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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)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
#> 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
#> 1: yield C1 G60 50 TRUE
#>
#> ---------------------------------------------------------------------
#> First Predicted Values and Standard Errors (BLUEs/BLUPs):
#> ---------------------------------------------------------------------
#> trait genotype trial BLUEs seBLUEs BLUPs seBLUPs wt
#> 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 0.02698141The results of the previous function are used in
met_analysis() to fit multi-environmental trial models.
if (requireNamespace("asreml", quietly = TRUE)) {
met_results <- met_analysis(obj)
print(met_results)
}
#> Online License checked out Fri Jan 19 19:02:00 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 102.39802These 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.