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