JointAI 0.6.1

Bug fixes

Fixed bug that messed up coefficients in clmm covariate model when there are no baseline covariates in the model
enable newdata for predict() that does not contain the outcome variable
add_samples(): calculation of end() of mcmc samples fixed

JointAI 0.6.0

Bug fixes

bug in add_samples() when used in parallel with thinning fixed
bug fixed that occurred when a complete longitudinal categorical variable was used in a model that did not contain any incomplete baseline variables
bug-fix for monitoring random effects
fixed typo in selecting parameters in Gamma models
predict() 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)
bug-fix for get_MIdat() and plot_imp_distr() when only one variable has missing values
bug-fix for longitudinal model with interaction with random slope variable
bug-fix for model with multiple longitudinal ordinal incomplete covariates (fixed wrong selection of columns of the design matrix of longitudinal covariates in these models)

Minor changes

moved message about bug reports to startup
enabled inverse link by adding max restriction
“:” in factor labels are automatically replaced by "_"
argument ncores 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 trick

New Features / Extensions

predict() now has an argument length to change number of evaluation points
summary(), predict(), traceplot(), densplot(), GR_crit(), MC_error() now have an argument exclude_chains that allows to specify chains that should be omitted
citation() now refers to a manuscript on arXiv
glmm_lognorm available to impute level-1 covariates with a log-normal mixed model
methods residuals() and plot() available for (some of the) main analysis types (details see documentation)
argument 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)

JointAI 0.5.2

Bug fixes

Error message in print.JointAI fixed

JointAI 0.5.1

Bug fixes

bug in ordinal models with only completely observed variables fixed (all necessary data is not passed to JAGS)
enable thinning when using parallel sampling
matrix Xl is no longer included in data_list when it is not used in the model
bug-fix in subset when specified as vector
bug-fix in ridge regression (gave an error message)
bug-fix in recognition of binary factors that are coded as numeric and have missing values
bug-fix in summary: range of iterations is printed correctly now when argument end is used
bug-fix: error that occurred in re-scaling when reference category was changed is solved
bug-fix in survival models: coding of censoring variable fixed

Minor changes

summary() calls GR_crit() with argument autoburnin = FALSE unless specified otherwise via ...
when inits is specified as a function, the function is evaluated and the resulting list passed to JAGS (previously the function was passed to JAGS)
the example data simong and simWide have changed (more variables, less subjects)
added check if there are incomplete covariates before setting imp_pars = TRUE (when user specified via monitor_params or subset)
in survreg_imp the sign of the regression coefficient is now opposite to match the one from survreg

JointAI 0.5.0

Important

the argument meth has changed to models

Bug fixes

add_samples(): bug that copied the last chain to all other chains fixed
bug-fix for the order of columns in the matrix Xc, so that specification of functions of covariates in auxiliary variables works better
adding vertical lines to a densplot() issue (all plots showed all lines) fixed
nested functions involving powers made possible
typo causing issue in poisson glm and glme removed

Minor changes

plot_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)
change in longDF example data: new version containing complete and incomplete categorical longitudinal variables (and variable names L1 and L2 changed to c1 and c2)
Some minor changes in notes, warnings and error messages
The function list_impmodels() changed to list_models() (but list_impmodels() is kept as an alias for now)
improved handling of functional forms of covariates (also in longitudinal covariates and random effects)

New Features / Extensions

clm_imp() and clmm_imp(): new functions for analysis of ordinal (mixed) models
It is now possible to impute incomplete longitudinal covariates (continuous, binary and ordered factors).
coxph_imp(): new function to fit Cox proportional hazards models with incomplete (baseline) covariates
Argument no_model allows to specify names of completely observed variables for which no model should be specified (e.g., “time” in a mixed model)
Shrinkage: argument ridge = TRUE allows to use shrinkage priors on the precision of the regression coefficients in the analysis model
plot_all() can now handle variables from classes Date and POSIXt
new argument parallel allows different MCMC chains to be sampled in parallel
new argument ncores allows to specify the maximum number of cores to be used
new argument seed 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 values

JointAI 0.4.0

Bug fixes

RinvD is no longer selected to be monitored in random intercept model (RinvD is not used in such a model)
fixed various bugs for models in which only the intercept is used (no covariates)

Minor changes

summary(): reduced default number of digits
continuous variables with two distinct values are converted to factor
argument meth now uses default values if only specified for subset of incomplete variables
get_MIdat(): argument minspace added to ensure spacing of iterations selected as imputations
densplot(): accepts additional options, e.g., lwd, col, …
list_models() replaces the function list_impmodels() (which is now an alias)

Extensions

coef() method added for JointAI object and summary.JointAI object
confint() method added for JointAI object
print() method added for JointAI object
survreg_imp() added to perform analysis of parametric (Weibull) survival models
glme_imp() added to perform generalized linear mixed modeling
extended documentation; two new vignettes on MCMC parameters and functions for after the model is estimated; added messages about coding of ordinal variables

JointAI 0.3.4

Bug fixes

traceplot(), densplot(): specification of nrow AND ncol possible; fixed bug when only nrow specified

JointAI 0.3.3

Bug fixes

remove deprecated code specifying contrast.arg that now in some cases cause error
fixed problem identifying non-linear functions in formula when the name of another variable contains the function name

# 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

JointAI 0.3.1

Bug fixes

plot_all() uses correct level-2 %NA in title
simWide: case with no observed bmi values removed
traceplot(), densplot(): ncol and nrow now work with use_ggplot = TRUE
traceplot(), densplot(): error in specification of nrow fixed
densplot(): use of color fixed
functions with argument subset now return random effects covariance matrix correctly
summary() displays output with rowname when only one node is returned and fixed display of D matrix
GR_crit(): Literature reference corrected
predict(): prediction with varying factor fixed
no scaling for variables involved in a function to avoid problems with re-scaling

Minor changes

plot_all() uses xpd = TRUE when printing text for character variables
list_impmodels() uses linebreak when output of predictor variables exceeds getOption("width")
summary() now displays tail-probabilities for off-diagonal elements of D
added option to show/hide constant effects of auxiliary variables in plots
predict(): 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 hyperparameters * 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

JointAI 0.2.0

Bug fixes

md_pattern(): does not generate duplicate plot any more
corrected names of imputation methods in help file
scaling when no continuous covariates are in the model or scaling is deselected fixed
initial value specification for coefficient for auxiliary variables fixed
get_MIdat(): imputed values are now filled in in the correct order
get_MIdat(): variables imputed with lognorm are now included when extracting an imputed dataset
get_MIdat(): imputed values of transformed variables are now included in imputed datasets
problem with non valid names of factor labels fixed
data matrix is now ordered according to order in user-specified meth argument

Minor changes

md.pattern(): adaptation to new version of md.pattern() from the mice package
internally change all NaN to NA
allow for scaling of incomplete covariates with quadratic effects
changed hyperparameter for precision in models with logit link from 4/9 to 0.001

Extensions

gamma and beta imputation methods implemented