Skip to contents

Prints information about check_design_met() function.

Usage

# S3 method for checkAgri
print(x, ...)

Arguments

x

An object fitted with the function check_design_met().

...

Options used by the tibble package to format the output. See `tibble::print()` for more details.

Value

an object inheriting from class checkAgri.

Author

Johan Aparicio [aut]

Examples

library(agridat)
library(agriutilities)
data(besag.met)
dat <- besag.met
results <- check_design_met(
  data = dat,
  genotype = "gen",
  trial = "county",
  traits = c("yield"),
  rep = "rep",
  block = "block",
  col = "col",
  row = "row"
)
print(results)
#> ---------------------------------------------------------------------
#> Summary Traits by Trial:
#> ---------------------------------------------------------------------
#> # A tibble: 6 × 11
#>   county traits   Min  Mean Median   Max    SD    CV     n n_miss miss_perc
#>   <fct>  <chr>  <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <int>  <int>     <dbl>
#> 1 C1     yield   87.9 149.   151.   200.  17.7 0.119   198      6    0.0303
#> 2 C2     yield   24.4  56.1   52.1  125.  18.4 0.328   198      6    0.0303
#> 3 C3     yield   28.2  87.9   89.2  137.  19.7 0.225   198      6    0.0303
#> 4 C4     yield  103.  145.   143.   190.  17.1 0.118   198      6    0.0303
#> 5 C5     yield   66.9 115.   116.   152.  16.4 0.142   198      6    0.0303
#> 6 C6     yield   29.2  87.6   87.8  148.  26.6 0.304   198      6    0.0303
#> 
#> ---------------------------------------------------------------------
#> Experimental Design Detected:
#> ---------------------------------------------------------------------
#>   county exp_design
#> 1     C1    row_col
#> 2     C2    row_col
#> 3     C3    row_col
#> 4     C4    row_col
#> 5     C5    row_col
#> 6     C6    row_col
#> 
#> ---------------------------------------------------------------------
#> Summary Experimental Design:
#> ---------------------------------------------------------------------
#> # A tibble: 6 × 9
#>   county     n n_gen n_rep n_block n_col n_row num_of_reps num_of_gen
#>   <fct>  <int> <int> <int>   <int> <int> <int> <fct>       <fct>     
#> 1 C1       198    64     3       8    11    18 3_9         63_1      
#> 2 C2       198    64     3       8    11    18 3_9         63_1      
#> 3 C3       198    64     3       8    11    18 3_9         63_1      
#> 4 C4       198    64     3       8    11    18 3_9         63_1      
#> 5 C5       198    64     3       8    11    18 3_9         63_1      
#> 6 C6       198    64     3       8    11    18 3_9         63_1      
#> 
#> ---------------------------------------------------------------------
#> Connectivity Matrix:
#> ---------------------------------------------------------------------
#>    C1 C2 C3 C4 C5 C6
#> C1 64 64 64 64 64 64
#> C2 64 64 64 64 64 64
#> C3 64 64 64 64 64 64
#> C4 64 64 64 64 64 64
#> C5 64 64 64 64 64 64
#> C6 64 64 64 64 64 64
#> 
#> ---------------------------------------------------------------------
#> Filters Applied:
#> ---------------------------------------------------------------------
#> List of 1
#>  $ yield:List of 4
#>   ..$ missing_50%     : chr(0) 
#>   ..$ no_variation    : chr(0) 
#>   ..$ row_col_dup     : chr(0) 
#>   ..$ trials_to_remove: chr(0) 
#>