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