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