| imp |
Multiple Iterative Regression Imputation |
| imp-method |
Multiple Iterative Regression Imputation |
| imp.mi |
Multiple Iterative Regression Imputation |
| imp.mi-method |
Multiple Iterative Regression Imputation |
| imputed |
Virtual class for all mi classes. |
| imputed-method |
Virtual class for all mi classes. |
| info.mi |
Multiple Iterative Regression Imputation |
| info.mi-method |
Multiple Iterative Regression Imputation |
| is.mi |
Multiple Iterative Regression Imputation |
| is.mi-method |
Multiple Iterative Regression Imputation |
| is.mi.info |
Function to create information matrix for missing data imputation |
| m |
Multiple Iterative Regression Imputation |
| m-method |
Multiple Iterative Regression Imputation |
| marginal.scatterplot |
Multiple Imputation Scatterplot |
| mi |
Multiple Iterative Regression Imputation |
| mi-class |
Multiple Iterative Regression Imputation |
| mi-method |
Multiple Iterative Regression Imputation |
| mi.binary |
Elementary function: Bayesian logistic regression to impute a binary variable. |
| mi.binary-class |
Elementary function: Bayesian logistic regression to impute a binary variable. |
| mi.categorical |
Elementary function: multinomial log-linear models to impute a categorical variable. |
| mi.categorical-class |
Elementary function: multinomial log-linear models to impute a categorical variable. |
| mi.completed |
Multiply Imputed Dataframes |
| mi.completed-method |
Multiply Imputed Dataframes |
| mi.continuous |
Elementary function: linear regression to impute a continuous variable. |
| mi.continuous-class |
Elementary function: linear regression to impute a continuous variable. |
| mi.copy |
Elementary function: imputation of constant variable. |
| mi.copy-class |
Elementary function: imputation of constant variable. |
| mi.count |
Elementary function: Bayesian overdispersed poisson regression to impute a count variable. |
| mi.count-class |
Elementary function: Bayesian overdispersed poisson regression to impute a count variable. |
| mi.data.frame |
Multiply Imputed Dataframes |
| mi.data.frame-method |
Multiply Imputed Dataframes |
| mi.fixed |
Elementary function: imputation of constant variable. |
| mi.fixed-class |
Elementary function: imputation of constant variable. |
| mi.hist |
Multiple Imputation Histogram |
| mi.hist-method |
Multiple Imputation Histogram |
| mi.info |
Function to create information matrix for missing data imputation |
| mi.info-class |
Function to create information matrix for missing data imputation |
| mi.info.fix |
Function to create information matrix for missing data imputation |
| mi.info.update |
function to update mi.info object to use for multiple imputation |
| mi.info.update.collinear |
function to update mi.info object to use for multiple imputation |
| mi.info.update.determ.pred |
function to update mi.info object to use for multiple imputation |
| mi.info.update.imp.formula |
function to update mi.info object to use for multiple imputation |
| mi.info.update.imp.order |
function to update mi.info object to use for multiple imputation |
| mi.info.update.include |
function to update mi.info object to use for multiple imputation |
| mi.info.update.is.ID |
function to update mi.info object to use for multiple imputation |
| mi.info.update.level |
function to update mi.info object to use for multiple imputation |
| mi.info.update.other |
function to update mi.info object to use for multiple imputation |
| mi.info.update.params |
function to update mi.info object to use for multiple imputation |
| mi.info.update.type |
function to update mi.info object to use for multiple imputation |
| mi.interactive |
Function to create information matrix for missing data imputation |
| mi.method |
Virtual class for all mi classes. |
| mi.method-class |
Virtual class for all mi classes. |
| mi.pmm |
Elementary function: Probability Mean Matching for imputation. |
| mi.pmm-class |
Elementary function: Probability Mean Matching for imputation. |
| mi.polr |
Elementary function: multinomial log-linear models to impute a ordered categorical variable. |
| mi.polr-class |
Elementary function: multinomial log-linear models to impute a ordered categorical variable. |
| mi.pooled |
Modeling Functions for Multiply Imputed Dataset |
| mi.pooled-class |
Modeling Functions for Multiply Imputed Dataset |
| mi.postprocess |
Preproessing and Postprocessing mi data object |
| mi.preprocess |
Preproessing and Postprocessing mi data object |
| mi.scatterplot |
Multiple Imputation Scatterplot |
| mi.types |
Functions to identify types of the models of the mi object |
| missing.pattern.plot |
Missing Pattern Plot |
| mp.plot |
Missing Pattern Plot |
| plot |
Diagnostic Plots for multiple imputation object |
| plot-method |
Elementary function: Bayesian logistic regression to impute a binary variable. |
| plot-method |
Elementary function: multinomial log-linear models to impute a categorical variable. |
| plot-method |
Virtual class for all mi classes. |
| plot-method |
Elementary function: multinomial log-linear models to impute a ordered categorical variable. |
| plot-method |
Diagnostic Plots for multiple imputation object |
| plot.mi |
Diagnostic Plots for multiple imputation object |
| polr.mi |
Modeling Functions for Multiply Imputed Dataset |
| print-method |
Multiple Iterative Regression Imputation |
| print-method |
Function to create information matrix for missing data imputation |
| print-method |
Virtual class for all mi classes. |
| print.mi.pooled |
Modeling Functions for Multiply Imputed Dataset |
| random.imp |
Random Imputation of Missing Data |
| resid-method |
Elementary function: Bayesian logistic regression to impute a binary variable. |
| resid-method |
Elementary function: multinomial log-linear models to impute a categorical variable. |
| resid-method |
Elementary function: Bayesian overdispersed poisson regression to impute a count variable. |
| resid-method |
Virtual class for all mi classes. |
| resid-method |
Elementary function: multinomial log-linear models to impute a ordered categorical variable. |
| residuals-method |
Elementary function: Bayesian logistic regression to impute a binary variable. |
| residuals-method |
Elementary function: multinomial log-linear models to impute a categorical variable. |
| residuals-method |
Elementary function: Bayesian overdispersed poisson regression to impute a count variable. |
| residuals-method |
Virtual class for all mi classes. |
| residuals-method |
Elementary function: multinomial log-linear models to impute a ordered categorical variable. |