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Example data for OptiSparseMET

Source: R/data_example.R
OptiSparseMET_example_data.Rd

Synthetic dataset for demonstrating and testing sparse multi-environment trial allocation and within-environment field design construction across the functions of OptiSparseMET. All components are internally consistent: treatment IDs, family labels, seed inventories, relationship matrices, and environment specifications refer to the same set of synthetic lines and environments, so any combination of components can be passed to the package functions without modification.

Usage

OptiSparseMET_example_data

Format

A named list with the following components:

treatments

Character vector of test treatment IDs. Candidate lines to be allocated across environments. Does not include check treatments.

environments

Character vector of environment names. Used as the environments argument in allocate_sparse_met() and as keys in env_design_specs.

treatment_info

Data frame with columns Treatment and Family. Used as the treatment_info argument in allocate_sparse_met() when allocation_group_source = "Family".

common_treatments

Character vector of treatment IDs forced into all environments before sparse allocation. A subset of treatments.

seed_info

Data frame with columns Treatment and SeedAvailable. One row per treatment in treatments. Used as the seed_available argument in assign_replication_by_seed().

seed_required_per_plot

Data frame with columns Environment and SeedRequiredPerPlot. Per-plot seed requirement for each environment.

OptiSparseMET_GRM

Numeric matrix. Genomic relationship matrix with row and column names matching treatments. Used when allocation_group_source = "GRM", cluster_source = "GRM", or dispersion_source = "GRM".

OptiSparseMET_A

Numeric matrix. Pedigree relationship matrix with row and column names matching treatments. Used when allocation_group_source = "A", cluster_source = "A", or dispersion_source = "A".

OptiSparseMET_K

Numeric matrix. Relationship matrix for mixed-model efficiency evaluation and dispersion optimisation. Used when prediction_type %in% c("GBLUP", "PBLUP") or dispersion_source = "K".

sparse_example_args_random_balanced

Named list of arguments ready to pass to allocate_sparse_met() with allocation_method = "random_balanced".

sparse_example_args_balanced_incomplete

Named list of arguments ready to pass to allocate_sparse_met() with allocation_method = "balanced_incomplete".

env_design_specs

Named list with one element per environment. Each element is a named list specifying the local field design for that environment. The design field in each element is set to either "met_prep_famoptg" or "met_alpha_rc_stream" – the OptiSparseMET-specific versions of the within-environment constructors. These carry the met_ prefix to avoid namespace conflicts when both OptiSparseMET and OptiDesign are loaded simultaneously.

Source

Synthetically generated for package documentation and testing.

Details

All data are synthetic and generated solely for use in package examples, unit tests, and vignettes. No real breeding trial data are included. Relationship matrices (OptiSparseMET_GRM, OptiSparseMET_A, OptiSparseMET_K) are positive semi-definite by construction and have row and column names consistent with treatments, so they can be used directly without an id_map.

The seed_info and seed_required_per_plot components are calibrated so that a range of replication outcomes (fully replicated, partially replicated, and excluded treatments) arise naturally when passed to assign_replication_by_seed(), making the data useful for demonstrating all three replication modes.

The env_design_specs component is ready to pass directly to plan_sparse_met_design() via the env_design_specs argument. Each specification uses design = "met_prep_famoptg" or design = "met_alpha_rc_stream" to select the local design engine.

Illustrative workflow

data("OptiSparseMET_example_data", package = "OptiSparseMET")
x <- OptiSparseMET_example_data

# Step 0: check feasibility
suggest_safe_k(x$treatments, x$environments, buffer = 3)

# Step 1: allocate across environments
alloc <- do.call(allocate_sparse_met,
  x$sparse_example_args_random_balanced)

# Step 2: seed-aware replication for one environment
rep_plan <- assign_replication_by_seed(
  treatments             = alloc$allocation_long$Treatment[
    alloc$allocation_long$Environment == "E1" &
    alloc$allocation_long$Assigned == 1L],
  seed_available         = x$seed_info,
  seed_required_per_plot = x$seed_required_per_plot$SeedRequiredPerPlot[
    x$seed_required_per_plot$Environment == "E1"],
  replication_mode       = "p_rep"
)

# Step 3: within-environment design (using met_ prefix function)
design_E1 <- met_prep_famoptg(
  check_treatments        = x$common_treatments,
  check_families          = rep("CHECK", length(x$common_treatments)),
  p_rep_treatments        = rep_plan$p_rep_treatments,
  p_rep_reps              = rep_plan$p_rep_reps,
  p_rep_families          = x$treatment_info$Family[
    match(rep_plan$p_rep_treatments, x$treatment_info$Treatment)],
  unreplicated_treatments = rep_plan$unreplicated_treatments,
  unreplicated_families   = x$treatment_info$Family[
    match(rep_plan$unreplicated_treatments, x$treatment_info$Treatment)],
  n_blocks = 4L, n_rows = 10L, n_cols = 12L
)

# Or run the full pipeline end-to-end
met_result <- plan_sparse_met_design(
  treatments                     = x$treatments,
  environments                   = x$environments,
  n_test_entries_per_environment = suggest_safe_k(x$treatments, x$environments),
  common_treatments              = x$common_treatments,
  env_design_specs               = x$env_design_specs,
  treatment_info                 = x$treatment_info,
  seed_info                      = x$seed_info,
  seed_required_per_plot         = x$seed_required_per_plot,
  seed                           = 123
)