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