The multimput package | Very short intro to multiple imputation | Short intro to multiple imputation | The dataset | Create the imputation model | Apply the imputation model | Aggregate the imputed dataset | Model the aggregated imputed dataset | Simple example | Return only the parameters associated with fYear | Predict a smoother for predefined values | Compare the results using different imputation models | Modelling aggregated data with glm.nb | Modelling aggregated data with inla | References
This package is considered a duplicate. The official version of this package is found at:https://inbo.r-universe.dev/multimput
Package: multimput 0.2.15
multimput: Using Multiple Imputation to Address Missing Data
Accompanying package for the paper: Working with population totals in the presence of missing data comparing imputation methods in terms of bias and precision. Published in 2017 in the Journal of Ornithology volume 158 page 603–615 (<doi:10.1007/s10336-016-1404-9>).
Authors:
multimput_0.2.15.tar.gz
multimput_0.2.15.zip(r-4.7)multimput_0.2.15.zip(r-4.6)multimput_0.2.15.zip(r-4.5)
multimput_0.2.15.tgz(r-4.6-any)multimput_0.2.15.tgz(r-4.5-any)
multimput_0.2.15.tar.gz(r-4.7-any)multimput_0.2.15.tar.gz(r-4.6-any)
multimput_0.2.15.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
multimput/json (API)
| # Install 'multimput' in R: |
| install.packages('multimput', repos = c('https://thierryo.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/inbo/multimput/issues
Pkgdown/docs site:https://inbo.github.io
Datasets:
- waterfowl - The observation pattern in the Flemish waterfowl dataset
Last updated from:654e208239. Checks:6 OK, 1 ERROR, 2 FAIL. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 247 | ||
| source / vignettes | OK | 257 | ||
| linux-release-x86_64 | OK | 250 | ||
| macos-release-arm64 | ERROR | 118 | ||
| macos-oldrel-arm64 | OK | 258 | ||
| windows-devel | FAIL | 824 | ||
| windows-release | FAIL | 853 | ||
| windows-oldrel | OK | 292 | ||
| wasm-release | OK | 143 |
Exports:aggregate_imputegenerate_datahurdle_imputeimputemissing_at_randommissing_current_countmissing_observedmissing_volunteermodel_impute
Dependencies:assertthatbootclassclassIntclicpp11DBIdigestdplyre1071fmeshergenericsglueINLAKernSmoothlatticelifecyclelme4magrittrMASSMatrixMatrixModelsminqamvtnormnlmenloptrpillarpkgconfigproxypurrrR6rbibutilsRcppRcppEigenRdpackreformulasrlangs2sfspsplancsstringistringrtibbletidyrtidyselectunitsutf8vctrswithrwk
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Aggregate an imputed dataset | aggregate_impute aggregate_impute,aggregatedImputed-method aggregate_impute,ANY-method aggregate_impute,rawImputed-method |
| The 'aggregatedImputed' class Holds an aggregated imputation data set | aggregatedImputed-class |
| Generate simulated data | generate_data |
| Combine two models into a hurdle model | hurdle_impute |
| Impute a dataset | impute impute,ANY-method impute,glmerMod-method impute,lm-method impute,maybeInla-method |
| The 'maybeInla' class | maybeInla-class |
| Generate missing data at random | missing_at_random |
| Generate missing data depending on the counts | missing_current_count |
| Generate missing data based on the observed patterns in the real dataset. | missing_observed |
| Generate missing data mimicking choices made by volunteers. | missing_volunteer |
| Model an imputed dataset | model_impute model_impute,aggregatedImputed-method model_impute,ANY-method |
| The 'rawImputed' class Holds a dataset and imputed values | rawImputed-class |
| The observation pattern in the Flemish waterfowl dataset | waterfowl |
