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mi (version 0.10-1)
Missing Data Imputation and Model Checking
Description
R functions for performing multiple imputation with several checks.
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Install
install.packages('mi')
Monthly Downloads
25,787
Version
0.10-1
License
GPL (>= 2)
Maintainer
YuSung Su
Last Published
April 6th, 2015
Functions in mi (0.10-1)
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mi.hist
Multiple Imputation Histogram
mi.fixed
Elementary function: imputation of constant variable.
mi.method
Virtual class for all mi classes.
mi.scatterplot
Multiple Imputation Scatterplot
mi
Multiple Iterative Regression Imputation
missing.pattern.plot
Missing Pattern Plot
mi.preprocess
Preproessing and Postprocessing mi data object
mi.binary
Elementary function: Bayesian logistic regression to impute a binary variable.
convergence.plot
Convergence Plot of mi Object
CHAIN
Subset of variables from the CHAIN project, a longitudinal cohort study of people living with HIV in New York City.
mi.pooled
Modeling Functions for Multiply Imputed Dataset
mi.info.update
function to update mi.info object to use for multiple imputation
mi.continuous
Elementary function: linear regression to impute a continuous variable.
mi.pmm
Elementary function: Predictive Mean Matching for imputation.
mi.completed
Multiply Imputed Dataframes
mi.info
Function to create information matrix for missing data imputation
noise.control
Auxiliary for Adding Priors to Missing Data Imputation
typecast
Variables type
type.models
Functions to identify types of the models of the mi object
random.imp
Random Imputation of Missing Data
mi.count
Elementary function: Bayesian overdispersed poisson regression to impute a count variable.
mi.categorical
Elementary function: multinomial log-linear models to impute a categorical variable.
write.mi
Writes mi impuations to file
mi.polr
Elementary function: multinomial log-linear models to impute a ordered categorical variable.
plot.mi
Diagnostic Plots for multiple imputation object