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