Anova - Individual
library(metan)
library(rio)
library(emmeans)
# gerar tabelas html
print_tbl <- function(table, digits = 3, ...){
knitr::kable(table, booktabs = TRUE, digits = digits, ...)
}
# dados
df <- import("http://bit.ly/df_ge", setclass = "tbl")
print(df)
## # A tibble: 156 x 13
## ENV GEN BLOCO ALT_PLANT ALT_ESP COMPES DIAMES COMP_SAB DIAM_SAB MGE
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 A1 H1 I 2.61 1.71 16.1 52.2 28.1 16.3 217.
## 2 A1 H1 II 2.87 1.76 14.2 50.3 27.6 14.5 184.
## 3 A1 H1 III 2.68 1.58 16.0 50.7 28.4 16.4 208.
## 4 A1 H10 I 2.83 1.64 16.7 54.1 31.7 17.4 194.
## 5 A1 H10 II 2.79 1.71 14.9 52.7 32.0 15.5 176.
## 6 A1 H10 III 2.72 1.51 16.7 52.7 30.4 17.5 207.
## 7 A1 H11 I 2.75 1.51 17.4 51.7 30.6 18.0 217.
## 8 A1 H11 II 2.72 1.56 16.7 47.2 28.7 17.2 181.
## 9 A1 H11 III 2.77 1.67 15.8 47.9 27.6 16.4 166.
## 10 A1 H12 I 2.73 1.54 14.9 47.5 28.2 15.5 161.
## # ... with 146 more rows, and 3 more variables: NFIL <dbl>, MMG <dbl>,
## # NGE <dbl>
Anova individual - anova_ind()
ind_an <- anova_ind(df,
env = ENV,
gen = GEN,
rep = BLOCO,
resp = everything(),
verbose = FALSE)
print(ind_an)
## Variable ALT_PLANT
## ---------------------------------------------------------------------------
## Within-environment ANOVA results
## ---------------------------------------------------------------------------
## # A tibble: 4 x 15
## ENV MEAN DFG MSG FCG PFG DFB MSB FCB PFB DFE
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <int>
## 1 A1 2.79 12 0.0185 1.27 2.98e- 1 2 0.00437 0.300 0.743 24
## 2 A2 2.46 12 0.477 37.4 1.43e-12 2 0.00747 0.585 0.565 24
## 3 A3 2.17 12 0.0840 2.56 2.39e- 2 2 0.0507 1.55 0.233 24
## 4 A4 2.52 12 0.0254 0.858 5.96e- 1 2 0.0179 0.603 0.555 24
## # ... with 4 more variables: MSE <dbl>, CV <dbl>, h2 <dbl>, AS <dbl>
## ---------------------------------------------------------------------------
##
##
##
## Variable ALT_ESP
## ---------------------------------------------------------------------------
## Within-environment ANOVA results
## ---------------------------------------------------------------------------
## # A tibble: 4 x 15
## ENV MEAN DFG MSG FCG PFG DFB MSB FCB PFB DFE
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <int>
## 1 A1 1.58 12 0.0256 2.03 6.74e- 2 2 0.00728 0.578 0.569 24
## 2 A2 1.31 12 0.363 45.6 1.53e-13 2 0.0180 2.26 0.126 24
## 3 A3 1.08 12 0.0488 1.44 2.14e- 1 2 0.00892 0.264 0.770 24
## 4 A4 1.41 12 0.00919 0.321 9.78e- 1 2 0.0229 0.802 0.460 24
## # ... with 4 more variables: MSE <dbl>, CV <dbl>, h2 <dbl>, AS <dbl>
## ---------------------------------------------------------------------------
##
##
##
## Variable COMPES
## ---------------------------------------------------------------------------
## Within-environment ANOVA results
## ---------------------------------------------------------------------------
## # A tibble: 4 x 15
## ENV MEAN DFG MSG FCG PFG DFB MSB FCB PFB DFE MSE
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl>
## 1 A1 15.6 12 1.03 0.623 0.802 2 0.363 0.220 0.804 24 1.65
## 2 A2 15.2 12 4.35 5.92 0.000110 2 0.455 0.619 0.547 24 0.734
## 3 A3 14.7 12 1.13 1.14 0.373 2 0.637 0.648 0.532 24 0.984
## 4 A4 15.1 12 3.39 3.50 0.00431 2 0.409 0.422 0.660 24 0.969
## # ... with 3 more variables: CV <dbl>, h2 <dbl>, AS <dbl>
## ---------------------------------------------------------------------------
##
##
##
## Variable DIAMES
## ---------------------------------------------------------------------------
## Within-environment ANOVA results
## ---------------------------------------------------------------------------
## # A tibble: 4 x 15
## ENV MEAN DFG MSG FCG PFG DFB MSB FCB PFB DFE MSE
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl>
## 1 A1 51.6 12 7.10 3.88 0.00228 2 0.141 0.0772 0.926 24 1.83
## 2 A2 48.7 12 19.7 11.6 0.000000317 2 2.04 1.20 0.319 24 1.70
## 3 A3 47.9 12 18.5 7.63 0.0000138 2 5.19 2.13 0.140 24 2.43
## 4 A4 49.9 12 5.61 1.27 0.297 2 2.03 0.460 0.637 24 4.42
## # ... with 3 more variables: CV <dbl>, h2 <dbl>, AS <dbl>
## ---------------------------------------------------------------------------
##
##
##
## Variable COMP_SAB
## ---------------------------------------------------------------------------
## Within-environment ANOVA results
## ---------------------------------------------------------------------------
## # A tibble: 4 x 15
## ENV MEAN DFG MSG FCG PFG DFB MSB FCB PFB DFE
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <int>
## 1 A1 29.7 12 11.3 5.51 1.92e-4 2 2.72 1.32 0.285 24
## 2 A2 28.5 12 18.1 17.4 5.47e-9 2 0.00937 0.00898 0.991 24
## 3 A3 28.4 12 14.2 10.1 1.18e-6 2 8.06 5.70 0.00945 24
## 4 A4 29.4 12 5.75 2.74 1.73e-2 2 0.861 0.410 0.668 24
## # ... with 4 more variables: MSE <dbl>, CV <dbl>, h2 <dbl>, AS <dbl>
## ---------------------------------------------------------------------------
##
##
##
## Variable DIAM_SAB
## ---------------------------------------------------------------------------
## Within-environment ANOVA results
## ---------------------------------------------------------------------------
## # A tibble: 4 x 15
## ENV MEAN DFG MSG FCG PFG DFB MSB FCB PFB DFE MSE
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl>
## 1 A1 16.4 12 1.38 1.17 0.355 2 0.0558 0.0476 0.954 24 1.17
## 2 A2 15.9 12 4.20 5.68 0.000153 2 0.228 0.308 0.738 24 0.739
## 3 A3 15.8 12 1.35 2.13 0.0550 2 1.27 2.01 0.156 24 0.634
## 4 A4 15.8 12 2.49 2.33 0.0372 2 0.318 0.299 0.745 24 1.06
## # ... with 3 more variables: CV <dbl>, h2 <dbl>, AS <dbl>
## ---------------------------------------------------------------------------
##
##
##
## Variable MGE
## ---------------------------------------------------------------------------
## Within-environment ANOVA results
## ---------------------------------------------------------------------------
## # A tibble: 4 x 15
## ENV MEAN DFG MSG FCG PFG DFB MSB FCB PFB DFE MSE
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl>
## 1 A1 199. 12 597. 2.01 7.01e-2 2 49.8 0.168 0.846 24 297.
## 2 A2 168. 12 3770. 14.9 2.58e-8 2 46.3 0.183 0.834 24 253.
## 3 A3 147. 12 823. 2.94 1.19e-2 2 620. 2.21 0.131 24 280.
## 4 A4 177. 12 836. 1.17 3.59e-1 2 57.6 0.0803 0.923 24 717.
## # ... with 3 more variables: CV <dbl>, h2 <dbl>, AS <dbl>
## ---------------------------------------------------------------------------
##
##
##
## Variable NFIL
## ---------------------------------------------------------------------------
## Within-environment ANOVA results
## ---------------------------------------------------------------------------
## # A tibble: 4 x 15
## ENV MEAN DFG MSG FCG PFG DFB MSB FCB PFB DFE MSE
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl>
## 1 A1 16.9 12 6.34 2.17 0.0515 2 0.529 0.181 0.836 24 2.92
## 2 A2 15.8 12 4.35 4.63 0.000698 2 2.10 2.23 0.130 24 0.941
## 3 A3 15.8 12 4.81 3.79 0.00267 2 0.640 0.503 0.611 24 1.27
## 4 A4 16.0 12 2.57 1.78 0.111 2 1.20 0.831 0.448 24 1.44
## # ... with 3 more variables: CV <dbl>, h2 <dbl>, AS <dbl>
## ---------------------------------------------------------------------------
##
##
##
## Variable MMG
## ---------------------------------------------------------------------------
## Within-environment ANOVA results
## ---------------------------------------------------------------------------
## # A tibble: 4 x 15
## ENV MEAN DFG MSG FCG PFG DFB MSB FCB PFB DFE MSE
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl>
## 1 A1 360. 12 2553. 2.52 2.62e-2 2 59.5 0.0587 0.943 24 1015.
## 2 A2 334. 12 9498. 14.1 4.55e-8 2 581. 0.863 0.435 24 674.
## 3 A3 318. 12 3541. 3.48 4.53e-3 2 1172. 1.15 0.333 24 1018.
## 4 A4 343. 12 1842. 1.90 8.67e-2 2 2622. 2.71 0.0868 24 967.
## # ... with 3 more variables: CV <dbl>, h2 <dbl>, AS <dbl>
## ---------------------------------------------------------------------------
##
##
##
## Variable NGE
## ---------------------------------------------------------------------------
## Within-environment ANOVA results
## ---------------------------------------------------------------------------
## # A tibble: 4 x 15
## ENV MEAN DFG MSG FCG PFG DFB MSB FCB PFB DFE MSE
## <chr> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl>
## 1 A1 558. 12 5238. 1.43 0.220 2 897. 0.245 0.785 24 3664.
## 2 A2 505. 12 7062. 3.51 0.00430 2 2119. 1.05 0.365 24 2014.
## 3 A3 468. 12 8346. 3.48 0.00451 2 1416. 0.590 0.562 24 2399.
## 4 A4 516. 12 7430. 1.62 0.153 2 3661. 0.797 0.462 24 4595.
## # ... with 3 more variables: CV <dbl>, h2 <dbl>, AS <dbl>
## ---------------------------------------------------------------------------
# Obter dados de todas as variáveis (Coeficiente de variação)
gmd(ind_an, "CV") %>% print_tbl()
## Class of the model: anova_ind
## Variable extracted: CV
| ENV | ALT_PLANT | ALT_ESP | COMPES | DIAMES | COMP_SAB | DIAM_SAB | MGE | NFIL | MMG | NGE |
|---|---|---|---|---|---|---|---|---|---|---|
| A1 | 4.321 | 7.119 | 8.224 | 2.620 | 4.816 | 6.605 | 8.639 | 10.124 | 8.840 | 10.850 |
| A2 | 4.589 | 6.796 | 5.624 | 2.678 | 3.589 | 5.412 | 9.436 | 6.141 | 7.775 | 8.893 |
| A3 | 8.353 | 17.052 | 6.762 | 3.256 | 4.181 | 5.047 | 11.406 | 7.143 | 10.044 | 10.468 |
| A4 | 6.836 | 12.038 | 6.505 | 4.209 | 4.929 | 6.520 | 15.126 | 7.497 | 9.072 | 13.133 |
# F-máximo
gmd(ind_an, what = "FMAX") %>% print_tbl()
## Class of the model: anova_ind
## Variable extracted: FMAX
| TRAIT | F_RATIO |
|---|---|
| ALT_PLANT | 2.565 |
| ALT_ESP | 4.243 |
| COMPES | 2.249 |
| DIAMES | 2.593 |
| COMP_SAB | 2.014 |
| DIAM_SAB | 1.851 |
| MGE | 2.840 |
| NFIL | 3.109 |
| MMG | 1.512 |
| NGE | 2.282 |
Anova individual - gafem()
ind_an2 <- gafem(df,
gen = GEN,
rep = BLOCO,
resp = everything(),
by = ENV,
verbose = FALSE)
# Obter dados de todas as variáveis
# P-value
pval <- gmd(ind_an2, what = "Pr(>F)", verbose = FALSE)
print_tbl(pval)
| ENV | Source | ALT_PLANT | ALT_ESP | COMPES | DIAMES | COMP_SAB | DIAM_SAB | MGE | NFIL | MMG | NGE |
|---|---|---|---|---|---|---|---|---|---|---|---|
| A1 | REP | 0.743 | 0.569 | 0.804 | 0.926 | 0.285 | 0.954 | 0.846 | 0.836 | 0.943 | 0.785 |
| A1 | GEN | 0.298 | 0.067 | 0.802 | 0.002 | 0.000 | 0.355 | 0.070 | 0.052 | 0.026 | 0.220 |
| A2 | REP | 0.565 | 0.126 | 0.547 | 0.319 | 0.991 | 0.738 | 0.834 | 0.130 | 0.435 | 0.365 |
| A2 | GEN | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.000 | 0.004 |
| A3 | REP | 0.233 | 0.770 | 0.532 | 0.140 | 0.009 | 0.156 | 0.131 | 0.611 | 0.333 | 0.562 |
| A3 | GEN | 0.024 | 0.214 | 0.373 | 0.000 | 0.000 | 0.055 | 0.012 | 0.003 | 0.005 | 0.005 |
| A4 | REP | 0.555 | 0.460 | 0.660 | 0.637 | 0.668 | 0.745 | 0.923 | 0.448 | 0.087 | 0.462 |
| A4 | GEN | 0.596 | 0.978 | 0.004 | 0.297 | 0.017 | 0.037 | 0.359 | 0.111 | 0.087 | 0.153 |
# Comparação de médias (MGE dentro do ambiente 2)
model_mge_a2 <- ind_an2[[2]][[2]][["MGE"]][["model"]]
pairwise_means <- tukey_hsd(model_mge_a2, "GEN")
print_tbl(pairwise_means)
| term | group1 | group2 | estimate | conf.low | conf.high | p.adj | sign |
|---|---|---|---|---|---|---|---|
| GEN | H1 | H10 | -28.304 | -75.822 | 19.214 | 0.612 | ns |
| GEN | H1 | H11 | -24.589 | -72.107 | 22.929 | 0.783 | ns |
| GEN | H1 | H12 | -56.922 | -104.440 | -9.404 | 0.010 | ** |
| GEN | H1 | H13 | -19.127 | -66.645 | 28.391 | 0.949 | ns |
| GEN | H1 | H2 | 30.659 | -16.859 | 78.177 | 0.498 | ns |
| GEN | H1 | H3 | 2.746 | -44.772 | 50.263 | 1.000 | ns |
| GEN | H1 | H4 | 9.267 | -38.251 | 56.785 | 1.000 | ns |
| GEN | H1 | H5 | -1.955 | -49.473 | 45.563 | 1.000 | ns |
| GEN | H1 | H6 | 26.832 | -20.686 | 74.350 | 0.683 | ns |
| GEN | H1 | H7 | -44.395 | -91.913 | 3.123 | 0.083 | ns |
| GEN | H1 | H8 | -75.235 | -122.753 | -27.717 | 0.000 | *** |
| GEN | H1 | H9 | -75.867 | -123.385 | -28.349 | 0.000 | *** |
| GEN | H10 | H11 | 3.715 | -43.803 | 51.233 | 1.000 | ns |
| GEN | H10 | H12 | -28.618 | -76.136 | 18.900 | 0.597 | ns |
| GEN | H10 | H13 | 9.177 | -38.341 | 56.695 | 1.000 | ns |
| GEN | H10 | H2 | 58.963 | 11.445 | 106.481 | 0.007 | ** |
| GEN | H10 | H3 | 31.049 | -16.469 | 78.567 | 0.480 | ns |
| GEN | H10 | H4 | 37.571 | -9.947 | 85.089 | 0.225 | ns |
| GEN | H10 | H5 | 26.349 | -21.169 | 73.867 | 0.705 | ns |
| GEN | H10 | H6 | 55.136 | 7.618 | 102.654 | 0.013 | * |
| GEN | H10 | H7 | -16.091 | -63.609 | 31.426 | 0.986 | ns |
| GEN | H10 | H8 | -46.931 | -94.449 | 0.587 | 0.055 | ns |
| GEN | H10 | H9 | -47.563 | -95.081 | -0.045 | 0.050 | * |
| GEN | H11 | H12 | -32.333 | -79.851 | 15.185 | 0.421 | ns |
| GEN | H11 | H13 | 5.462 | -42.056 | 52.980 | 1.000 | ns |
| GEN | H11 | H2 | 55.248 | 7.730 | 102.766 | 0.013 | * |
| GEN | H11 | H3 | 27.334 | -20.184 | 74.852 | 0.659 | ns |
| GEN | H11 | H4 | 33.856 | -13.662 | 81.374 | 0.356 | ns |
| GEN | H11 | H5 | 22.634 | -24.884 | 70.152 | 0.857 | ns |
| GEN | H11 | H6 | 51.420 | 3.903 | 98.938 | 0.026 | * |
| GEN | H11 | H7 | -19.807 | -67.325 | 27.711 | 0.935 | ns |
| GEN | H11 | H8 | -50.646 | -98.164 | -3.129 | 0.029 | * |
| GEN | H11 | H9 | -51.278 | -98.796 | -3.760 | 0.026 | * |
| GEN | H12 | H13 | 37.795 | -9.723 | 85.313 | 0.218 | ns |
| GEN | H12 | H2 | 87.581 | 40.063 | 135.099 | 0.000 | **** |
| GEN | H12 | H3 | 59.667 | 12.149 | 107.185 | 0.006 | ** |
| GEN | H12 | H4 | 66.189 | 18.671 | 113.707 | 0.002 | ** |
| GEN | H12 | H5 | 54.967 | 7.449 | 102.485 | 0.014 | * |
| GEN | H12 | H6 | 83.754 | 36.236 | 131.272 | 0.000 | **** |
| GEN | H12 | H7 | 12.526 | -34.992 | 60.044 | 0.998 | ns |
| GEN | H12 | H8 | -18.313 | -65.831 | 29.205 | 0.962 | ns |
| GEN | H12 | H9 | -18.945 | -66.463 | 28.573 | 0.952 | ns |
| GEN | H13 | H2 | 49.786 | 2.268 | 97.304 | 0.034 | * |
| GEN | H13 | H3 | 21.872 | -25.646 | 69.390 | 0.882 | ns |
| GEN | H13 | H4 | 28.394 | -19.124 | 75.912 | 0.608 | ns |
| GEN | H13 | H5 | 17.172 | -30.346 | 64.690 | 0.976 | ns |
| GEN | H13 | H6 | 45.959 | -1.559 | 93.477 | 0.065 | ns |
| GEN | H13 | H7 | -25.269 | -72.786 | 22.249 | 0.754 | ns |
| GEN | H13 | H8 | -56.108 | -103.626 | -8.590 | 0.011 | * |
| GEN | H13 | H9 | -56.740 | -104.258 | -9.222 | 0.010 | * |
| GEN | H2 | H3 | -27.914 | -75.432 | 19.604 | 0.631 | ns |
| GEN | H2 | H4 | -21.392 | -68.910 | 26.126 | 0.896 | ns |
| GEN | H2 | H5 | -32.614 | -80.132 | 14.904 | 0.409 | ns |
| GEN | H2 | H6 | -3.827 | -51.345 | 43.691 | 1.000 | ns |
| GEN | H2 | H7 | -75.055 | -122.573 | -27.537 | 0.000 | *** |
| GEN | H2 | H8 | -105.894 | -153.412 | -58.376 | 0.000 | **** |
| GEN | H2 | H9 | -106.526 | -154.044 | -59.008 | 0.000 | **** |
| GEN | H3 | H4 | 6.522 | -40.996 | 54.040 | 1.000 | ns |
| GEN | H3 | H5 | -4.700 | -52.218 | 42.818 | 1.000 | ns |
| GEN | H3 | H6 | 24.086 | -23.432 | 71.604 | 0.804 | ns |
| GEN | H3 | H7 | -47.141 | -94.659 | 0.377 | 0.053 | ns |
| GEN | H3 | H8 | -77.981 | -125.499 | -30.463 | 0.000 | *** |
| GEN | H3 | H9 | -78.612 | -126.130 | -31.094 | 0.000 | *** |
| GEN | H4 | H5 | -11.222 | -58.740 | 36.296 | 0.999 | ns |
| GEN | H4 | H6 | 17.565 | -29.953 | 65.082 | 0.972 | ns |
| GEN | H4 | H7 | -53.663 | -101.181 | -6.145 | 0.017 | * |
| GEN | H4 | H8 | -84.502 | -132.020 | -36.985 | 0.000 | **** |
| GEN | H4 | H9 | -85.134 | -132.652 | -37.616 | 0.000 | **** |
| GEN | H5 | H6 | 28.786 | -18.731 | 76.304 | 0.589 | ns |
| GEN | H5 | H7 | -42.441 | -89.959 | 5.077 | 0.112 | ns |
| GEN | H5 | H8 | -73.281 | -120.798 | -25.763 | 0.000 | *** |
| GEN | H5 | H9 | -73.912 | -121.430 | -26.394 | 0.000 | *** |
| GEN | H6 | H7 | -71.227 | -118.745 | -23.709 | 0.001 | *** |
| GEN | H6 | H8 | -102.067 | -149.585 | -54.549 | 0.000 | **** |
| GEN | H6 | H9 | -102.698 | -150.216 | -55.180 | 0.000 | **** |
| GEN | H7 | H8 | -30.840 | -78.358 | 16.678 | 0.490 | ns |
| GEN | H7 | H9 | -31.471 | -78.989 | 16.047 | 0.460 | ns |
| GEN | H8 | H9 | -0.631 | -48.149 | 46.887 | 1.000 | ns |
# comparações de médias com o pacote emmeans
(means <- emmeans(model_mge_a2, "GEN"))
## GEN emmean SE df lower.CL upper.CL
## H1 188 9.18 24 169.3 207
## H10 160 9.18 24 141.0 179
## H11 164 9.18 24 144.7 183
## H12 131 9.18 24 112.3 150
## H13 169 9.18 24 150.1 188
## H2 219 9.18 24 199.9 238
## H3 191 9.18 24 172.0 210
## H4 197 9.18 24 178.5 216
## H5 186 9.18 24 167.3 205
## H6 215 9.18 24 196.1 234
## H7 144 9.18 24 124.9 163
## H8 113 9.18 24 94.0 132
## H9 112 9.18 24 93.4 131
##
## Results are averaged over the levels of: REP
## Confidence level used: 0.95
plot(means,
comparisons = TRUE,
CIs = FALSE,
xlab = "Massa de grãos por espiga",
ylab = "Genótipos")
