- The argument
mmrm()now only specifies the method used for the degrees of freedom adjustment.
- Add empirical, empirical Jackknife and empirical bias-reduced adjusted coefficients covariance estimates, which can be specified via the new
- Add residual and between-within degrees of freedom methods.
- Add Kenward-Roger support for spatial covariance structures.
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.
augment()methods to tidy the fit results into summary tables.
tidymodelsframework support via a
- Add argument
mmrm()to allow for easier programmatic access to specifying the model’s covariance structure and to expose covariance customization through the
mmrm()follows the global option
na.actionand if it is set other than
"na.omit"an assertion would fail. This is now fixed and hence
NAvalues are always removed prior to model fitting, independent of the global
- Previously a
model.frame()call on an
mmrmobject 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
dataargument. Now fitting
mmrmcan also use environment variables instead of requiring
dataargument. (Note that
fit_mmrmis 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.
- Use automatic differentiation to calculate Satterthwaite adjusted degrees of freedom, resulting in 10-fold speed-up of the Satterthwaite calculations after the initial model fit.
- Add an interactive confirmation step if the number of visit levels is too large for non-spatial covariance structures. Use
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
fullargument is deprecated and the
includeargument can be used instead; by default all relevant variables are returned. Furthermore, it returns a
data.framethe size of the number of observations utilized in the model for all combinations of the
CRAN release: 2022-12-20
- Add support for Kenward-Roger adjusted coefficients covariance matrix and degrees of freedom in
mmrmfunction 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.
- Update the
mmrmarguments 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
- Add new argument
drop_visit_levelsto 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_controlfor more details.
- Previously duplicate time points could be present for a single subject, and this could lead to segmentation faults if more than the total number of unique time points were available for any subject. Now it is checked that there are no duplicate time points per subject, and this is explained also in the function documentation and the introduction vignette.
- Previously in
weightsobject in the environment where the formula is defined was replaced by the
weightsused internally. Now this behavior is removed and your variable
weightse.g. in the global environment will no longer be replaced.
CRAN release: 2022-10-18
- First CRAN version of the package.
- The package fits mixed models for repeated measures (MMRM) based on the marginal linear model without random effects.
- The motivation for this package is to have a fast, reliable (in terms of convergence behavior) and feature complete implementation of MMRM in R.
- Currently 10 covariance structures are supported (unstructured; as well as homogeneous and heterogeneous versions of Toeplitz, auto-regressive order one, ante-dependence, compound symmetry; and spatial exponential).
- Fast C++ implementation of Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML) estimation.
- Currently Satterthwaite adjusted degrees of freedom calculation is supported.
- Interface to the
emmeanspackage for computing estimated marginal means (also called least-square means) for the coefficients.
- Multiple optimizers are run to reach convergence in as many cases as possible.
- Flexible formula based model specification and support for standard S3 methods such as