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 likebrm_archetype_successive_cells()
.- check_rank
TRUE
to check the rank of the model matrix forsigma
and throw an error if rank deficiency is detected.FALSE
to skip this check. Rank-deficiency may causesigma
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. IfTRUE
, then effect size will be omitted from the output ofbrm_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. IfTRUE
, then effect size will be omitted from the output ofbrm_marginal_draws()
.- baseline_time
Logical of length 1.
TRUE
to include baseline-by-time interaction,FALSE
to omit. IfTRUE
, then effect size will be omitted from the output ofbrm_marginal_draws()
.- covariates
Logical of length 1.
TRUE
(default) to include any additive covariates declared with thecovariates
argument ofbrm_data()
,FALSE
to omit. IfTRUE
, then effect size will be omitted from the output ofbrm_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.
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.
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.
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
#> sigma ~ 1
#> attr(,"brm_allow_effect_size")
#> [1] TRUE
#> <environment: 0x559128182300>
by_group
#> sigma ~ group + time
#> attr(,"brm_allow_effect_size")
#> [1] TRUE
#> <environment: 0x55912747faf0>
brm_formula(data, sigma = homogeneous)
#> response ~ group + group:time + time + unstr(time = time, gr = patient)
#> sigma ~ 1
brm_formula(data, sigma = by_group)
#> response ~ group + group:time + time + unstr(time = time, gr = patient)
#> sigma ~ group + time