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