Added xlimits
and ylimits
arguments to the g_mmrm_lsmeans
function.
scda
and scda.2022
in vignettes with random.cdisc.data
.Adapt to release 0.3 of the mmrm
package.
...
to mmrm::mmrm
when
calling fit_mmrm
. In particular, the method
argument allows to choose
Kenward-Roger adjustment of degrees of freedom and coefficients covariance
matrix.optimizer
argument when calling fit_mmrm
.parallelly
is now used internally to handle the determination of available cores.mmrm
package instead
of lme4
and lmerTest
. This greatly increases convergence and speed. Different
covariance structures and optimizers are now available compared to before.g_covariance()
to visualize a covariance matrix, which
can be helpful for choosing or visualizing the covariance structure in the MMRM.averages_emmeans
to fit_mmrm()
which allows estimation of
least square means for averages of visits.accept_singular
to fit_mmrm()
which allows estimation of
rank-deficient models (like lm()
and gls()
) by omitting the columns
of singular coefficients from the design matrix.show_lines
and xlab
to g_mmrm_lsmeans()
which allow the
addition of lines connecting the estimates, as well as a custom x-axis label.table_stats
, table_formats
, table_labels
, table_font_size
,
and table_rel_height
to g_mmrm_lsmeans()
which allow the addition of and
configure the appearance of an LS means estimates statistics table below the LS
means estimates plot.constant_baseline
and n_baseline
to g_mmrm_lsmeans()
which
allow plotting of a constant baseline value and specifying the corresponding
number of patients (visible in the optional table) for the LS means plots.purrr
, tibble
, scda
and scda.2022
mmrm_test_data
as sample data.tern
.