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