Imputation by linear regression, prediction method
Multivariate Imputation by Chained Equations (Iteration Step)
Multiple imputation pooling
Converts an multiply imputed dataset (long format) into a mids object
Density plot of observed and imputed data
Imputation by a two-level normal model
Fluxplot of the missing data pattern
Complete cases n
Influx and outflux of multivariate missing data patterns
Generalized linear model for mids object
Create a mira object from repeated analyses
Imputation by linear discriminant analysis
Complete cases
Imputation of quadratric terms
Imputation by linear regression, bootstrap method
Box-and-whisker plot of observed and imputed data
Conversion of a imputed data set (long form) to a mids object
Missing data pattern by variable pairs
Imputation by linear regression (non Bayesian)
Imputation by polytomous regression - unordered
Imputation by a two-level normal model using pan
Fifth Dutch growth study 2009
Imputation by predictive mean matching
Check for mira object
Project on preterm and small for gestational age infants (POPS)
Appends specified break to the data
Scatterplot of observed and imputed data
Growth of Dutch boys
Columnwise combination of a mids object.
NHANES example - all variables numerical
SE Fireworks disaster data
Creates imputed data sets from a mids object
Multivariate Imputation by Chained Equations (MICE)
Linear regression for mids object
Imputation by classification and regression trees
Imputation by polytomous regression - ordered
Imputation at level 2 by Bayesian linear regression
Imputation by logistic regression using the bootstrap
Imputation by the mean
Self-reported and measured BMI
Complete case indicator
Evaluate an expression in multiple imputed datasets
Squeeze the imputed values to be within specified boundaries.
Incomplete cases
Set the theme for the plotting Trellis functions
Multiple imputation pooling: univariate version
Imputation of the mean within the class
Graphical parameter for missing data plots.
Summary of a mira object
Conditional imputation helper
Imputation by fast predictive mean matching
Extracts fit objects from mira object
Incomplete case indicator
Leiden 85+ study
Export mids object to Mplus
Cumulative hazard rate or Nelson-Aalen estimator
Combine imputations fitted to the same data
Export mids object to SPSS
Imputation by the random indicator method for nonignorable data
Incomplete cases n
Multiply imputed repeated analyses (mira)
Imputation by random forests
Multiply imputed pooled analysis (mipo)
Missing data pattern
NHANES example - mixed numerical and discrete variables
Datasets with various missing data patterns
Hox pupil popularity data with missing popularity scores
Print a mids object
Quick selection of predictors from the data
Potthoff-Roy data
Terneuzen birth cohort
Extract broken stick estimates from a lmer object
Check for mipo object
Supports semi-transparent foreground colors?
Multiply imputed data set (mids)
Mammal sleep data
Imputation at level 2 by predictive mean matching
Compare two nested models fitted to imputed data
Walking disability data
Stripplot of observed and imputed data
Plot the trace lines of the MICE algorithm
Check for mids object
Passive imputation
Echoes the package version number
Subset of Irish wind speed data
Draws values of beta and sigma by Bayesian linear regression
Pooling: R squared
Imputation by logistic regression
Imputation by Bayesian linear regression
Imputation by simple random sampling
Fraction of incomplete cases among cases with observed
Rowwise combination of a mids object.