brm_formula_sigma {brms.mmrm}

Formula for standard deviation parameters

Description

Parameterize standard deviations using a formula for the sigma argument of brm_formula().

Usage

brm_formula_sigma(
  data,
  check_rank = TRUE,
  intercept = FALSE,
  baseline = FALSE,
  baseline_subgroup = FALSE,
  baseline_subgroup_time = FALSE,
  baseline_time = FALSE,
  covariates = FALSE,
  group = FALSE,
  group_subgroup = FALSE,
  group_subgroup_time = FALSE,
  group_time = FALSE,
  subgroup = FALSE,
  subgroup_time = FALSE,
  time = TRUE
)

Arguments

data

A classed data frame from brm_data(), or an informative prior archetype from a function like brm_archetype_successive_cells().

check_rank

TRUE to check the rank of the model matrix for sigma and throw an error if rank deficiency is detected. FALSE to skip this check. Rank-deficiency may cause sigma to be non-identifiable, may prevent the MCMC from converging.

intercept

Logical of length 1. TRUE (default) to include an intercept, FALSE to omit.

baseline

Logical of length 1. TRUE to include an additive effect for baseline response, FALSE to omit. If TRUE, then effect size will be omitted from the output of brm_marginal_draws().

baseline_subgroup

Logical of length 1.

baseline_subgroup_time

Logical of length 1. TRUE to include baseline-by-subgroup-by-time interaction, FALSE to omit. If TRUE, then effect size will be omitted from the output of brm_marginal_draws().

baseline_time

Logical of length 1. TRUE to include baseline-by-time interaction, FALSE to omit. If TRUE, then effect size will be omitted from the output of brm_marginal_draws().

covariates

Logical of length 1. TRUE (default) to include any additive covariates declared with the covariates argument of brm_data(), FALSE to omit. If TRUE, then effect size will be omitted from the output of brm_marginal_draws().

group

Logical of length 1. TRUE (default) to include additive effects for treatment groups, FALSE to omit.

group_subgroup

Logical of length 1. TRUE to include group-by-subgroup interaction, FALSE to omit.

group_subgroup_time

Logical of length 1. TRUE to include group-by-subgroup-by-time interaction, FALSE to omit.

group_time

Logical of length 1.

subgroup

Logical of length 1. TRUE to include additive fixed effects for subgroup levels, FALSE to omit.

subgroup_time

Logical of length 1. TRUE to include subgroup-by-time interaction, FALSE to omit.

time

Logical of length 1.

Details

In brms, the standard deviations of the residuals are modeled through a parameter vector called sigma. brms.mmrm always treats sigma as a distributional parameter (https://paul-buerkner.github.io/brms/articles/brms_distreg.html). brm_formula_sigma() lets you control the parameterization of sigma. The output of brm_formula_sigma() serves as input to the sigma argument of brm_formula().

The default sigma formula is sigma ~ 0 + time, where time is the discrete time variable in the data. This is the usual heterogeneous variance structure which declares one standard deviation parameter for each time point in the data. Alternatively, you could write brm_formula_sigma(data, intercept = TRUE, time = FALSE). This will produce sigma ~ 1, which yields a single scalar variance (a structure termed "homogeneous variance").

With arguments like baseline and covariates, you can specify extremely complicated variance structures. However, if baseline or covariates are used, then the output of brm_marginal_draws() omit effect size due to the statistical challenges of calculating marginal means of draws of sigma for this uncommon scenario.

Value

A base R formula with S3 class "brms_mmrm_formula_sigma". This formula controls the parameterization of sigma, the linear-scale brms distributional parameters which represent standard deviations.

See Also

Other models: brm_formula(), brm_model()

Examples

set.seed(0)
data <- brm_data(
  data = brm_simulate_simple()$data,
  outcome = "response",
  group = "group",
  time = "time",
  patient = "patient",
  reference_group = "group_1",
  reference_time = "time_1"
)
homogeneous <- brm_formula_sigma(data, time = FALSE, intercept = TRUE)
by_group <- brm_formula_sigma(data, group = TRUE, intercept = TRUE)
homogeneous
by_group
brm_formula(data, sigma = homogeneous)
brm_formula(data, sigma = by_group)