mice

Compare two nested models using D1-statistic

Compare two nested models using D2-statistic

Compare two nested models using D3-statistic

Generate missing data for simulation purposes

Multivariate amputation based on continuous probability functions

Default freq in ampute

Default odds in ampute()

Default patterns in ampute

Default type in ampute()

Default weights in ampute

Multivariate amputation based on discrete probability functions

Multivariate amputation under a MCAR mechanism

Compare several nested models

Appends specified break to the data

Converts an imputed dataset (long format) into a mids object

Create a mira object from repeated analyses

Converts into a mitml.result object

Growth of Dutch boys

Brandsma school data used Snijders and Bosker (2012)

Box-and-whisker plot of amputed and non-amputed data

Box-and-whisker plot of observed and imputed data

Combine R objects by rows and columns

Combine mids objects by columns

Select complete cases

Complete case indicator

Extracts the completed data from a mids object

Construct blocks from formulas and predictorMatrix

Density plot of observed and imputed data

Employee selection data

Computes least squares parameters

Extends a formula with predictors

Extends formula's with predictor matrix settings

Extract broken stick estimates from a lmer object

SE Fireworks disaster data

Fifth Dutch growth study 2009

Fraction of incomplete cases among cases with observed

Subset rows of a mids object

Fix coefficients and update model

Influx and outflux of multivariate missing data patterns

Fluxplot of the missing data pattern

Extract list of fitted models

Extract estimate from mipo object

Glance method to extract information from a 'mipo' object

Generalized linear model for mids object

Enlarge number of imputations by combining mids objects

Select incomplete cases

Incomplete case indicator

Conditional imputation helper

Check for mads object

Check for mids object

Check for mipo object

Check for mira object

Check for mitml.result object

Leiden 85+ study

Linear regression for mids object

Multivariate amputed data set (mads)

Creates a blocks argument

Creates a blots argument

Creates a formulas argument

Creates a method argument

Creates a post argument

Creates a predictorMatrix argument

Creates a visitSequence argument

Creates a where argument

Mammal sleep data

Find index of matched donor units

Missing data pattern by variable pairs

Missing data pattern

Graphical parameter for missing data plots

mice: Multivariate Imputation by Chained Equations

Imputation by a two-level logistic model using glmer

Imputation by a two-level normal model using lmer

Imputation by a two-level normal model

Imputation by a two-level normal model using pan

Imputation of most likely value within the class

Imputation at level 2 by Bayesian linear regression

Imputation at level 2 by predictive mean matching

Imputation by classification and regression trees

Multivariate multilevel imputation using jomo

Imputation by linear discriminant analysis

Imputation by logistic regression

Imputation by logistic regression using the bootstrap

Imputation by the mean

Imputation by predictive mean matching with distance aided donor selection

Imputation under MNAR mechanism by NARFCS

Imputation by Bayesian linear regression

Imputation by linear regression, bootstrap method

Imputation by linear regression without parameter uncertainty

Imputation by linear regression through prediction

Impute multilevel missing data using pan

Passive imputation

Imputation by predictive mean matching

Imputation of ordered data by polytomous regression

Imputation of unordered data by polytomous regression

Imputation of quadratic terms

Imputation by random forests

Imputation by the random indicator method for nonignorable data

Imputation by simple random sampling

Multivariate Imputation by Chained Equations (Iteration Step)

Set the theme for the plotting Trellis functions

Multiply imputed data set (mids)

Export mids object to Mplus

Export mids object to SPSS

mipo: Multiple imputation pooled object

Multiply imputed repeated analyses (mira)

MNAR demo data

Name imputation blocks

Name formula list elements

Number of complete cases

Cumulative hazard rate or Nelson-Aalen estimator

NHANES example - all variables numerical

NHANES example - mixed numerical and discrete variables

Number of incomplete cases

Number of imputations per block

Draws values of beta and sigma by Bayesian linear regression

Wrapper function that runs MICE in parallel

Datasets with various missing data patterns

Plot the trace lines of the MICE algorithm

Finds an imputed value from matches in the predictive metric (deprecated)

Combine estimates by Rubin's rules

Compare two nested models fitted to imputed data

Pools R^2 of m models fitted to multiply-imputed data

Multiple imputation pooling: univariate version

Hox pupil popularity data with missing popularity scores

Project on preterm and small for gestational age infants (POPS)

Potthoff-Roy data

Print a mids object

Print a mads object

Quick selection of predictors from the data

Combine mids objects by rows

Objects exported from other packages

Self-reported and measured BMI

Squeeze the imputed values to be within specified boundaries.

Stripplot of observed and imputed data

Summary of a mira object

Supports semi-transparent foreground colors?

Terneuzen birth cohort

Tidy method to extract results from a 'mipo' object

Toenail data

Toenail data

Echoes the package version number

Walking disability data

Subset of Irish wind speed data

Evaluate an expression in multiple imputed datasets

Scatterplot of amputed and non-amputed data against weighted sum scores

Scatterplot of observed and imputed data