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