Rcpp specific flag to avoid warnings when using
the upcoming new version of Rcpp.Anova.mmrm() now has a test.statistic
argument allowing the user to choose the Chi-squared test as opposed to
the default F-test.mmrm_control() now has a
disable_theta_vcov argument allowing the user to disable
computation of the variance-covariance matrix of the covariance
parameters. In that case, the nlminb optimizer cannot be
used and other optimizers should be selected. Avoiding this computation
can be useful in cases where the number of covariance parameters is
large and computation of this very large matrix is slow and
memory-intensive.model.frame() has a new argument exclude
which allows to exclude specific model variables from the returned data
frame, after creating the initial model.frame.
This is useful when the same rows should be kept as in the original
model fit (e.g. after na.action has been applied), but some
variables are not needed in the returned data frame. The
terms attribute of the returned data frame is updated
accordingly.cs and csh) could have divergence issues,
especially when the correlation was negative. This was because the range
of the correlation parameter was from -1 to 1, whereas the correct range
is between -1/(m-1) and 1, where m is the number of observations per
subject. This is fixed now by using another mapping from the real line
to the correct range, and therefore compound symmetry correlation models
will work more robustly.predict() method failed when requesting
an unconditional prediction interval. This is fixed now.emp_start() starting values could only
work with variables included directly in data. Now they can
also work when variables are not included in data but are
available in the parent environment: The variables will be correctly
included in the design matrix. This is achieved by an internal update to
h_mmrm_tmb_data().model.frame() method failed when the
model formula included variables from the environment. This is fixed
now. In addition, whereas model.frame() used to apply
na.action before processing the include
argument, na.action is now applied afterwards. As a result,
any variables excluded from include are no longer
considered when na.action is applied. Therefore, in the
default case when na.action = "na.omit", this can lead to
more rows being kept in the returned data frame compared to previous
versions.model.frame() method did not pass on
additional arguments to the model.frame.default() method,
e.g. subset. This is fixed now.vcov() method returned the asymptotic
covariance matrix, even if an adjusted one has been used. This is now
fixed and the requested adjusted covariance matrix will be returned.
(Please note this was not an issue for downstream summary()
or emmeans() calls, where the adjusted covariance matrix
was correctly used.)Anova.mmrm() method
could give incorrect results which were not compatible with results from
lme4::lmer() or other software (e.g. when using other than
treatment contrasts). This is fixed now. In particular, any combination
of contrasts can be used and will yield consistent results.Anova.mmrm() can now handle intercept-free models in
type 2 tests, even when the first effect containing a categorical
variable has an aliased parameter.Anova.mmrm() method were not recognized by
broom::tidy(). This is fixed now by using more standard
names for the resulting data frame columns (e.g. F instead
of F Statistic etc.)logLik.mmrm_tmb() now includes an nobs
attribute containing the number of subjects in the provided model (i.e.,
component(mmrm_object, "n_subjects")). As a result,
BIC(logLik(mmrm_object)) can now be successfully
calculated.mmrm now returns score per subject in empirical
covariance. It can be accessed by
component(obj, name = "score_per_subject").empirical_g_mat in the mmrm object, instead of
the previous empirical_df_mat matrix. The model fit is now
much faster and does not exhaust the memory anymore. If old model fit
objects are used, the empirical_df_mat will still be used
correctly, however a deprecation warning will be issued. Please consider
re-fitting the model to get the new empirical_g_mat
matrix.mmrm from source using a
TMB version below 1.9.15, and installing a newer
TMB of version 1.9.15 or above, would render the
mmrm package unusable. This is fixed now, by checking in
the dynamic library of mmrm whether the version of
TMB has been sufficient.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.
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