Print the waas 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 waas
print(x, export = FALSE, file.name = NULL, digits = 4, ...)Arguments
- x
An object of class
waas.- 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 <- waas(data_ge,
resp = c(GY, HM),
gen = GEN,
env = ENV,
rep = REP
)
#> 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 NA NA
#> REP(ENV) 28 9.662 0.3451 3.57 3.59e-08 NA NA
#> GEN 9 12.995 1.4439 14.93 2.19e-19 NA NA
#> GEN:ENV 117 31.220 0.2668 2.76 1.01e-11 NA NA
#> 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 89.0
#> 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 3.0 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 0.4 100.0
#> Residuals 252 24.367 0.0967 NA NA 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 NA NA
#> REP(ENV) 28 214.93 7.676 2.70 2.20e-05 NA NA
#> GEN 9 269.81 29.979 10.56 7.41e-14 NA NA
#> GEN:ENV 117 1100.73 9.408 3.31 1.06e-15 NA NA
#> 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 29.0 63.6
#> PC3 17 114.26 6.721 2.37 2.10e-03 10.4 74.0
#> 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 93.0
#> 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 0.3 100.0
#> Residuals 252 715.69 2.840 NA NA 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
#> ---------------------------------------------------------------------------
#> Individual analysis of variance
#> ---------------------------------------------------------------------------
#> NULL
#> ---------------------------------------------------------------------------
#> 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 NA
#> 2 REP(ENV) 28 9.661516 0.34505416 3.568548 3.593191e-08 NA
#> 3 GEN 9 12.995044 1.44389374 14.932741 2.190118e-19 NA
#> 4 GEN:ENV 117 31.219565 0.26683389 2.759595 1.005191e-11 NA
#> 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 3.0
#> 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
#> 14 Residuals 252 24.366674 0.09669315 NA NA NA
#> 15 Total 536 389.035920 0.72581328 NA NA NA
#> Accumulated
#> 1 NA
#> 2 NA
#> 3 NA
#> 4 NA
#> 5 34.4
#> 6 66.2
#> 7 79.2
#> 8 89.0
#> 9 93.6
#> 10 96.6
#> 11 98.4
#> 12 99.6
#> 13 100.0
#> 14 NA
#> 15 NA
#> ---------------------------------------------------------------------------
#> Weighted average of the absolute scores
#> ---------------------------------------------------------------------------
#> # A tibble: 24 × 22
#> 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 13 more variables: PC7 <dbl>, PC8 <dbl>, PC9 <dbl>,
#> # WAAS <dbl>, PctResp <dbl>, PctWAAS <dbl>, wRes <dbl>, wWAAS <dbl>,
#> # OrResp <dbl>, OrWAAS <dbl>, OrPC1 <dbl>, WAASY <dbl>, OrWAASY <dbl>
#> ---------------------------------------------------------------------------
#> 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 analysis of variance
#> ---------------------------------------------------------------------------
#> NULL
#> ---------------------------------------------------------------------------
#> 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 NA
#> 2 REP(ENV) 28 214.93065 7.676095 2.702830 2.196589e-05 NA
#> 3 GEN 9 269.81118 29.979019 10.555915 7.414690e-14 NA
#> 4 GEN:ENV 117 1100.73412 9.407984 3.312646 1.063605e-15 NA
#> 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 29.0
#> 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
#> 14 Residuals 252 715.68528 2.840021 NA NA NA
#> 15 Total 536 9112.21209 17.000396 NA NA NA
#> Accumulated
#> 1 NA
#> 2 NA
#> 3 NA
#> 4 NA
#> 5 34.6
#> 6 63.6
#> 7 74.0
#> 8 81.5
#> 9 87.7
#> 10 93.0
#> 11 97.3
#> 12 99.7
#> 13 100.0
#> 14 NA
#> 15 NA
#> ---------------------------------------------------------------------------
#> Weighted average of the absolute scores
#> ---------------------------------------------------------------------------
#> # A tibble: 24 × 22
#> 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 13 more variables: PC7 <dbl>, PC8 <dbl>, PC9 <dbl>,
#> # WAAS <dbl>, PctResp <dbl>, PctWAAS <dbl>, wRes <dbl>, wWAAS <dbl>,
#> # OrResp <dbl>, OrWAAS <dbl>, OrPC1 <dbl>, WAASY <dbl>, OrWAASY <dbl>
#> ---------------------------------------------------------------------------
#> 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"
#>
#>
#>
# }