The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
The function check_design_met helps us to check the
quality of the data and also to identify the experimental design of the
trials. This works as a quality check or quality control before we fit
any model.
library(agriutilities)
library(agridat)
#> Warning: package 'agridat' was built under R version 4.4.2
data(besag.met)
dat <- besag.met
results <- check_design_met(
data = dat,
genotype = "gen",
trial = "county",
traits = "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)The results of the previous function are used in
single_trial_analysis() to fit single trial models.
obj <- single_trial_analysis(results, progress = FALSE)
print(obj)
#> ---------------------------------------------------------------------
#> Summary Fitted Models:
#> ---------------------------------------------------------------------
#> trait trial heritability CV VarGen VarErr design
#> <char> <char> <num> <num> <num> <num> <char>
#> 1: yield C1 0.70 6.370054 85.28086 92.70982 row_col
#> 2: yield C2 0.39 16.987235 26.87283 105.50494 row_col
#> 3: yield C3 0.64 12.366843 82.84379 118.86865 row_col
#> 4: yield C4 0.41 8.179794 35.75059 136.21686 row_col
#> 5: yield C5 0.80 7.042116 104.44077 66.96454 row_col
#> 6: yield C6 0.49 16.583972 72.16813 206.54020 row_col
#>
#> ---------------------------------------------------------------------
#> Outliers Removed:
#> ---------------------------------------------------------------------
#> Null data.table (0 rows and 0 cols)
#>
#> ---------------------------------------------------------------------
#> First Predicted Values and Standard Errors (BLUEs/BLUPs):
#> ---------------------------------------------------------------------
#> trait genotype trial BLUEs seBLUEs BLUPs seBLUPs wt
#> <char> <fctr> <fctr> <num> <num> <num> <num> <num>
#> 1: yield G01 C1 142.9316 6.380244 144.5151 5.421481 0.02456549
#> 2: yield G02 C1 156.7765 6.277083 155.0523 5.367425 0.02537957
#> 3: yield G03 C1 126.5654 6.402526 133.1766 5.444349 0.02439480
#> 4: yield G04 C1 155.7790 6.391590 154.2435 5.440070 0.02447836
#> 5: yield G05 C1 163.9856 6.443261 160.7620 5.444314 0.02408732
#> 6: yield G06 C1 129.5092 6.400364 134.7404 5.421543 0.02441129The results of the previous function are used in
met_analysis() to fit multi-environmental trial models.
#> Online License checked out Fri Jan 17 13:48:39 2025
#> Fitting MET model for yield.
#> ---------------------------------------------------------------------
#> Trial Effects (BLUEs):
#> ---------------------------------------------------------------------
#> trait trial predicted.value std.error status
#> 1 yield C1 149.58855 1.374709 Estimable
#> 2 yield C2 67.20519 1.135676 Estimable
#> 3 yield C3 90.80064 1.444905 Estimable
#> 4 yield C4 148.12440 1.203151 Estimable
#> 5 yield C5 122.40153 1.438032 Estimable
#> 6 yield C6 88.35130 1.530783 Estimable
#>
#> ---------------------------------------------------------------------
#> Heritability:
#> ---------------------------------------------------------------------
#> trait h2
#> 1 yield 0.824676
#>
#> ---------------------------------------------------------------------
#> First Overall Predicted Values and Standard Errors (BLUPs):
#> ---------------------------------------------------------------------
#> trait genotype predicted.value std.error status
#> 1 yield G01 110.8726 2.543835 Estimable
#> 2 yield G02 111.0740 2.555394 Estimable
#> 3 yield G03 102.8086 2.556853 Estimable
#> 4 yield G04 116.0421 2.552859 Estimable
#> 5 yield G05 121.0498 2.563815 Estimable
#> 6 yield G06 109.2002 2.576279 Estimable
#>
#> ---------------------------------------------------------------------
#> Variance-Covariance Matrix:
#> ---------------------------------------------------------------------
#>
#> Correlation Matrix ('us'): yield
#> C1 C2 C3 C4 C5 C6
#> C1 1.00 0.67 0.63 0.69 0.96 0.48
#> C2 0.67 1.00 0.57 0.70 0.54 0.76
#> C3 0.63 0.57 1.00 0.95 0.71 0.27
#> C4 0.69 0.70 0.95 1.00 0.74 0.46
#> C5 0.96 0.54 0.71 0.74 1.00 0.34
#> C6 0.48 0.76 0.27 0.46 0.34 1.00
#>
#> Covariance Matrix ('us'): yield
#> C1 C2 C3 C4 C5 C6
#> C1 79.82 30.44 50.21 33.48 86.30 35.17
#> C2 30.44 26.09 25.84 19.42 27.74 31.93
#> C3 50.21 25.84 79.24 45.89 63.94 20.14
#> C4 33.48 19.42 45.89 29.30 40.64 20.65
#> C5 86.30 27.74 63.94 40.64 101.79 27.92
#> C6 35.17 31.93 20.14 20.65 27.92 68.10
#>
#> ---------------------------------------------------------------------
#> First Stability Coefficients:
#> ---------------------------------------------------------------------
#> trait genotype superiority static wricke predicted.value
#> 1 yield G57 22.82799 31.96023 14.021231 92.59577
#> 2 yield G29 17.38531 33.69133 5.079065 99.63303
#> 3 yield G34 17.37005 32.77775 8.637229 99.95851
#> 4 yield G59 17.05744 33.96539 5.068193 100.07485
#> 5 yield G31 16.09429 32.03412 10.289722 102.02500
#> 6 yield G10 15.77663 31.68227 11.579487 102.72633These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.