Skip to contents

Choose a design

Start from the number of entries and the field space you have.

design_options()
Suggest trial layouts from the number of entries and available field space
augmented_row_column_capacity()
Calculate augmented row-column design capacity
augmented_row_column_layouts()
List the analyzable layouts of an augmented row-column field
augmented_prep_parameters()
Feasible parameters for augmented p-rep constructions

Augmented row-column designs

Unreplicated entries plus replicated checks in rows, columns and blocks (Federer, Nair and Raghavarao 1975; Piepho and Williams 2016).

augmented_row_column_design()
Generate an augmented row-column field design
plot_augmented_row_column_design()
Plot an augmented row-column design grid
analyze_augmented_row_column_design()
Analyze an augmented row-column design
print(<augmented_row_column_analysis>)
Print augmented row-column analysis

Augmented p-rep designs

Multi-location partially replicated designs (Williams, Piepho and Whitaker 2011).

augmented_prep_design()
Generate an augmented p-rep design
augmented_prep_concurrence()
Calculate pairwise concurrence for a p-rep design
plot_augmented_prep_design()
Plot an augmented p-rep design grid
analyze_augmented_prep_design()
Analyze an augmented p-rep design
analyze_augmented_prep_two_stage()
Two-stage analysis of a multi-location augmented p-rep trial
print(<augmented_prep_analysis>)
Print augmented p-rep analysis
print(<augmented_prep_two_stage>)
Print a two-stage p-rep analysis

Resolvable row-column designs

Fully replicated spatial designs (Williams, John and Whitaker 2006).

resolvable_row_column_design()
Generate a resolvable (spatial) row-column design
resolvable_row_column_efficiency()
Efficiency of a resolvable row-column design
plot_resolvable_row_column_design()
Plot a resolvable row-column design
analyze_resolvable_row_column()
Analyze a resolvable row-column trial with RC, LV + RC or AR + RC models
print(<resolvable_row_column_analysis>)
Print a resolvable row-column analysis

Analysis for any design

Trait plots, outliers, two-stage and single-stage analysis of several trials, and the values to store from them.

plot_trait()
Plot a trait: distribution and field map
find_outliers()
Find outliers in trait data (IQR rule)
remove_outliers()
Remove outliers from trait data (IQR rule)
analyze_two_stage()
Two-stage analysis of a multi-location trial, for any design
analyze_single_stage()
Single-stage analysis of several trials
compare_first_stage()
Compare raw and first-stage adjusted values
first_stage_values()
First-stage plot values from an analysis
second_stage_values()
Second-stage genotype values from analyses
prediction_accuracy()
Accuracy of adjusted means against known values
print(<two_stage_analysis>)
Print a two-stage analysis
print(<single_stage_analysis>)
Print a single-stage analysis

Design checks

Verify a design and record a checklist of what was tested.

check_setup()
Check that everything is ready before starting
check_design()
Standard checks of a design
check_section()
Set the section label for the next checks
check_result()
Record and print a check
check_summary()
Summary of the recorded checks
check_reset()
Clear the recorded checks

Breedbase connection and lookups

Log in through BrAPI, list programs, folders and locations, and check names before designing.

breedbase_login()
Log in to a Breedbase server
breedbase_programs() breedbase_folders() breedbase_locations()
List breeding programs, folders and locations in Breedbase
breedbase_check()
Check names in Breedbase before building a design
breedbase_statistics()
List the statistic terms of Breedbase (SGNSTAT ontology)
breedbase_adjusted_trait()
Find or create the Breedbase trait for adjusted values
breedbase_refresh()
Refresh the Breedbase phenotype cache

Save designs to Breedbase

Convert agricolae designs, preview the payload, and save trials with their plots.

prepare_breedbase_trial()
Prepare an agricolae design and trial information for Breedbase
as_breedbase_design()
Convert an agricolae field book for saving to Breedbase
save_breedbase()
Save a design to Breedbase
breedbase_trial_plots()
Read the plots of a trial saved in Breedbase
phenotype_template()
Phenotyping template with the Breedbase plot names of a design

Phenotypes and analysis results in Breedbase

Read and save phenotypic data, first-stage plot values and second-stage analyses.

breedbase_phenotypes()
Read phenotypic data of trials from Breedbase
save_breedbase_phenotypes()
Save phenotypic data to Breedbase
save_breedbase_first_stage()
Save first-stage results to Breedbase as a trait of the field plots
save_breedbase_analysis()
Save analysis results to Breedbase as an analysis
add_breedbase_analysis_values()
Add results to an analysis that is already in Breedbase

Multi-environment trials from Breedbase files

Read Breedbase phenotype files, test homogeneity of error variances (Hartley’s Fmax) and group environments by GxE (Lin 1982).

read_breedbase_file()
Read phenotype or design data from Excel, CSV, or tab-delimited files
read_breedbase_excel()
Read a Breedbase Excel phenotype file
prepare_breedbase_pheno()
Prepare Breedbase phenotype data for multi-environment analysis
run_breedbase_variance_check()
Run Breedbase multi-location phenotype variance check
run_environment_stratification()
Run Lin-style environment stratification
run_breedbase_environment_stratification()
Run Lin-style environment stratification from a Breedbase phenotype file
read_locations()
Load location coordinates
map_environment_groups()
Create a map of environment groups

Variance check building blocks

Lower-level steps used by run_breedbase_variance_check().

analyze_environment_anova()
Run individual ANOVA by environment
analyze_one_environment()
Analyze one environment using ANOVA
hartley_fmax_test()
Hartley Fmax test for residual variance homogeneity
hartley_critical_sim()
Simulate Hartley Fmax critical value
make_environment_groups()
Test all compatible environment groups using Hartley Fmax
get_compatible_groups()
Get compatible environment groups
get_maximal_groups()
Select maximal compatible environment groups
harmonic_mean()
Calculate harmonic mean

Environment stratification building blocks

Lower-level steps used by run_environment_stratification().

prepare_environment_stratification_data()
Prepare phenotype data for Lin environment stratification
detect_design()
Detect field trial design for environment stratification
prepare_design_factors()
Prepare design factors
calculate_environment_stratification_anova()
Calculate design-aware ANOVA
lin_group_environments()
Group environments using Lin-style GE compatibility testing
lin_test_group()
Test one Lin environment group
lin_ss_ge()
Calculate Lin genotype-by-environment sum of squares
lin_error_mse()
Calculate residual error MSE for a Lin environment group
complete_environment_info()
Complete environment metadata
add_environment_metadata()
Add environment metadata to result table
add_pairwise_environment_metadata()
Add environment metadata to pairwise Lin results
environment_summary_by_group()
Summarize environment names by group

Column and table helpers

find_column()
Find the first matching column
guess_column()
Guess a column name from possible Breedbase column names
normalize_column_name()
Normalize Breedbase-style column names

Package

breedtools breedtools-package
breedtools: design, analysis and 'Breedbase' storage of plant breeding trials