mice3.18.0 package

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

Generate missing data for simulation purposes

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

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

Select complete cases

Complete case indicator

Extracts the completed data from a mids object

Construct blocks from formulas and predictorMatrix

Computes convergence diagnostics for a mids object

Compare two nested models using D1-statistic

Compare two nested models using D2-statistic

Compare two nested models using D3-statistic

Density plot of observed and imputed 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

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

Wrapper function that runs MICE in parallel

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

Linear regression for mids object

Multivariate amputed data set (mads)

Creates a blocks argument

Creates a blots argument

Create calltype of the imputation model

Creates a formulas argument

Creates a method argument

Creates a post argument

Creates a predictorMatrix argument

Creates a visitSequence argument

Creates a where argument

Find index of matched donor units

Jamshidian and Jalal's Non-Parametric MCAR Test

Missing data pattern by variable pairs

Missing data pattern

Graphical parameter for missing data plots

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 direct use of lasso logistic regression

Imputation by direct use of lasso linear regression

Imputation by indirect use of lasso logistic regression

Imputation by indirect use of lasso linear regression

Imputation by linear discriminant analysis

Imputation by logistic regression using the bootstrap

Imputation by logistic regression

Imputation by the mean

Imputation by predictive mean matching with distance aided donor selec...

Imputation under MNAR mechanism by NARFCS

Imputation by multivariate predictive mean matching

Imputation by linear regression, bootstrap method

Imputation by linear regression without parameter uncertainty

Imputation by linear regression through prediction

Imputation by Bayesian linear regression

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)

mice: Multivariate Imputation by Chained Equations

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

Create an object of class "mira"

Name imputation blocks

Name formula list elements

Number of complete cases

Cumulative hazard rate or Nelson-Aalen estimator

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

Finds an imputed value from matches in the predictive metric (deprecat...

Compare two nested models fitted to imputed data

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

Combine estimates by pooling rules

Multiple imputation pooling: univariate version

Combines estimates from a tidy table

Print a mira object

Quick selection of predictors from the data

Objects exported from other packages

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?

Tidy method to extract results from a mipo object

Echoes the package version number

Evaluate an expression in multiple imputed datasets

Scatterplot of amputed and non-amputed data against weighted sum score...

Scatterplot of observed and imputed data