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conditional
for predict
method to control whether the prediction is conditional on the
observation or not.predict
and simulate
will fail.
This is fixed now.mmrm
will
fail. This is fixed now.Anova
fail. This is fixed now.Anova
is implemented for mmrm
models and
available upon loading the car
package. It supports type II
and III hypothesis testing.start
for mmrm_control()
is
updated to allow better choices of initial values.confint
on mmrm
models will give t-based
confidence intervals now, instead of the normal approximation.mmrm_control()
, the allowed
vcov
definition is corrected to “Empirical-Jackknife”
(CR3), and “Empirical-Bias-Reduced” (CR2).df_md
, it
will return statistics with NA
values.method
of mmrm()
now only
specifies the method used for the degrees of freedom adjustment.vcov
argument of mmrm()
.model.matrix()
and terms()
methods to
assist in post-processing.predict()
method to obtain conditional mean
estimates and prediction intervals.simulate()
method to simulate observations from the
predictive distribution.residuals()
method to obtain raw, Pearson or
normalized residuals.tidy()
, glance()
and
augment()
methods to tidy the fit results into summary
tables.tidymodels
framework support via a
parsnip
interface.covariance
to mmrm()
to allow
for easier programmatic access to specifying the model’s covariance
structure and to expose covariance customization through the
tidymodels
interface.mmrm()
follows the global option
na.action
and if it is set other than
"na.omit"
an assertion would fail. This is now fixed and
hence NA
values are always removed prior to model fitting,
independent of the global na.action
option.model.frame()
call on an mmrm
object with transformed terms, or new data,
e.g. model.frame(mmrm(Y ~ log(X) + ar1(VISIT|ID), data = new_data)
,
would fail. This is now fixed.mmrm()
always required a data
argument. Now fitting mmrm
can also use environment
variables instead of requiring data
argument. (Note that
fit_mmrm
is not affected.)emmeans()
failed when using transformed
terms or not including the visit variable in the model formula. This is
now fixed.mmrm()
might provide non-finite values in
the Jacobian calculations, leading to errors in the Satterthwaite
degrees of freedom calculations. This will raise an error now and thus
alert the user that the model fit was not successful.options(mmrm.max_visits = )
to specify the maximum number
of visits allowed in non-interactive mode.free_cores()
in favor of
parallelly::availableCores(omit = 1)
.model.frame()
method has been updated: The
full
argument is deprecated and the include
argument can be used instead; by default all relevant variables are
returned. Furthermore, it returns a data.frame
the size of
the number of observations utilized in the model for all combinations of
the include
argument when
na.action= "na.omit"
.component(., "optimizer")
instead of previously
attr(., "optimizer")
.mmrm
function call with
argument method
. Options are “Kenward-Roger”,
“Kenward-Roger-Linear” and “Satterthwaite” (which is still the default).
Subsequent methods calls will respect this initial choice,
e.g. vcov(fit)
will return the adjusted coefficients
covariance matrix if a Kenward-Roger method has been used.mmrm
arguments to allow users more
fine-grained control, e.g.
mmrm(..., start = start, optimizer = c("BFGS", "nlminb"))
to set the starting values for the variance estimates and to choose the
available optimizers. These arguments will be passed to the new function
mmrm_control
.drop_visit_levels
to allow users to
keep all levels in visits, even when they are not observed in the data.
Dropping unobserved levels was done silently previously, and now a
message will be given. See ?mmrm_control
for more
details.mmrm
calls, the weights
object in the environment where the formula is defined was replaced by
the weights
used internally. Now this behavior is removed
and your variable weights
e.g. in the global environment
will no longer be replaced.free_cores()
in favor of
parallelly::availableCores(omit = 1)
.optimizer = "automatic"
in favor of not
specifying the optimizer
. By default, all remaining
optimizers will be tried if the first optimizer fails to reach
convergence.emmeans
package for computing
estimated marginal means (also called least-square means) for the
coefficients.summary
, logLik
, etc.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.