Main functions
JointAI provides the following main functions:
lm_imp() # linear regression glm_imp() # generalized linear regression clm_imp() # cumulative logit model mlogit_imp() # multinomial logit model lognorm_imp() # log-normal regression betareg_imp() # beta regression lme_imp() / lmer_imp() # linear mixed model glme_imp() / glmer_imp() # generalized linear mixed model clmm_imp() # cumulative logit mixed model mlogitmm_imp() # multinomial logit model lognormmm_imp() # log-normal regression betamm_imp() # beta regression survreg_imp() # parametric (Weibull) survival model coxph_imp() # proportional hazards survival model JM_imp() # joint model for longitudinal and survival data
The functions use specification similar to that of well known standard functions like lm() and glm() from base R, nlme::lme() (from the package nlme) , lme4::lmer() or lme4::glmer() (from the package lme4) and survival::survreg() and survival::coxph() (from the package survival).
Functions summary(), coef(), traceplot() and densplot() provide a summary of the posterior distribution and its visualization.
GR_crit() and MC_error() implement the Gelman-Rubin diagnostic for convergence and the Monte Carlo error of the MCMC sample, respectively.
JointAI also provides functions for exploration of the distribution of the data and missing values, export of imputed values and prediction.
