miceadds v3.2-48
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Some Additional Multiple Imputation Functions, Especially for 'mice'
Contains functions for multiple imputation which
complements existing functionality in R.
In particular, several imputation methods for the
mice package (van Buuren & Groothuis-Oudshoorn, 2011,
<doi:10.18637/jss.v045.i03>) are included.
Main features of the miceadds package include
plausible value imputation (Mislevy, 1991,
<doi:10.1007/BF02294457>), multilevel imputation for
variables at any level or with any number of hierarchical
and non-hierarchical levels (Grund, Luedtke & Robitzsch,
2018, <doi:10.1177/1094428117703686>; van Buuren, 2018,
Ch.7, <doi:10.1201/9780429492259>), imputation using
partial least squares (PLS) for high dimensional
predictors (Robitzsch, Pham & Yanagida, 2016),
nested multiple imputation (Rubin, 2003,
<doi:10.1111/1467-9574.00217>) and substantive model
compatible imputation (Bartlett et al., 2015,
<doi:10.1177/0962280214521348>).
Readme
miceadds
Some Additional Multiple Imputation Functions, Especially for 'mice'
If you use miceadds and have suggestions for improvement or have found bugs, please email me at robitzsch@ipn.uni-kiel.de.
CRAN version
!--- [](https://cran.r-project.org/package=miceadds) --The official version of miceadds is hosted on CRAN and may be found here.
The CRAN version can be installed from within R using:
utils::install.packages("miceadds")
GitHub version
The version hosted here is the development version of miceadds.
The GitHub version can be installed using devtools as:
devtools::install_github("alexanderrobitzsch/miceadds")
Functions in miceadds
| Name | Description | |
| complete.miceadds | Creates Imputed Dataset from a mids.nmi or mids.1chain Object | |
| crlrem | R Utilities: Removing CF Line Endings | |
| Rsessinfo | R Utilities: R Session Information | |
| VariableNames2String | Stringing Variable Names with Line Breaks | |
| files_move | Moves Files from One Directory to Another Directory | |
| fleishman_sim | Simulating Univariate Data from Fleishman Power Normal Transformations | |
| ma_lme4_formula | Utility Functions for Working with lme4 Formula Objects | |
| ma_rmvnorm | Simulating Normally Distributed Data | |
| data.internet | Dataset Internet | |
| data.largescale | Large-scale Dataset for Testing Purposes (Many Cases, Few Variables) | |
| ma.scale2 | Standardization of a Matrix | |
| mice.impute.bygroup | Groupwise Imputation Function | |
| mice.impute.grouped | Imputation of a Variable with Grouped Values | |
| mice.nmi | Nested Multiple Imputation | |
| ma.wtd.statNA | Some Multivariate Descriptive Statistics for Weighted Data in miceadds | |
| mice.1chain | Multiple Imputation by Chained Equations using One Chain | |
| NestedImputationList | Functions for Analysis of Nested Multiply Imputed Datasets | |
| Reval | R Utilities: Evaluates a String as an Expression in R | |
| mice_imputation_2l_lmer | Imputation of a Continuous or a Binary Variable From a Two-Level Regression Model using lme4 or blme | |
| save.data | R Utilities: Saving/Writing Data Files using miceadds | |
| mice.impute.2l.contextual.pmm | Imputation by Predictive Mean Matching or Normal Linear Regression with Contextual Variables | |
| data.ma | Example Datasets for miceadds Package | |
| mice.impute.pmm3 | Imputation by Predictive Mean Matching (in miceadds) | |
| subset_datlist | Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets | |
| scale_datlist | Adding a Standardized Variable to a List of Multiply Imputed Datasets or a Single Datasets | |
| sumpreserving.rounding | Sum Preserving Rounding | |
| mice.impute.smcfcs | Substantive Model Compatible Multiple Imputation (Single Level) | |
| data.smallscale | Small-Scale Dataset for Testing Purposes (Moderate Number of Cases, Many Variables) | |
| kernelpls.fit2 | Kernel PLS Regression | |
| GroupMean | Calculation of Groupwise Descriptive Statistics for Matrices | |
| library_install | R Utilities: Loading a Package or Installation of a Package if Necessary | |
| mi.anova | Analysis of Variance for Multiply Imputed Data Sets (Using the \(D_2\) Statistic) | |
| mi_dstat | Cohen's d Effect Size for Missingness Indicators | |
| stats0 | Descriptive Statistics for a Vector or a Data Frame | |
| str_C.expand.grid | R Utilities: String Paste Combined with expand.grid | |
| ml_mcmc | MCMC Estimation for Mixed Effects Model | |
| mids2mlwin | Export mids object to MLwiN | |
| mice.impute.plausible.values | Plausible Value Imputation using Classical Test Theory and Based on Individual Likelihood | |
| NMIwaldtest | Wald Test for Nested Multiply Imputed Datasets | |
| cxxfunction.copy | R Utilities: Copy of an Rcpp File | |
| Rfunction_include_argument_values | Utility Functions for Writing R Functions | |
| data.allison | Datasets from Allison's Missing Data Book | |
| mice.impute.pls | Imputation using Partial Least Squares for Dimension Reduction | |
| grep.vec | R Utilities: Vector Based Versions of grep | |
| Rhat.mice | Rhat Convergence Statistic of a mice Imputation | |
| write.datlist | Write a List of Multiply Imputed Datasets | |
| in_CI | Indicator Function for Analyzing Coverage | |
| write.fwf2 | Reading and Writing Files in Fixed Width Format | |
| mice.impute.2l.latentgroupmean.ml | Imputation of Latent and Manifest Group Means for Multilevel Data | |
| mice.impute.2lonly.function | Imputation at Level 2 (in miceadds) | |
| data.enders | Datasets from Enders' Missing Data Book | |
| datlist2mids | Converting a List of Multiply Imputed Data Sets into a mids Object | |
| mice.impute.tricube.pmm | Imputation by Tricube Predictive Mean Matching | |
| micombine.F | Combination of F Statistics for Multiply Imputed Datasets Using a Chi Square Approximation | |
| micombine.chisquare | Combination of Chi Square Statistics of Multiply Imputed Datasets | |
| data.graham | Datasets from Grahams Missing Data Book | |
| mice.impute.weighted.pmm | Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression | |
| datlist_create | Creates Objects of Class datlist or nested.datlist | |
| round2 | R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden) | |
| save.Rdata | R Utilities: Save a Data Frame in Rdata Format | |
| write.mice.imputation | Export Multiply Imputed Datasets from a mids Object | |
| write.pspp | Writing a Data Frame into SPSS Format Using PSPP Software | |
| index.dataframe | R Utilities: Include an Index to a Data Frame | |
| filename_split | Some Functionality for Strings and File Names | |
| lm.cluster | Cluster Robust Standard Errors for Linear Models and General Linear Models | |
| draw.pv.ctt | Plausible Value Imputation Using a Known Measurement Error Variance (Based on Classical Test Theory) | |
| output.format1 | R Utilities: Formatting R Output on the R Console | |
| jomo2datlist | Converts a jomo Data Frame in Long Format into a List of Datasets or an Object of Class mids | |
| pca.covridge | Principal Component Analysis with Ridge Regularization | |
| visitSequence.determine | Automatic Determination of a Visit Sequence in mice | |
| load.Rdata | R Utilities: Loading Rdata Files in a Convenient Way | |
| with.miceadds | Evaluates an Expression for (Nested) Multiply Imputed Datasets | |
| load.data | R Utilities: Loading/Reading Data Files using miceadds | |
| lmer_vcov | Statistical Inference for Fixed and Random Structure for Fitted Models in lme4 | |
| mice.impute.hotDeck | Imputation of a Variable Using Probabilistic Hot Deck Imputation | |
| mice_inits | Arguments for mice::mice Function | |
| mids2datlist | Converting a mids, mids.1chain or mids.nmi Object in a Dataset List | |
| micombine.cor | Inference for Correlations and Covariances for Multiply Imputed Datasets | |
| miceadds-defunct | Defunct miceadds Functions | |
| scan.vec | R Utilities: Scan a Character Vector | |
| source.all | R Utilities: Source all R or Rcpp Files within a Directory | |
| mice.impute.ml.lmer | Multilevel Imputation Using lme4 | |
| miceadds-utilities | Utility Functions in miceadds | |
| miceadds-package | miceadds | |
| nestedList2List | Converting a Nested List into a List (and Vice Versa) | |
| nnig_sim | Simulation of Multivariate Linearly Related Non-Normal Variables | |
| pool_mi | Statistical Inference for Multiply Imputed Datasets | |
| pool.mids.nmi | Pooling for Nested Multiple Imputation | |
| systime | R Utilities: Various Strings Representing System Time | |
| tw.imputation | Two-Way Imputation | |
| No Results! | ||
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Details
| Type | Package |
| Date | 2019-04-15 18:10:29 |
| LinkingTo | Rcpp, RcppArmadillo |
| URL | https://github.com/alexanderrobitzsch/miceadds, https://sites.google.com/site/alexanderrobitzsch2/software |
| License | GPL (>= 2) |
| BugReports | https://github.com/alexanderrobitzsch/miceadds/issues?state=open |
| NeedsCompilation | yes |
| Packaged | 2019-04-15 16:13:34 UTC; sunpn563 |
| Repository | CRAN |
| Date/Publication | 2019-04-15 18:22:38 UTC |
| enhances | Amelia , jomo , micemd , mitml , pan |
| suggests | BIFIEsurvey , blme , car , coda , foreign , grouped , inline , MASS , MBESS , MCMCglmm , mdmb , multiwayvcov , numDeriv , readxl , sjlabelled |
| imports | CDM , graphics , lme4 , methods , mitools , Rcpp , sirt , stats , TAM , utils |
| depends | mice (>= 3.0.0) , R (>= 3.0) |
| linkingto | RcppArmadillo |
| Contributors | Alexander Robitzsch, Simon Grund, Thorsten Henke |
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