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TMB was
switched on, a warning would be given by fit_mmrm(),
instructing users to turn off the tape optimizer. However, this is not
necessary for reproducible results. Instead, it is now checked whether
the deterministic hash for the TMB tape optimizer is used,
and a warning is issued otherwise.fit_mmrm() was not visible to the user when calling
mmrm() because it was caught internally, causing the first
fit in each session to fail for the first tried optimizer and falling
back to the other optimizers. The warning is now issued directly by
mmrm(). This change ensures that the first model fit is
consistent regarding the chosen optimizer (and thus numeric results)
with subsequent model fits, avoiding discrepancies observed in version
0.3.13.TMB package versions below 1.9.15,
MMRM fit results are not completely reproducible. While this may not be
relevant for most applications, because the numerical differences are
very small, we now issue a warning to the user if this is the case. We
advise users to upgrade their TMB package versions to
1.9.15 or higher to ensure reproducibility.mmrm ignored contrasts defined for
covariates in the input data set. This is fixed now.predict always required the response to be
valid, even for unconditional predictions. This is fixed now and
unconditional prediction does not require the response to be valid or
present any longer.model.frame has been updated to ensure that the
na.action works correctly.emmeans::emmeans returned NA
for spatial covariance structures. This is fixed now.car::Anova gave incorrect results if an
interaction term is included and the covariate of interest was not the
first categorical variable. This is fixed now.car::Anova failed if the model did not
contain an intercept. This is fixed now.TMB is turned on. If so, a warning
is issued to the user once per session.mmrm now checks on the positive definiteness of the
covariance matrix theta_vcov. If it is not positive
definite, non-convergence is messaged appropriately.model.matrix has been updated to ensure that the
NA values are dropped. Additionally, an argument
use_response is added to decide whether records with
NA values in the response should be discarded.predict has been updated to allow duplicated subject
IDs for unconditional prediction.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.
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