custom: new argument in the main analysis functions that allows the user to replace the JAGS syntax for sub-models with custom syntax. The argument expects a named list where the names are the names of the variables for which the custom model will be used. This feature requires some knowledge of JAGS syntax.append_data_list: new argument in the main analysis functions that allows the user to add elements to the list of data that is passed to JAGS. This may be necessary for custom sub-models.rd_vcov: new argument in the main analysis functions that allows the specification of the structure of the random effects variance-covariance matrices in (multivariate) mixed models (and joint models).plot_all() not displaying the variable name in the message shown for character string variables.set_refcat(): wasn’t displaying the factor labels correctlyprint.JointAI: bugfix for multivariate mixed models with full variance-covariance matrix of the random effects that had the same number of random effects for each outcome, for which the printing of this matrix resulted in an error.rd_vcov(): new function added to extract the random effect variance- covariance matrices (posterior means)*_imp() functions from within another function that has the model formula as an argument.data_list: omit data matrix M_* from data_list if ncol == 0data_list: syntax to checking which pos_* to include can handle the case with multiple grouping variables being on the same, lowest level; before pos_* was excluded for only one of themadd_samples(): remove unnecessary call to doFuture::registerDoFuture()predDF() bug fix: the parameter for all methods is now called objectlist_models() now also works for errored JointAI objectsThis version of JointAI contains some major changes. To extend the package it was necessary to change the internal structure and it was not possible to assure backward compatibility.
betareg_imp(): beta regressionbetamm_imp(): beta mixed modellognorm_imp(): log-normal regression modellognormmm_imp(): log-normal mixed modelmlogit_imp(): multinomial logit modelmlogitmm_imp(): multinomial mixed modelJM_imp(): joint model for longitudinal and survival dataIt is now possible to fit hierarchical models with more than one level of grouping, with nested as well as crossed random effects (check the help file) of the main model function for details on how to specify such random effects structures.
This does also apply to survival models, i.e., it is possible to specify a random effects structure to model survival outcomes in data with a hierarchical structure, e.g., in a multi-centre setting.
coxph_imp() can now handle time-dependent covariates using last-observation-carried-forward. This requires to add (1 | <id variable>) to the model formula to identify which rows belong to the same subject, and to specify the argument timevar to identify the variable that contains the observation time of the longitudinal measurements.
By providing a list of model formulas it is possible to fit multiple analysis models (of different types) simultaneously. The models can share covariates and it is possible to have the response of one model as covariate in another model (in a sequential manner, however, not circular).
As before, proportional odds are assumed by default for all covariates of a cumulative logit model. The argument nonprop accepts a one-sided formula or a named list of one-sided formulas in which the covariates are specified for which non-proportional odds should be assumed.
Additionally, the argument rev is available to specify a vector of names of ordinal responses for which the odds should be inverted. For details, see the the help file.
lmer_imp() and glmer_imp() are aliases for lme_imp() and glme_imp().fixed and random) as well as using specification as in lme4 (with a combined model formula for fixed and random effects).df_basehaz in coxph_imp() and JM_imp() allows setting the number of degrees of freedom in the B-spline basis used to model the baseline hazard.options("contrasts") is no longer ignored. For completely observed covariates, any of the contrasts available in base R are possible and options("contrasts") is used to determine which contrasts to use. For covariates with missing values, effect coding (contr.sum) and dummy coding (contr.treatment) are possible. This means that for completely observed ordered factors the default is contr.poly, but for incomplete ordered factors the default is contr.treatment.summary(), the argument multivariate to the function GR_crit() is now set to FALSE to avoid issues. The multivariate version can still be obtained by using GR_crit() directly.coxph_imp() and JM_imp() are monitored automatically when “analysis_main = TRUE”.get_models() is no longer exported because it now requires more input variables and is no longer convenient to use. To obtain the default specification of the model types use one of the main functions (*_imp()), set n.adapt = 0 (and n.iter = 0), and obtain the model types as <mymodel>$models.list_models() now returns information on all sub-models, including the main analysis models (previously it included only information on covariates).parameters() has changed. The function returns a list of matrices, one per analysis model, with information on the response variable, response category, name of the regression coefficient and its associated covariate.missinfo = TRUE in summar() adds information on the missing values in the data involved in a JointAI model (number and proportion of complete cases, number and proportion of missing values per variable).ridge has changed to shrinkage. If shrinkage = "ridge", a ridge prior is used for all regression coefficients in all sub-models. If a vector of response variable names is provided to shinkage, ridge priors are only used for the coefficients in these models.default_hyperpars(): The default hyper-parameters for random effects are no longer provided as a function but more consistently with the hyper-parameters for other model parts, as a vector (within the list of all hyper-parameters).default_hyperpars(): the default number of degrees of freedom in the Wishart distribution used for the inverse of the random effects covariance matrix is now the number of random effects + 1 (was the number of random effects before).seed in JointAI is now only local, i.e., in functions in which set.seed() is called, the random number generator state .Random.seed before setting the seed is recorded, and re-set to that value on exiting the function.predDF() the argument var has changed to vars and expects a formula. This extends the functionality of predDF() to let multiple variables vary.JointAI object have changed. Potentially relevant for users:
JointAI objects now contain information on the computational time and environment they were run in: the element comp_info contains the time-stamp the model was fitted, the duration of the computation, the JointAI version number and the R session info.jagsmodel.warn and mess now also affect the output of rjags.future::plan() for how the “future” should be handled. As a consequence, the arguments parallel and n.cores are no longer used. Information on the setting that was used with regards to parallel computation is returned in a JointAI object via comp_list$future.clmm covariate model when there are no baseline covariates in the modelnewdata for predict() that does not contain the outcome variableadd_samples(): calculation of end() of MCMC samples fixedadd_samples() when used in parallel with thinning fixedpredict() can now handle newdata with missing outcome values; predicted values for cases with missing covariates are NA (prediction with incomplete covariates is planned to be implemented in the future)get_MIdat() and plot_imp_distr() when only one variable has missing valuesncores has changed to n.cores for consistency with n.iter, n.chains, etc.coxph_imp() does no longer use a counting process implementation but uses the likelihood in JAGS directly via the zeros trickpredict() now has an argument length to change number of evaluation pointssummary(), predict(), traceplot(), densplot(), GR_crit(), MC_error() now have an argument exclude_chains that allows to specify chains that should be omittedcitation() now refers to a manuscript on arXivglmm_lognorm available to impute level-1 covariates with a log-normal mixed modelresiduals() and plot() available for (some of the) main analysis types (details see documentation)models added to get_models() so that the user can specify to also include models for complete covariates (which are then positioned in the sequence of models according to the systematic used in JointAI). Specification of a model not needed for imputation prints a notification.JointAI objects (most types) now also include residuals and fitted values (so far, only using fixed effects)print.JointAI fixedXl is no longer included in data_list when it is not used in the modelsubset when specified as vectorsummary: range of iterations is printed correctly now when argument end is usedsummary() calls GR_crit() with argument autoburnin = FALSE unless specified otherwise via ...inits is specified as a function, the function is evaluated and the resulting list passed to JAGS (previously the function was passed to JAGS)simong and simWide have changed (more variables, less subjects)imp_pars = TRUE (when user specified via monitor_params or subset)survreg_imp the sign of the regression coefficient is now opposite to match the one from survregmeth has changed to modelsadd_samples(): bug that copied the last chain to all other chains fixedXc, so that specification of functions of covariates in auxiliary variables works betterdensplot() issue (all plots showed all lines) fixedplot_all(), densplot(), and traceplot() limit the number of plots on one page to 64 when rows and columns of the layout are not user specified (to avoid the ‘figure margins too large’ error)longDF example data: new version containing complete and incomplete categorical longitudinal variables (and variable names L1 and L2 changed to c1 and c2)list_impmodels() changed to list_models() (but list_impmodels() is kept as an alias for now)clm_imp() and clmm_imp(): new functions for analysis of ordinal (mixed) modelscoxph_imp(): new function to fit Cox proportional hazards models with incomplete (baseline) covariatesno_model allows to specify names of completely observed variables for which no model should be specified (e.g., “time” in a mixed model)ridge = TRUE allows to use shrinkage priors on the precision of the regression coefficients in the analysis modelplot_all() can now handle variables from classes Date and POSIXtparallel allows different MCMC chains to be sampled in parallelncores allows to specify the maximum number of cores to be usedseed added for reproducible results; also a sampler (.RNG.name) and seed value for the sampler (.RNG.seed) are set or added to user-provided initial values (necessary for parallel sampling and reproducibility of results)plot_imp_distr(): new function to plot distribution of observed and imputed valuesRinvD is no longer selected to be monitored in random intercept model (RinvD is not used in such a model)summary(): reduced default number of digitsmeth now uses default values if only specified for subset of incomplete variablesget_MIdat(): argument minspace added to ensure spacing of iterations selected as imputationsdensplot(): accepts additional options, e.g., lwd, col, …list_models() replaces the function list_impmodels() (which is now an alias)coef() method added for JointAI object and summary.JointAI objectconfint() method added for JointAI objectprint() method added for JointAI objectsurvreg_imp() added to perform analysis of parametric (Weibull) survival modelsglme_imp() added to perform generalized linear mixed modellingtraceplot(), densplot(): specification of nrow AND ncol possible; fixed bug when only nrow specifiedcontrast.arg that now in some cases cause error| # JointAI 0.3.2 |
## Bug fixes * lme_imp(): fixed error in JAGS model when interaction between random slope variable and longitudinal variable |
| ## Minor changes * unused levels of factors are dropped |
plot_all() uses correct level-2 %NA in titlesimWide: case with no observed bmi values removedtraceplot(), densplot(): ncol and nrow now work with use_ggplot = TRUEtraceplot(), densplot(): error in specification of nrow fixeddensplot(): use of color fixedsubset now return random effects covariance matrix correctlysummary() displays output with row name when only one node is returned and fixed display of D matrixGR_crit(): Literature reference correctedpredict(): prediction with varying factor fixedplot_all() uses xpd = TRUE when printing text for character variableslist_impmodels() uses line break when output of predictor variables exceeds getOption("width")summary() now displays tail-probabilities for off-diagonal elements of Dpredict(): now also returns newdata extended with prediction| # JointAI 0.3.0 |
## Bug fixes * monitor_params is now checked to avoid problems when only part of the main parameters is selected * categorical imputation models now use min-max trick to prevent probabilities outside [0, 1] * initial value generation for logistic analysis model fixed * bug-fix in re-ordering columns when a function is part of the linear predictor * bug-fix in initial values for categorical covariates * bug-fix in finding imputation method when function of variable is specified as auxiliary variable |
## Minor changes * md.pattern() now uses ggplot, which scales better than the previous version * lm_imp(), glm_imp() and lme_imp() now ask about overwriting a model file * analysis_main = T stays selected when other parameters are followed as well * get_MIdat(): argument include added to select if original data are included and id variable .id is added to the dataset * subset argument uses same logit as monitor_params argument * added switch to hide messages; distinction between messages and warnings * lm_imp(), glm_imp() and lme_imp() now take argument trunc in order to truncate the distribution of incomplete variables * summary() now omits auxiliary variables from the output * imp_par_list is now returned from JointAI models * cat_vars is no longer returned from lm_imp(), glm_imp() and lme_imp(), because it is contained in Mlist$refs |
## Extensions * plot_all() function added * densplot() and traceplot() optional with ggplot * densplot() option to combine chains before plotting * example datasets NHANES, simLong and simWide added * list_impmodels to print information on the imputation models and hyper-parameters * parameters() added to display the parameters to be/that were monitored * set_refcat() added to guide specification of reference categories * extension of possible functions of variables in model formula to (almost all) functions that are available in JAGS * added vignettes Minimal Example, Visualizing Incomplete Data, Parameter Selection and Model Specification |
md_pattern(): does not generate duplicate plot any moreget_MIdat(): imputed values are now filled in in the correct orderget_MIdat(): variables imputed with lognorm are now included when extracting an imputed datasetget_MIdat(): imputed values of transformed variables are now included in imputed datasetsmeth argumentmd.pattern(): adaptation to new version of md.pattern() from the mice packageNaN to NAgamma and beta imputation methods implemented