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Data for TMB Fit

Source: R/tmb.R
h_mmrm_tmb_data.Rd

Data for TMB Fit

Usage 

h_mmrm_tmb_data(
  formula_parts,
  data,
  weights,
  reml,
  singular = c("drop", "error", "keep"),
  drop_visit_levels,
  allow_na_response = FALSE,
  drop_levels = TRUE,
  xlev = NULL,
  contrasts = NULL,
  emmeans_gcomp_vars = NULL
)

Arguments

formula_parts

(mmrm_tmb_formula_parts)
list with formula parts from h_mmrm_tmb_formula_parts().

data

(data.frame)
which contains variables used in formula_parts.

weights

(vector)
weights to be used in the fitting process.

reml

(flag)
whether restricted maximum likelihood (REML) estimation is used, otherwise maximum likelihood (ML) is used.

singular

(string)
choices of method deal with rank-deficient matrices. "error" to stop the function return the error, "drop" to drop these columns, and "keep" to keep all the columns.

drop_visit_levels

(flag)
whether to drop levels for visit variable, if visit variable is a factor.

allow_na_response

(flag)
whether NA in response is allowed.

drop_levels

(flag)
whether drop levels for covariates. If not dropped could lead to singular matrix.

xlev

(list or NULL)
list of X levels produced by stats::.getXlevels

contrasts

(list or NULL)
an optional named list of contrast matrices or contrast functions (like stats::contr.sum or stats::contr.poly) for specific factor variables, matching the contrasts argument in stats::lm(). The list names must correspond to factor variable names in the model formula. When NULL (the default), the contrasts set on the factor variables in data are used. If a contrast matrix has rownames that include levels not present in data, those levels are preserved and the corresponding model matrix columns are marked as aliased (not estimable), enabling prediction on new data containing those levels.

emmeans_gcomp_vars

(character or NULL) treated as fixed for G-computation correction. Stored as a character vector in the returned list for downstream use in the emmeans hook.

Value

List of class mmrm_tmb_data with elements:

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

  • data: data.frame of input dataset.

  • x_matrix: matrix with n rows and p columns specifying the overall design matrix.

  • x_cols_aliased: logical with potentially more than p elements indicating which columns in the original design matrix have been left out to obtain a full rank x_matrix.

  • y_vector: length n numeric specifying the overall response vector.

  • weights_vector: length n numeric specifying the weights vector.

  • n_visits: int with the number of visits, which is the dimension of the covariance matrix.

  • n_subjects: int with the number of subjects.

  • subject_zero_inds: length n_subjects integer containing the zero-based start indices for each subject.

  • subject_n_visits: length n_subjects integer containing the number of observed visits for each subjects. So the sum of this vector equals n.

  • cov_type: string value specifying the covariance type.

  • is_spatial_int: int specifying whether the covariance structure is spatial(1) or not(0).

  • reml: int specifying whether REML estimation is used (1), otherwise ML (0).

  • subject_groups: factor specifying the grouping for each subject.

  • n_groups: int with the number of total groups

  • emmeans_gcomp_vars: character or NULL with the G-computation variable names.

Details

Note that the subject_var must not be factor but can also be character. If it is character, then it will be converted to factor internally. Here the levels will be the unique values, sorted alphabetically and numerically if there is a common string prefix of numbers in the character elements. For full control on the order please use a factor.