Complete case indicator
Extract broken stick estimates from a lmer
object
Multivariate Amputation Based On Continuous Probability Functions
Default freq
in ampute
Employee selection data
SE Fireworks disaster data
Compare two nested models using D3-statistic
Generate Missing Data for Simulation Purposes
Default odds
in ampute()
Computes least squares parameters
Converts an multiply imputed dataset (long format) into a mids
object
Create a mira
object from repeated analyses
Extends a formula with predictors
Extract estimate from mipo
object
Fluxplot of the missing data pattern
Extends formula's with predictor matrix settings
Default patterns
in ampute
Enlarge number of imputations by combining mids
objects
Converts into a mitml.result
object
Growth of Dutch boys
Box-and-whisker plot of observed and imputed data
Select incomplete cases
Missing data pattern
Graphical parameter for missing data plots.
Combine R Objects by Rows and Columns
Imputation by predictive mean matching with distance aided donor selection
Imputation by Bayesian linear regression
Multiply imputed data set (mids
)
Default type
in ampute()
Fifth Dutch growth study 2009
Fraction of incomplete cases among cases with observed
Default weights
in ampute
Export mids
object to Mplus
Multivariate Amputation Based On Discrete Probability Functions
Pooling: R squared
Multiple imputation pooling: univariate version
Summary of a mira
object
Multivariate Amputation In A MCAR Manner
Construct blocks from formulas
and predictorMatrix
Generalized linear model for mids
object
Density plot of observed and imputed data
Extract list of fitted model
Creates a formulas
argument
Check for mads
object
Supports semi-transparent foreground colors?
Incomplete case indicator
Brandsma school data used Snijders and Bosker (2012)
Scatterplot of observed and imputed data
Box-and-whisker plot of amputed and non-amputed data
Check for mids
object
Conditional imputation helper
Fix coefficients and update model
Creates a post
argument
Check for mitml.result
object
Combine mids
objects by columns
Leiden 85+ study
Creates a predictorMatrix
argument
mice : Multivariate Imputation by Chained Equations
Creates a method
argument
Select complete cases
Influx and outflux of multivariate missing data patterns
Linear regression for mids
object
Creates a visitSequence
argument
Multivariate Amputed Data Set (mads
)
Check for mipo
object
Imputation by a two-level logistic model using glmer
Mammal sleep data
Creates a where
argument
Missing data pattern by variable pairs
Check for mira
object
Creates a blocks
argument
Imputation by a two-level normal model using pan
Imputation at level 2 by Bayesian linear regression
Imputation at level 2 by predictive mean matching
Imputation by a two-level normal model using lmer
Creates a blots
argument
Imputation by classification and regression trees
Imputation by a two-level normal model
Imputation by logistic regression using the bootstrap
Impute multilevel missing data using pan
Imputation by the mean
Imputation by linear regression through prediction
Imputation of ordered data by polytomous regression
Multivariate multilevel imputation using jomo
Imputation of the mean within the class
Imputation by linear discriminant analysis
Passive imputation
Imputation of quadratic terms
Set the theme for the plotting Trellis functions
Multivariate Imputation by Chained Equations (Iteration Step)
Imputation by predictive mean matching
Imputation by random forests
Imputation of unordered data by polytomous regression
Export mids
object to SPSS
mipo
: Multiple imputation pooled object
Imputation by logistic regression
Cumulative hazard rate or Nelson-Aalen estimator
Imputation by linear regression, bootstrap method
Imputation by linear regression without parameter uncertainty
Imputation by the random indicator method for nonignorable data
Name formula list elements
Multiply imputed repeated analyses (mira
)
Imputation by simple random sampling
Number of imputations per block
Number of complete cases
Print a mads
object
NHANES example - mixed numerical and discrete variables
Number of incomplete cases
Quick selection of predictors from the data
Draws values of beta and sigma by Bayesian linear regression
NHANES example - all variables numerical
Wrapper function that runs MICE in parallel
Name imputation blocks
Plot the trace lines of the MICE algorithm
Finds an imputed value from matches in the predictive metric (deprecated)
Hox pupil popularity data with missing popularity scores
Datasets with various missing data patterns
Terneuzen birth cohort
Echoes the package version number
Project on preterm and small for gestational age infants (POPS)
Squeeze the imputed values to be within specified boundaries.
Stripplot of observed and imputed data
Walking disability data
Subset of Irish wind speed data
Combine estimates by Rubin's rules
Print a mids
object
Potthoff-Roy data
Compare two nested models fitted to imputed data
Combine mids
objects by rows
Self-reported and measured BMI
Scatterplot of amputed and non-amputed data against weighted sum scores
Evaluate an expression in multiple imputed datasets
Compare two nested models using D1-statistic
Compare two nested models using D2-statistic
Compare several nested models
Appends specified break to the data
Extracts the completed data from a mids
object