------------------------------------------- #> AMMI analysis table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) Proportion #> 1 ENV 13 5710.31673 439.255133 57.223777 1.110223e-16 . #> 2 REP(ENV) 28 214.93065 7.676095 2.702830 2.196589e-05 . #> 3 GEN 9 269.81118 29.979019 10.555915 7.414690e-14 . #> 4 GEN:ENV 117 1100.73412 9.407984 3.312646 1.063605e-15 . #> 5 PC1 21 381.12827 18.148970 6.390000 0.000000e+00 34.6 #> 6 PC2 19 319.43319 16.812270 5.920000 0.000000e+00 29 #> 7 PC3 17 114.26443 6.721440 2.370000 2.100000e-03 10.4 #> 8 PC4 15 81.96192 5.464130 1.920000 2.180000e-02 7.4 #> 9 PC5 13 68.11488 5.239610 1.840000 3.770000e-02 6.2 #> 10 PC6 11 59.07451 5.370410 1.890000 4.100000e-02 5.4 #> 11 PC7 9 46.69408 5.188230 1.830000 6.330000e-02 4.2 #> 12 PC8 7 26.65417 3.807740 1.340000 2.318000e-01 2.4 #> 13 PC9 5 3.40867 0.681730 0.240000 9.445000e-01 0.3 #> 51 Residuals 252 715.68528 2.840021 NA NA . #> 14 Total 536 9112.21209 17.000396 NA NA <NA> #> Accumulated #> 1 . #> 2 . #> 3 . #> 4 . #> 5 34.6 #> 6 63.6 #> 7 74 #> 8 81.5 #> 9 87.7 #> 10 93 #> 11 97.3 #> 12 99.7 #> 13 100 #> 51 . #> 14 <NA> #> --------------------------------------------------------------------------- #> Scores for genotypes and environments #> --------------------------------------------------------------------------- #> # A tibble: 24 x 12 #> type Code Y PC1 PC2 PC3 PC4 PC5 PC6 #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 GEN G1 47.08 0.2800 0.4635 0.1740 -1.369 -1.135 0.03658 #> 2 GEN G10 48.51 -1.779 1.866 -0.006219 0.9219 0.1096 -0.009745 #> 3 GEN G2 46.66 1.563 0.5518 -0.9357 0.4913 0.2843 1.184 #> 4 GEN G3 47.60 0.3417 -0.2012 -0.8001 0.3753 -0.4979 -1.294 #> 5 GEN G4 48.03 -0.2020 -1.841 0.2801 0.005954 0.8201 0.2734 #> 6 GEN G5 49.30 1.580 1.030 1.078 -0.2789 1.005 -0.7368 #> 7 GEN G6 48.73 0.5474 -0.2453 0.5324 0.4603 -1.008 0.5861 #> 8 GEN G7 47.97 -1.218 -0.4680 1.254 -0.05482 -0.03429 0.3366 #> 9 GEN G8 49.10 -0.04176 -1.241 -0.4105 0.6394 -0.1785 -0.5149 #> 10 GEN G9 47.90 -1.072 0.08563 -1.166 -1.191 0.6351 0.1393 #> # ... with 14 more rows, and 3 more variables: PC7 <dbl>, PC8 <dbl>, PC9 <dbl> #> #> #>
# }