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