R: Some Additional Multiple Imputation Functions, Especially for 'mice'

# Some Additional Multiple Imputation Functions, Especially for 'mice'

## Documentation for package ‘miceadds’ version 3.13-12

- DESCRIPTION file.
- Package NEWS.

## Help Pages

C D F G I J K L M N O P Q R S T V W

miceadds-package | Some Additional Multiple Imputation Functions, Especially for 'mice'

## -- C --

coef.glm.cluster | Cluster Robust Standard Errors for Linear Models and General Linear Models
coef.lm.cluster | Cluster Robust Standard Errors for Linear Models and General Linear Models
coef.lmer_vcov | Statistical Inference for Fixed and Random Structure for Fitted Models in 'lme4'
coef.mipo.nmi | Pooling for Nested Multiple Imputation
coef.ml_mcmc | MCMC Estimation for Mixed Effects Model
coef.pool_mi | Statistical Inference for Multiply Imputed Datasets
complete.mids.1chain | Creates Imputed Dataset from a 'mids.nmi' or 'mids.1chain' Object
complete.mids.nmi | Creates Imputed Dataset from a 'mids.nmi' or 'mids.1chain' Object
create.designMatrices.waldtest | Wald Test for Nested Multiply Imputed Datasets
crlrem | R Utilities: Removing CF Line Endings
cwc | Calculation of Groupwise Descriptive Statistics for Matrices
cxxfunction.copy | R Utilities: Copy of an 'Rcpp' File

## -- D --

data.allison | Datasets from Allison's _Missing Data_ Book
data.allison.gssexp | Datasets from Allison's _Missing Data_ Book
data.allison.hip | Datasets from Allison's _Missing Data_ Book
data.allison.usnews | Datasets from Allison's _Missing Data_ Book
data.enders | Datasets from Enders' _Missing Data_ Book
data.enders.depression | Datasets from Enders' _Missing Data_ Book
data.enders.eatingattitudes | Datasets from Enders' _Missing Data_ Book
data.enders.employee | Datasets from Enders' _Missing Data_ Book
data.graham | Datasets from Grahams _Missing Data_ Book
data.graham.ex3 | Datasets from Grahams _Missing Data_ Book
data.graham.ex6 | Datasets from Grahams _Missing Data_ Book
data.graham.ex8a | Datasets from Grahams _Missing Data_ Book
data.graham.ex8b | Datasets from Grahams _Missing Data_ Book
data.graham.ex8c | Datasets from Grahams _Missing Data_ Book
data.internet | Dataset Internet
data.largescale | Large-scale Dataset for Testing Purposes (Many Cases, Few Variables)
data.ma | Example Datasets for 'miceadds' Package
data.ma01 | Example Datasets for 'miceadds' Package
data.ma02 | Example Datasets for 'miceadds' Package
data.ma03 | Example Datasets for 'miceadds' Package
data.ma04 | Example Datasets for 'miceadds' Package
data.ma05 | Example Datasets for 'miceadds' Package
data.ma06 | Example Datasets for 'miceadds' Package
data.ma07 | Example Datasets for 'miceadds' Package
data.ma08 | Example Datasets for 'miceadds' Package
data.smallscale | Small-Scale Dataset for Testing Purposes (Moderate Number of Cases, Many Variables)
datalist2mids | Converting a List of Multiply Imputed Data Sets into a 'mids' Object
datlist2mids | Converting a List of Multiply Imputed Data Sets into a 'mids' Object
datlist2nested.datlist | Creates Objects of Class 'datlist' or 'nested.datlist'
datlist_create | Creates Objects of Class 'datlist' or 'nested.datlist'
draw.pv.ctt | Plausible Value Imputation Using a Known Measurement Error Variance (Based on Classical Test Theory)

## -- F --

fast.groupmean | Defunct 'miceadds' Functions
fast.groupsum | Defunct 'miceadds' Functions
filename_split | Some Functionality for Strings and File Names
filename_split_vec | Some Functionality for Strings and File Names
files_move | Moves Files from One Directory to Another Directory
fleishman_coef | Simulating Univariate Data from Fleishman Power Normal Transformations
fleishman_sim | Simulating Univariate Data from Fleishman Power Normal Transformations

## -- G --

glm.cluster | Cluster Robust Standard Errors for Linear Models and General Linear Models
gm | Calculation of Groupwise Descriptive Statistics for Matrices
grep.vec | R Utilities: Vector Based Versions of 'grep'
grepvec | R Utilities: Vector Based Versions of 'grep'
grepvec_leading | R Utilities: Vector Based Versions of 'grep'
grep_leading | R Utilities: Vector Based Versions of 'grep'
GroupMean | Calculation of Groupwise Descriptive Statistics for Matrices
GroupSD | Calculation of Groupwise Descriptive Statistics for Matrices
GroupSum | Calculation of Groupwise Descriptive Statistics for Matrices

## -- I --

index.dataframe | R Utilities: Include an Index to a Data Frame
in_CI | Indicator Function for Analyzing Coverage

## -- J --

jomo2datlist | Converts a 'jomo' Data Frame in Long Format into a List of Datasets or an Object of Class 'mids'
jomo2mids | Converts a 'jomo' Data Frame in Long Format into a List of Datasets or an Object of Class 'mids'

## -- K --

kernelpls.fit2 | Kernel PLS Regression

## -- L --

library_install | R Utilities: Loading a Package or Installation of a Package if Necessary
List2nestedList | Converting a Nested List into a List (and Vice Versa)
lm.cluster | Cluster Robust Standard Errors for Linear Models and General Linear Models
lmer_pool | Statistical Inference for Fixed and Random Structure for Fitted Models in 'lme4'
lmer_pool2 | Statistical Inference for Fixed and Random Structure for Fitted Models in 'lme4'
lmer_vcov | Statistical Inference for Fixed and Random Structure for Fitted Models in 'lme4'
lmer_vcov2 | Statistical Inference for Fixed and Random Structure for Fitted Models in 'lme4'
load.data | R Utilities: Loading/Reading Data Files using 'miceadds'
load.files | R Utilities: Loading/Reading Data Files using 'miceadds'
load.Rdata | R Utilities: Loading 'Rdata' Files in a Convenient Way
load.Rdata2 | R Utilities: Loading 'Rdata' Files in a Convenient Way

## -- M --

ma.scale2 | Standardization of a Matrix
ma.wtd.corNA | Some Multivariate Descriptive Statistics for Weighted Data in 'miceadds'
ma.wtd.covNA | Some Multivariate Descriptive Statistics for Weighted Data in 'miceadds'
ma.wtd.kurtosisNA | Some Multivariate Descriptive Statistics for Weighted Data in 'miceadds'
ma.wtd.meanNA | Some Multivariate Descriptive Statistics for Weighted Data in 'miceadds'
ma.wtd.quantileNA | Some Multivariate Descriptive Statistics for Weighted Data in 'miceadds'
ma.wtd.sdNA | Some Multivariate Descriptive Statistics for Weighted Data in 'miceadds'
ma.wtd.skewnessNA | Some Multivariate Descriptive Statistics for Weighted Data in 'miceadds'
ma.wtd.statNA | Some Multivariate Descriptive Statistics for Weighted Data in 'miceadds'
max0 | Descriptive Statistics for a Vector or a Data Frame
ma_exists | Utility Functions in 'miceadds'
ma_exists_get | Utility Functions in 'miceadds'
ma_lme4_formula | Utility Functions for Working with 'lme4' Formula Objects
ma_lme4_formula_design_matrices | Utility Functions for Working with 'lme4' Formula Objects
ma_lme4_formula_terms | Utility Functions for Working with 'lme4' Formula Objects
ma_rmvnorm | Simulating Normally Distributed Data
mean0 | Descriptive Statistics for a Vector or a Data Frame
mi.anova | Analysis of Variance for Multiply Imputed Data Sets (Using the D_2 Statistic)
mice.1chain | Multiple Imputation by Chained Equations using One Chain
mice.impute.2l.binary | Imputation of a Continuous or a Binary Variable From a Two-Level Regression Model using 'lme4' or 'blme'
mice.impute.2l.contextual.norm | Imputation by Predictive Mean Matching or Normal Linear Regression with Contextual Variables
mice.impute.2l.contextual.pmm | Imputation by Predictive Mean Matching or Normal Linear Regression with Contextual Variables
mice.impute.2l.continuous | Imputation of a Continuous or a Binary Variable From a Two-Level Regression Model using 'lme4' or 'blme'
mice.impute.2l.groupmean | Imputation of Latent and Manifest Group Means for Multilevel Data
mice.impute.2l.groupmean.elim | Imputation of Latent and Manifest Group Means for Multilevel Data
mice.impute.2l.latentgroupmean.mcmc | Imputation of Latent and Manifest Group Means for Multilevel Data
mice.impute.2l.latentgroupmean.ml | Imputation of Latent and Manifest Group Means for Multilevel Data
mice.impute.2l.plausible.values | Defunct 'miceadds' Functions
mice.impute.2l.pls | Defunct 'miceadds' Functions
mice.impute.2l.pls2 | Imputation using Partial Least Squares for Dimension Reduction
mice.impute.2l.pmm | Imputation of a Continuous or a Binary Variable From a Two-Level Regression Model using 'lme4' or 'blme'
mice.impute.2lonly.function | Imputation at Level 2 (in 'miceadds')
mice.impute.2lonly.norm2 | Defunct 'miceadds' Functions
mice.impute.2lonly.pmm2 | Defunct 'miceadds' Functions
mice.impute.bygroup | Groupwise Imputation Function
mice.impute.constant | Imputation Using a Fixed Vector
mice.impute.hotDeck | Imputation of a Variable Using Probabilistic Hot Deck Imputation
mice.impute.imputeR.cFun | Wrapper Function to Imputation Methods in the 'imputeR' Package
mice.impute.imputeR.lmFun | Wrapper Function to Imputation Methods in the 'imputeR' Package
mice.impute.lm | Imputation of a Linear Model by Bayesian Bootstrap
mice.impute.lm_fun | Imputation of a Linear Model by Bayesian Bootstrap
mice.impute.lqs | Imputation of a Linear Model by Bayesian Bootstrap
mice.impute.ml.lmer | Multilevel Imputation Using 'lme4'
mice.impute.plausible.values | Plausible Value Imputation using Classical Test Theory and Based on Individual Likelihood
mice.impute.pls | Imputation using Partial Least Squares for Dimension Reduction
mice.impute.pmm3 | Imputation by Predictive Mean Matching (in 'miceadds')
mice.impute.pmm4 | Imputation by Predictive Mean Matching (in 'miceadds')
mice.impute.pmm5 | Imputation by Predictive Mean Matching (in 'miceadds')
mice.impute.pmm6 | Imputation by Predictive Mean Matching (in 'miceadds')
mice.impute.rlm | Imputation of a Linear Model by Bayesian Bootstrap
mice.impute.simputation | Wrapper Function to Imputation Methods in the 'simputation' Package
mice.impute.smcfcs | Substantive Model Compatible Multiple Imputation (Single Level)
mice.impute.synthpop | Using a 'synthpop' Synthesizing Method in the 'mice' Package
mice.impute.tricube.pmm | Imputation by Tricube Predictive Mean Matching
mice.impute.tricube.pmm2 | Defunct 'miceadds' Functions
mice.impute.weighted.norm | Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression
mice.impute.weighted.pmm | Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression
mice.nmi | Nested Multiple Imputation
miceadds | Some Additional Multiple Imputation Functions, Especially for 'mice'
miceadds-defunct | Defunct 'miceadds' Functions
miceadds-utilities | Utility Functions in 'miceadds'
miceadds_rcpp_ml_mcmc_compute_xtx | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_compute_ztz | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_predict_fixed | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_predict_fixed_random | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_predict_random | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_predict_random_list | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_probit_category_prob | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_sample_beta | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_sample_latent_probit | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_sample_psi | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_sample_sigma2 | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_sample_thresholds | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_sample_u | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_subtract_fixed | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_ml_mcmc_subtract_random | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_pnorm | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_qnorm | MCMC Estimation for Mixed Effects Model
miceadds_rcpp_rtnorm | MCMC Estimation for Mixed Effects Model
mice_imputation_get_states | Utility Functions in 'miceadds'
mice_inits | Arguments for 'mice::mice' Function
micombine.chisquare | Combination of Chi Square Statistics of Multiply Imputed Datasets
micombine.cor | Inference for Correlations and Covariances for Multiply Imputed Datasets
micombine.cov | Inference for Correlations and Covariances for Multiply Imputed Datasets
micombine.F | Combination of F Statistics for Multiply Imputed Datasets Using a Chi Square Approximation
MIcombine.NestedImputationResultList | Functions for Analysis of Nested Multiply Imputed Datasets
mids2datlist | Converting a 'mids', 'mids.1chain' or 'mids.nmi' Object in a Dataset List
mids2mlwin | Export 'mids' object to MLwiN
min0 | Descriptive Statistics for a Vector or a Data Frame
MIwaldtest | Wald Test for Nested Multiply Imputed Datasets
mi_dstat | Cohen's d Effect Size for Missingness Indicators
ml_mcmc | MCMC Estimation for Mixed Effects Model
ml_mcmc_fit | MCMC Estimation for Mixed Effects Model

## -- N --

nested.datlist2datlist | Creates Objects of Class 'datlist' or 'nested.datlist'
nested.datlist_create | Creates Objects of Class 'datlist' or 'nested.datlist'
NestedImputationList | Functions for Analysis of Nested Multiply Imputed Datasets
nestedList2List | Converting a Nested List into a List (and Vice Versa)
NMIcombine | Pooling for Nested Multiple Imputation
NMIextract | Pooling for Nested Multiple Imputation
NMIwaldtest | Wald Test for Nested Multiply Imputed Datasets
nnig_coef | Simulation of Multivariate Linearly Related Non-Normal Variables
nnig_sim | Simulation of Multivariate Linearly Related Non-Normal Variables

## -- O --

output.format1 | R Utilities: Formatting R Output on the R Console

## -- P --

pca.covridge | Principal Component Analysis with Ridge Regularization
plot.mids.1chain | Multiple Imputation by Chained Equations using One Chain
plot.ml_mcmc | MCMC Estimation for Mixed Effects Model
pool.mids.nmi | Pooling for Nested Multiple Imputation
pool_mi | Statistical Inference for Multiply Imputed Datasets
pool_nmi | Pooling for Nested Multiple Imputation
predict.kernelpls.fit2 | Kernel PLS Regression
print.datlist | Creates Objects of Class 'datlist' or 'nested.datlist'
print.mids.1chain | Multiple Imputation by Chained Equations using One Chain
print.mids.nmi | Nested Multiple Imputation
print.nested.datlist | Creates Objects of Class 'datlist' or 'nested.datlist'
print.NestedImputationList | Functions for Analysis of Nested Multiply Imputed Datasets
prop_miss | Descriptive Statistics for a Vector or a Data Frame

## -- Q --

quantile0 | Descriptive Statistics for a Vector or a Data Frame

## -- R --

Rcppfunction | Utility Functions for Writing R Functions
Rcppfunction_remove_classes | Utility Functions for Writing R Functions
rcpp_create_header_file | R Utilities: Source all R or 'Rcpp' Files within a Directory
read.fwf2 | Reading and Writing Files in Fixed Width Format
Reval | R Utilities: Evaluates a String as an Expression in R
Revalpr | R Utilities: Evaluates a String as an Expression in R
Revalprstr | R Utilities: Evaluates a String as an Expression in R
Revalpr_maxabs | R Utilities: Evaluates a String as an Expression in R
Revalpr_round | R Utilities: Evaluates a String as an Expression in R
Rfunction | Utility Functions for Writing R Functions
Rfunction_include_argument_values | Utility Functions for Writing R Functions
Rfunction_output_list_result_function | Utility Functions for Writing R Functions
Rhat.mice | Rhat Convergence Statistic of a 'mice' Imputation
round2 | R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)
Rsessinfo | R Utilities: R Session Information

## -- S --

save.data | R Utilities: Saving/Writing Data Files using 'miceadds'
save.Rdata | R Utilities: Save a Data Frame in 'Rdata' Format
scale_datlist | Adding a Standardized Variable to a List of Multiply Imputed Datasets or a Single Datasets
scan.vec | R Utilities: Scan a Character Vector
scan.vector | R Utilities: Scan a Character Vector
scan0 | R Utilities: Scan a Character Vector
sd0 | Descriptive Statistics for a Vector or a Data Frame
source.all | R Utilities: Source all R or 'Rcpp' Files within a Directory
source.Rcpp.all | R Utilities: Source all R or 'Rcpp' Files within a Directory
stats0 | Descriptive Statistics for a Vector or a Data Frame
string_extract_part | Some Functionality for Strings and File Names
string_to_matrix | Some Functionality for Strings and File Names
str_C.expand.grid | R Utilities: String Paste Combined with 'expand.grid'
subset.datlist | Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets
subset.imputationList | Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets
subset.mids | Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets
subset.mids.1chain | Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets
subset.nested.datlist | Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets
subset.NestedImputationList | Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets
subset_datlist | Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets
subset_nested.datlist | Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets
summary.glm.cluster | Cluster Robust Standard Errors for Linear Models and General Linear Models
summary.lm.cluster | Cluster Robust Standard Errors for Linear Models and General Linear Models
summary.lmer_pool | Statistical Inference for Fixed and Random Structure for Fitted Models in 'lme4'
summary.lmer_vcov | Statistical Inference for Fixed and Random Structure for Fitted Models in 'lme4'
summary.mids.1chain | Multiple Imputation by Chained Equations using One Chain
summary.mids.nmi | Nested Multiple Imputation
summary.mipo.nmi | Pooling for Nested Multiple Imputation
summary.mira.nmi | Evaluates an Expression for (Nested) Multiply Imputed Datasets
summary.MIwaldtest | Wald Test for Nested Multiply Imputed Datasets
summary.ml_mcmc | MCMC Estimation for Mixed Effects Model
summary.NMIwaldtest | Wald Test for Nested Multiply Imputed Datasets
summary.pool_mi | Statistical Inference for Multiply Imputed Datasets
sumpreserving.rounding | Sum Preserving Rounding
syn.constant | Synthesizing Method for Fixed Values by Design in 'synthpop'
syn.formula | Synthesizing Method for 'synthpop' Using a Formula Interface
syn.mice | Using a 'mice' Imputation Method in the 'synthpop' Package
syn_mice | Constructs Synthetic Dataset with 'mice' Imputation Methods
systime | R Utilities: Various Strings Representing System Time

## -- T --

tw.imputation | Two-Way Imputation
tw.mcmc.imputation | Two-Way Imputation

## -- V --

var0 | Descriptive Statistics for a Vector or a Data Frame
VariableNames2String | Stringing Variable Names with Line Breaks
vcov.glm.cluster | Cluster Robust Standard Errors for Linear Models and General Linear Models
vcov.lm.cluster | Cluster Robust Standard Errors for Linear Models and General Linear Models
vcov.lmer_vcov | Statistical Inference for Fixed and Random Structure for Fitted Models in 'lme4'
vcov.mipo.nmi | Pooling for Nested Multiple Imputation
vcov.ml_mcmc | MCMC Estimation for Mixed Effects Model
vcov.pool_mi | Statistical Inference for Multiply Imputed Datasets
visitSequence.determine | Automatic Determination of a Visit Sequence in 'mice'

## -- W --

with.datlist | Evaluates an Expression for (Nested) Multiply Imputed Datasets
with.mids.1chain | Evaluates an Expression for (Nested) Multiply Imputed Datasets
with.mids.nmi | Evaluates an Expression for (Nested) Multiply Imputed Datasets
with.nested.datlist | Evaluates an Expression for (Nested) Multiply Imputed Datasets
with.NestedImputationList | Evaluates an Expression for (Nested) Multiply Imputed Datasets
within.datlist | Evaluates an Expression for (Nested) Multiply Imputed Datasets
within.imputationList | Evaluates an Expression for (Nested) Multiply Imputed Datasets
within.nested.datlist | Evaluates an Expression for (Nested) Multiply Imputed Datasets
within.NestedImputationList | Evaluates an Expression for (Nested) Multiply Imputed Datasets
withPool_MI | Evaluates an Expression for (Nested) Multiply Imputed Datasets
withPool_NMI | Evaluates an Expression for (Nested) Multiply Imputed Datasets
write.datlist | Write a List of Multiply Imputed Datasets
write.fwf2 | Reading and Writing Files in Fixed Width Format
write.mice.imputation | Export Multiply Imputed Datasets from a 'mids' Object
write.pspp | Writing a Data Frame into SPSS Format Using PSPP Software