Man pages for ThierryO/multimput
Using Multiple Imputation to Address Missing Data
| aggregatedImputed-class | The aggregatedimputed class Holds an aggregated imputation... |
| aggregate_impute | Aggregate an imputed dataset |
| example | The indices for the example dataset |
| generateData | Generate simulated data |
| impute | Impute a dataset |
| imputedTotals | Calculate the totals over an imputed dataset |
| imputeINLA | impute missing data using multiple imputation |
| imputeINLAfit | impute missing data using the predicted values |
| imputeTruth | impute missing data using multiple imputation |
| imputeUnderhill | impute missing data |
| imputeUnderhillAltered | impute missing data using an alterned Underhill method |
| missingAtRandom | Generate missing data at random |
| missingCurrentCount | Generate missing data depending on the counts |
| missingObserved | Generate missing data based on the observed patterns in the... |
| missingVolunteer | Generate missing data mimicschoices made by volunteers. |
| model_impute | Model an imputed dataset |
| rawImputed-class | The rawimputed class Holds a dataset and imputed values |
| results.inla | The Monte Carlo simualtion results using INLA |
| results.trim | The Monte Carlo simualtion results using birdSTATs and TRIM |
| results.truth | The Monte Carlo simualtion results using the complete data |
| results.underhill | The Monte Carlo simualtion results using Underhill |
| summarizeImputationGLM | summarize the imputed dataset with a glm model |
| summarizeImputationGLM.nb | summarize the imputed dataset with a glm model |
| waterfowl | The observation pattern in the Flemish waterfowl dataset |
