h_mmrm_tmb_data | R Documentation |
TMB
FitData for TMB
Fit
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
)
formula_parts |
( |
data |
( |
weights |
( |
reml |
( |
singular |
( |
drop_visit_levels |
( |
allow_na_response |
( |
drop_levels |
( |
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.
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
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