Package: mdmb 1.10-1
mdmb: Model Based Treatment of Missing Data
Contains model-based treatment of missing data for regression models with missing values in covariates or the dependent variable using maximum likelihood or Bayesian estimation (Ibrahim et al., 2005; <doi:10.1198/016214504000001844>; Luedtke, Robitzsch, & West, 2020a, 2020b; <doi:10.1080/00273171.2019.1640104><doi:10.1037/met0000233>). The regression model can be nonlinear (e.g., interaction effects, quadratic effects or B-spline functions). Multilevel models with missing data in predictors are available for Bayesian estimation. Substantive-model compatible multiple imputation can be also conducted.
Authors:
mdmb_1.10-1.tar.gz
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mdmb.pdf |mdmb.html✨
mdmb/json (API)
NEWS
| # Install 'mdmb' in R: |
| install.packages('mdmb', repos = c('https://alexanderrobitzsch.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/alexanderrobitzsch/mdmb/issues
missing-datamultiple-imputation
Last updated 11 days agofrom:809cbf6e2a
Exports:bc_antitrafobc_trafobct_regressiondbct_scaleddoprobitdt_scaleddyjt_scaledeval_prior_listeval_prior_list_sumlogfit_bct_scaledfit_oprobitfit_t_scaledfit_yjt_scaledfrm_emfrm_fbfrm2datlistlogistic_regressionoffset_values_extractoprobit_regressionrbct_scaledremove_NA_data_framert_scaledryjt_scaledyj_antitrafoyj_trafoyjt_regression
Dependencies:admiscbackportsbitbit64bootbroomCDMclicliprcodacodetoolscpp11crayonDBIdplyrfansiforcatsforeachgenericsglmnetgluehavenhmsiteratorsjomolatticelifecyclelme4magrittrMASSMatrixmicemiceaddsminqamitmlmitoolsmvtnormnlmenloptrnnetnumDerivordinalpanpbapplypbvpillarpkgconfigpolycorprettyunitsprogresspurrrR6RcppRcppArmadilloRcppEigenreadrrlangrpartshapesirtstringistringrsurvivalTAMtibbletidyrtidyselecttzdbucminfutf8vctrsvroomwithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Model Based Treatment of Missing Data | mdmb-package mdmb |
| Example Datasets for 'mdmb' Package | data.mb data.mb01 data.mb02 data.mb03 data.mb04 data.mb05 |
| Evaluates Several Prior Distributions | eval_prior_list eval_prior_list_sumlog |
| Factored Regression Model: Generalized Linear Regression Model with Missing Covariates | coef.frm_em coef.frm_fb frm frm2datlist frm_em frm_fb logLik.frm_em plot.frm_fb summary.frm_em summary.frm_fb vcov.frm_em vcov.frm_fb |
| Several Regression Models with Prior Distributions and Sampling Weights | bct_regression coef.bct_regression coef.logistic_regression coef.oprobit_regression coef.yjt_regression logistic_regression logLik.bct_regression logLik.logistic_regression logLik.oprobit_regression logLik.yjt_regression oprobit_regression predict.bct_regression predict.logistic_regression predict.oprobit_regression predict.yjt_regression summary.bct_regression summary.logistic_regression summary.oprobit_regression summary.yjt_regression vcov.bct_regression vcov.logistic_regression vcov.oprobit_regression vcov.yjt_regression yjt_regression |
| Extracts Offset Values | offset_values_extract |
| Ordinal Probit Models | coef.fit_oprobit doprobit fit_oprobit logLik.fit_oprobit oprobit_dist summary.fit_oprobit vcov.fit_oprobit |
| Removes Rows with Some Missing Entries in a Data Frame | remove_NA_data_frame |
| Scaled t Distribution with Yeo-Johnson and Box-Cox Transformations | bc_antitrafo bc_trafo coef.fit_bct_scaled coef.fit_t_scaled coef.fit_yjt_scaled dbct_scaled dt_scaled dyjt_scaled fit_bct_scaled fit_t_scaled fit_yjt_scaled logLik.fit_bct_scaled logLik.fit_t_scaled logLik.fit_yjt_scaled rbct_scaled rt_scaled ryjt_scaled summary.fit_bct_scaled summary.fit_t_scaled summary.fit_yjt_scaled vcov.fit_bct_scaled vcov.fit_t_scaled vcov.fit_yjt_scaled yjt_dist yj_antitrafo yj_trafo |
