agriutilities 
agriutilities is an R package designed to make the analysis of field
trials easier and more accessible for everyone working in plant
breeding. It provides a simple and intuitive interface for conducting
single and multi-environmental trial analysis, with minimal
coding required. Whether you’re a beginner or an experienced user,
agriutilities will help you quickly and easily carry out complex
analyses with confidence. With built-in functions for fitting Linear
Mixed Models (LMM), agriutilities is the ideal choice for anyone who
wants to save time and focus on interpreting their results.
Installation
From CRAN
install.packages("agriutilities")From GitHub
You can install the development version of agriutilities from GitHub with:
remotes::install_github("AparicioJohan/agriutilities")Automatic Data Analysis Pipeline
This is a basic example which shows you how to use some of the functions of the package.
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"
)plot(results, type = "connectivity")
plot(results, type = "missing")
Inspecting the output.
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 (STA)
The results of the previous function are used in
single_trial_analysis() to fit single trial models. This function can
fit, Completely Randomized Designs (CRD), Randomized Complete Block
Designs (RCBD), Resolvable Incomplete Block Designs (res-IBD),
Non-Resolvable Row-Column Designs (Row-Col) and Resolvable
Row-Column Designs (res-Row-Col).
NOTE: It fits models based on the randomization detected.
obj <- single_trial_analysis(results, progress = FALSE)Inspecting the output.
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 0.02698141plot(obj, horizontal = TRUE, nudge_y_h2 = 0.12)
plot(obj, type = "correlation")
The returning object is a set of lists with trial summary, BLUEs, BLUPs, heritability, variance components, potential extreme observations, residuals, the models fitted and the data used.
Two-Stage Analysis (MET)
The results of the previous function are used in met_analysis() to fit
multi-environmental trial models.
met_results <- met_analysis(obj, vcov = "fa2", progress = FALSE)
Online License checked out Mon Jun 17 09:16:16 2024Inspecting the output.
print(met_results)
---------------------------------------------------------------------
Trial Effects (BLUEs):
---------------------------------------------------------------------
trait trial predicted.value std.error status
1 yield C1 149.75463 1.357203 Estimable
2 yield C2 65.99443 1.140633 Estimable
3 yield C3 90.60647 1.445419 Estimable
4 yield C4 148.12551 1.172065 Estimable
5 yield C5 121.77655 1.434386 Estimable
6 yield C6 88.31651 1.532150 Estimable
---------------------------------------------------------------------
Heritability:
---------------------------------------------------------------------
trait h2
1 yield 0.8253127
---------------------------------------------------------------------
First Overall Predicted Values and Standard Errors (BLUPs):
---------------------------------------------------------------------
trait genotype predicted.value std.error status
1 yield G01 110.4107 2.524415 Estimable
2 yield G02 111.0488 2.534145 Estimable
3 yield G03 102.6031 2.538533 Estimable
4 yield G04 115.3953 2.530336 Estimable
5 yield G05 120.6700 2.545688 Estimable
6 yield G06 108.6678 2.552319 Estimable
---------------------------------------------------------------------
Variance-Covariance Matrix:
---------------------------------------------------------------------
Correlation Matrix ('fa2'): yield
C1 C2 C3 C4 C5 C6
C1 1.00 0.54 0.67 0.81 0.93 0.35
C2 0.54 1.00 0.60 0.90 0.52 0.68
C3 0.67 0.60 1.00 0.73 0.70 0.40
C4 0.81 0.90 0.73 1.00 0.83 0.60
C5 0.93 0.52 0.70 0.83 1.00 0.34
C6 0.35 0.68 0.40 0.60 0.34 1.00
Covariance Matrix ('fa2'): yield
C1 C2 C3 C4 C5 C6
C1 80.39 25.73 53.51 35.80 84.13 25.75
C2 25.73 28.74 28.50 23.86 28.05 29.67
C3 53.51 28.50 78.96 32.26 62.98 28.93
C4 35.80 23.86 32.26 24.54 41.43 24.38
C5 84.13 28.05 62.98 41.43 101.53 27.94
C6 25.75 29.67 28.93 24.38 27.94 66.57
---------------------------------------------------------------------
First Stability Coefficients:
---------------------------------------------------------------------
trait genotype superiority static wricke predicted.value
1 yield G57 22.56100 33.35260 15.210394 92.44993
2 yield G29 16.98170 34.39632 4.243540 99.41798
3 yield G34 16.98545 33.64634 8.152888 99.81112
4 yield G59 16.68886 34.56884 5.361337 99.91280
5 yield G31 15.97985 31.29838 12.350068 101.69455
6 yield G10 15.61917 32.24828 11.747802 102.32827Exploring Factor Analytic in MET analysis.
pvals <- met_results$trial_effects
model <- met_results$met_models$yield
fa_objt <- fa_summary(
model = model,
trial = "trial",
genotype = "genotype",
BLUEs_trial = pvals,
k_biplot = 8,
size_label_var = 4,
filter_score = 1
)fa_objt$plots$loadings_c
fa_objt$plots$biplot
For more information and to learn more about what is described here you may find useful the following sources: Isik, Holland, and Maltecca (2017); Rodriguez-Alvarez et al. (2018).
Code of Conduct
Please note that the agriutilities project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.