generate_mme_workload: Generate Parameterized MME Workloads for Solver Evaluation

In sommer: Solving Mixed Model Equations in R

Generate Parameterized MME Workloads for Solver Evaluation

Description

Instantiates reproducible synthetic mixed model datasets and formulas spanning factor dimensions, covariance structures, relationship densities, and tasks.

Usage

generate_mme_workload(name = NULL,
                      n_ind = 60L,
                      n_levels = 5L,
                      structure = c("ar1", "ar2", "us", "diag", "fa",
                                    "rr", "spatial", "sar", "car"),
                      relationship = c("none", "dense", "sparse"),
                      missing_rate = 0.0,
                      reml = TRUE,
                      computeCi = 0L,
                      seed = 42L)

Arguments

name	Descriptive label for the workload.
n_ind	Number of unique individuals / genotypes. Default is 60.
n_levels	Number of factor levels (e.g. environments, timepoints, or sites). Default is 5.
structure	Covariance structure type. Supported: "ar1", "ar2", "us", "diag", "fa", "rr", "spatial", "sar", "car".
relationship	Individual relationship matrix: "none" (identity), "dense" (genomic relationship inverse), or "sparse" (pedigree inverse).
missing_rate	Fraction of missing response values (0.0 to 0.5). Default is 0.0.
reml	Logical; whether to fit by REML (TRUE) or ML (FALSE). Default is TRUE.
computeCi	Final inverse demand: 0 (none), 1 (Takahashi selected inverse), 2 (full inverse).
seed	Random seed for reproducible dataset simulation.

Value

A list of class "mme_workload" containing data, formulas, REML and computeCi settings, and workload metadata.

See Also

mmes, mmes_control

Examples

# Generate a small 1D autoregressive workload
wl <- generate_mme_workload(
  name = "demo_ar1",
  n_ind = 20,
  n_levels = 4,
  structure = "ar1"
)

sommer documentation built on Oct. 24, 2026, 1:08 a.m.