Identify the Experimental Design
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)
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)Single Trial Analysis
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 0.73 6.022489 87.39848 82.86095 row_col
#> 2: yield C2 0.37 17.104998 25.80684 108.68546 row_col
#> 3: yield C3 0.64 12.357202 83.57907 118.55567 row_col
#> 4: yield C4 0.41 8.179408 35.75568 136.21218 row_col
#> 5: yield C5 0.80 7.037586 103.79822 66.97523 row_col
#> 6: yield C6 0.49 16.632367 71.92232 207.53073 row_col
#>
#> ---------------------------------------------------------------------
#> Outliers Removed:
#> ---------------------------------------------------------------------
#> trait trial genotype id outlier
#> <char> <fctr> <fctr> <int> <lgcl>
#> 1: yield C1 G60 50 TRUE
#>
#> ---------------------------------------------------------------------
#> 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 141.4161 6.078858 143.5308 5.249771 0.02706176
#> 2: yield G02 C1 157.8110 5.979708 155.8037 5.194547 0.02796663
#> 3: yield G03 C1 127.3836 6.091534 133.0256 5.269999 0.02694925
#> 4: yield G04 C1 154.8445 6.093866 153.8364 5.270427 0.02692863
#> 5: yield G05 C1 163.8950 6.132141 161.1831 5.271809 0.02659352
#> 6: yield G06 C1 128.5168 6.087902 133.6857 5.247130 0.02698141Multi-Environmental Trial Analysis
The results of the previous function are used in
met_analysis() to fit multi-environmental trial models.
met_results <- met_analysis(obj)
print(met_results)#> Online License checked out Mon Jun 17 09:29:11 2024
#> Fitting MET model for yield.
#> ---------------------------------------------------------------------
#> Trial Effects (BLUEs):
#> ---------------------------------------------------------------------
#> trait trial predicted.value std.error status
#> 1 yield C1 149.74946 1.358117 Estimable
#> 2 yield C2 65.99561 1.141995 Estimable
#> 3 yield C3 90.60825 1.449096 Estimable
#> 4 yield C4 148.12392 1.202934 Estimable
#> 5 yield C5 121.77612 1.429239 Estimable
#> 6 yield C6 88.31194 1.532688 Estimable
#>
#> ---------------------------------------------------------------------
#> Heritability:
#> ---------------------------------------------------------------------
#> trait h2
#> 1 yield 0.8239191
#>
#> ---------------------------------------------------------------------
#> First Overall Predicted Values and Standard Errors (BLUPs):
#> ---------------------------------------------------------------------
#> trait genotype predicted.value std.error status
#> 1 yield G01 110.4297 2.528111 Estimable
#> 2 yield G02 110.8617 2.537200 Estimable
#> 3 yield G03 102.6812 2.541066 Estimable
#> 4 yield G04 115.4946 2.533730 Estimable
#> 5 yield G05 120.6600 2.548344 Estimable
#> 6 yield G06 108.8297 2.555281 Estimable
#>
#> ---------------------------------------------------------------------
#> Variance-Covariance Matrix:
#> ---------------------------------------------------------------------
#>
#> Correlation Matrix ('us'): yield
#> C1 C2 C3 C4 C5 C6
#> C1 1.00 0.57 0.58 0.65 0.95 0.43
#> C2 0.57 1.00 0.55 0.70 0.52 0.76
#> C3 0.58 0.55 1.00 0.95 0.72 0.27
#> C4 0.65 0.70 0.95 1.00 0.75 0.47
#> C5 0.95 0.52 0.72 0.75 1.00 0.33
#> C6 0.43 0.76 0.27 0.47 0.33 1.00
#>
#> Covariance Matrix ('us'): yield
#> C1 C2 C3 C4 C5 C6
#> C1 80.57 27.36 46.81 31.67 85.61 31.17
#> C2 27.36 28.90 26.44 20.41 27.99 33.51
#> C3 46.81 26.44 79.66 45.84 64.54 19.81
#> C4 31.67 20.41 45.84 29.28 40.79 20.76
#> C5 85.61 27.99 64.54 40.79 100.60 27.13
#> C6 31.17 33.51 19.81 20.76 27.13 66.66
#>
#> ---------------------------------------------------------------------
#> First Stability Coefficients:
#> ---------------------------------------------------------------------
#> trait genotype superiority static wricke predicted.value
#> 1 yield G57 22.67503 32.45871 13.962970 92.45997
#> 2 yield G29 17.27533 34.41794 4.343501 99.38429
#> 3 yield G34 17.26249 33.29276 8.514332 99.74688
#> 4 yield G59 16.94882 34.39425 4.798863 99.87221
#> 5 yield G31 16.23001 31.89042 11.722935 101.66382
#> 6 yield G10 15.75253 32.02994 11.499867 102.39802