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Print the anova_joint object in two ways. By default, the results are shown in the R console. The results can also be exported to the directory into a *.txt file.

Usage

# S3 method for anova_joint
print(x, export = FALSE, file.name = NULL, digits = 3, ...)

Arguments

x

An object of class anova_joint.

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 <- data_ge %>% anova_joint(ENV, GEN, REP, c(GY, HM))
#> variable GY 
#> ---------------------------------------------------------------------------
#> Joint ANOVA table
#> ---------------------------------------------------------------------------
#>     Source     Df Sum Sq Mean Sq F value    Pr(>F)
#>        ENV  13.00 279.57 21.5057  222.41 7.25e-130
#>   REP(ENV)  28.00   9.66  0.3451    3.57  3.59e-08
#>        GEN   9.00  13.00  1.4439   14.93  2.19e-19
#>    GEN:ENV 117.00  31.22  0.2668    2.76  1.01e-11
#>  Residuals 252.00  24.37  0.0967      NA        NA
#>      CV(%)  11.63     NA      NA      NA        NA
#>  MSR+/MSR-   6.71     NA      NA      NA        NA
#>     OVmean   2.67     NA      NA      NA        NA
#> ---------------------------------------------------------------------------
#> 
#> variable HM 
#> ---------------------------------------------------------------------------
#> Joint ANOVA table
#> ---------------------------------------------------------------------------
#>     Source     Df Sum Sq Mean Sq F value    Pr(>F)
#>        ENV  13.00   5710  439.26  154.67 5.86e-112
#>   REP(ENV)  28.00    215    7.68    2.70  2.20e-05
#>        GEN   9.00    270   29.98   10.56  7.41e-14
#>    GEN:ENV 117.00   1101    9.41    3.31  1.06e-15
#>  Residuals 252.00    716    2.84      NA        NA
#>      CV(%)   3.50     NA      NA      NA        NA
#>  MSR+/MSR-   5.24     NA      NA      NA        NA
#>     OVmean  48.09     NA      NA      NA        NA
#> ---------------------------------------------------------------------------
#> 
#> All variables with significant (p < 0.05) genotype-vs-environment interaction
#> Done!
print(model)
#> Variable GY 
#> ---------------------------------------------------------------------------
#> $anova
#>      Source         Df     Sum Sq     Mean Sq    F value        Pr(>F)
#> 1       ENV  13.000000 279.573552 21.50565785 222.411390 7.253126e-130
#> 2  REP(ENV)  28.000000   9.661516  0.34505416   3.568548  3.593191e-08
#> 3       GEN   9.000000  12.995044  1.44389374  14.932741  2.190118e-19
#> 4   GEN:ENV 117.000000  31.219565  0.26683389   2.759595  1.005191e-11
#> 5 Residuals 252.000000  24.366674  0.09669315         NA            NA
#> 6     CV(%)  11.627790         NA          NA         NA            NA
#> 7 MSR+/MSR-   6.708789         NA          NA         NA            NA
#> 8    OVmean   2.674242         NA          NA         NA            NA
#> 
#> $model
#> Call:
#>    aov(formula = mean ~ GEN + ENV + GEN:ENV + ENV/REP, data = data)
#> 
#> Terms:
#>                       GEN       ENV   GEN:ENV   ENV:REP Residuals
#> Sum of Squares   12.99504 279.57355  31.21956   9.66152  24.36667
#> Deg. of Freedom         9        13       117        28       252
#> 
#> Residual standard error: 0.3109552
#> Estimated effects may be unbalanced
#> 
#> $augment
#> # A tibble: 420 × 11
#>    ENV   GEN   REP    mean   hat sigma fitted    resid  stdres se.fit factors
#>    <fct> <fct> <fct> <dbl> <dbl> <dbl>  <dbl>    <dbl>   <dbl>  <dbl> <chr>  
#>  1 E1    G1    1      2.17 0.400 0.311   2.42 -0.255   -1.06    0.197 G1_1   
#>  2 E1    G1    2      2.50 0.400 0.311   2.40  0.101    0.420   0.197 G1_2   
#>  3 E1    G1    3      2.43 0.400 0.311   2.27  0.154    0.640   0.197 G1_3   
#>  4 E1    G2    1      3.21 0.400 0.311   2.96  0.249    1.04    0.197 G2_1   
#>  5 E1    G2    2      2.93 0.400 0.312   2.94 -0.00492 -0.0204  0.197 G2_2   
#>  6 E1    G2    3      2.56 0.400 0.311   2.81 -0.244   -1.01    0.197 G2_3   
#>  7 E1    G3    1      2.77 0.4   0.311   2.95 -0.176   -0.729   0.197 G3_1   
#>  8 E1    G3    2      3.62 0.400 0.306   2.92  0.696    2.89    0.197 G3_2   
#>  9 E1    G3    3      2.28 0.400 0.309   2.80 -0.521   -2.16    0.197 G3_3   
#> 10 E1    G4    1      2.36 0.400 0.311   2.65 -0.286   -1.19    0.197 G4_1   
#> # … with 410 more rows
#> 
#> $details
#> # A tibble: 10 × 2
#>    Parameters mean               
#>    <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) "          
#> 
#> ---------------------------------------------------------------------------
#> 
#> 
#> 
#> Variable HM 
#> ---------------------------------------------------------------------------
#> $anova
#>      Source         Df    Sum Sq    Mean Sq    F value        Pr(>F)
#> 1       ENV  13.000000 5710.3167 439.255133 154.666159 5.864441e-112
#> 2  REP(ENV)  28.000000  214.9307   7.676095   2.702830  2.196589e-05
#> 3       GEN   9.000000  269.8112  29.979019  10.555915  7.414690e-14
#> 4   GEN:ENV 117.000000 1100.7341   9.407984   3.312646  1.063605e-15
#> 5 Residuals 252.000000  715.6853   2.840021         NA            NA
#> 6     CV(%)   3.504463        NA         NA         NA            NA
#> 7 MSR+/MSR-   5.235567        NA         NA         NA            NA
#> 8    OVmean  48.088286        NA         NA         NA            NA
#> 
#> $model
#> Call:
#>    aov(formula = mean ~ GEN + ENV + GEN:ENV + ENV/REP, data = data)
#> 
#> Terms:
#>                      GEN      ENV  GEN:ENV  ENV:REP Residuals
#> Sum of Squares   269.811 5710.317 1100.734  214.931   715.685
#> Deg. of Freedom        9       13      117       28       252
#> 
#> Residual standard error: 1.685236
#> Estimated effects may be unbalanced
#> 
#> $augment
#> # A tibble: 420 × 11
#>    ENV   GEN   REP    mean   hat sigma fitted  resid stdres se.fit factors
#>    <fct> <fct> <fct> <dbl> <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <chr>  
#>  1 E1    G1    1      44.9 0.400  1.68   46.5 -1.62  -1.24    1.07 G1_1   
#>  2 E1    G1    2      46.9 0.400  1.69   46.0  0.942  0.721   1.07 G1_2   
#>  3 E1    G1    3      47.8 0.400  1.69   47.1  0.678  0.519   1.07 G1_3   
#>  4 E1    G2    1      45.2 0.400  1.69   45.4 -0.153 -0.117   1.07 G2_1   
#>  5 E1    G2    2      45.3 0.400  1.69   44.8  0.538  0.412   1.07 G2_2   
#>  6 E1    G2    3      45.5 0.400  1.69   45.9 -0.386 -0.295   1.07 G2_3   
#>  7 E1    G3    1      46.7 0.4    1.69   45.9  0.791  0.606   1.07 G3_1   
#>  8 E1    G3    2      43.2 0.400  1.68   45.3 -2.11  -1.62    1.07 G3_2   
#>  9 E1    G3    3      47.8 0.400  1.69   46.4  1.32   1.01    1.07 G3_3   
#> 10 E1    G4    1      47.9 0.400  1.69   48.3 -0.386 -0.296   1.07 G4_1   
#> # … with 410 more rows
#> 
#> $details
#> # A tibble: 10 × 2
#>    Parameters mean            
#>    <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) "    
#> 
#> ---------------------------------------------------------------------------
#> 
#> 
#> 
# }