Low-Level Fitting Function for MMRM

This is the low-level function to fit an MMRM. Note that this does not try different optimizers or adds Jacobian information etc. in contrast to mmrm().

Usage

fit_mmrm(
  formula,
  data,
  weights,
  reml = TRUE,
  covariance = NULL,
  tmb_data,
  formula_parts,
  control = mmrm_control()
)

Arguments

formula: (formula)
model formula with exactly one special term specifying the visits within subjects, see details.
data: (data.frame)
input data containing the variables used in formula.
weights: (vector)
input vector containing the weights.
reml: (flag)
whether restricted maximum likelihood (REML) estimation is used, otherwise maximum likelihood (ML) is used.
covariance: (cov_struct)
A covariance structure type definition, or value that can be coerced to a covariance structure using as.cov_struct(). If no value is provided, a structure is derived from the provided formula.
tmb_data: (mmrm_tmb_data)
object.
formula_parts: (mmrm_tmb_formula_parts)
list with formula parts from h_mmrm_tmb_formula_parts().
control: (mmrm_control)
list of control options produced by mmrm_control().

Value

List of class mmrm_tmb, see h_mmrm_tmb_fit() for details.

Details

The formula typically looks like:

FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)

which specifies response and covariates as usual, and exactly one special term defines which covariance structure is used and what are the visit and subject variables.

Always use only the first optimizer if multiple optimizers are provided.

Examples

formula <- FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
data <- fev_data
system.time(result <- fit_mmrm(formula, data, rep(1, nrow(fev_data))))
#>    user  system elapsed 
#>   0.047   0.000   0.046