mmrm (version 0.3.14)

component: Component Access for mmrm_tmb Objects

Description

Usage

component(
  object,
  name = c("cov_type", "subject_var", "n_theta", "n_subjects", "n_timepoints", "n_obs",
    "beta_vcov", "beta_vcov_complete", "varcor", "formula", "dataset", "n_groups",
    "reml", "convergence", "evaluations", "method", "optimizer", "conv_message", "call",
    "theta_est", "beta_est", "beta_est_complete", "beta_aliased", "x_matrix", "y_vector",
    "neg_log_lik", "jac_list", "theta_vcov", "full_frame", "xlev", "contrasts")
)

Value

The corresponding component of the object, see details.

Arguments

object

(mmrm_tmb)
the fitted MMRM.

name

(character)
the component(s) to be retrieved.

Details

Available component() names are as follows:

• call: low-level function call which generated the model.

• formula: model formula.

• dataset: data set name.

• cov_type: covariance structure type.

• n_theta: number of parameters.

• n_subjects: number of subjects.

• n_timepoints: number of modeled time points.

• n_obs: total number of observations.

• reml: was REML used (ML was used if FALSE).

• neg_log_lik: negative log likelihood.

• convergence: convergence code from optimizer.

• conv_message: message accompanying the convergence code.

• evaluations: number of function evaluations for optimization.

• method: Adjustment method which was used (for mmrm objects), otherwise NULL (for mmrm_tmb objects).

• beta_vcov: estimated variance-covariance matrix of coefficients (excluding aliased coefficients). When Kenward-Roger/Empirical adjusted coefficients covariance matrix is used, the adjusted covariance matrix is returned (to still obtain the original asymptotic covariance matrix use object$beta_vcov).

• beta_vcov_complete: estimated variance-covariance matrix including aliased coefficients with entries set to NA.

• varcor: estimated covariance matrix for residuals. If there are multiple groups, a named list of estimated covariance matrices for residuals will be returned. The names are the group levels.

• theta_est: estimated variance parameters.

• beta_est: estimated coefficients (excluding aliased coefficients).

• beta_est_complete: estimated coefficients including aliased coefficients set to NA.

• beta_aliased: whether each coefficient was aliased (i.e. cannot be estimated) or not.

• theta_vcov: estimated variance-covariance matrix of variance parameters.

• x_matrix: design matrix used (excluding aliased columns).

• xlev: a named list of character vectors giving the full set of levels to be assumed for each factor.

• contrasts: a list of contrasts used for each factor.

• y_vector: response vector used.

• jac_list: Jacobian, see h_jac_list() for details.

• full_frame: data.frame with n rows containing all variables needed in the model.

See Also

In the lme4 package there is a similar function getME().

Examples

fit <- mmrm(
  formula = FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data
)
# Get all available components.
component(fit)
# Get convergence code and message.
component(fit, c("convergence", "conv_message"))
# Get modeled formula as a string.
component(fit, c("formula"))