mice.impute.sample

Incomplete data vector of length <code>n</code>

Vector of missing data pattern (<code>FALSE</code>=missing,
<code>TRUE</code>=observed)

Matrix (<code>n</code> x <code>p</code>) of complete covariates.

Imputes a random sample from the observed <code>y</code> data

datagen

Multiple imputation using Fully Conditional Specification (FCS)
    implemented by the MICE algorithm. Each variable has its own imputation
    model. Built-in imputation models are provided for continuous data
    (predictive mean matching, normal), binary data (logistic regression),
    unordered categorical data (polytomous logistic regression) and ordered
    categorical data (proportional odds). MICE can also impute continuous
    two-level data (normal model, pan, second-level variables). Passive
    imputation can be used to maintain consistency between variables. Various
    diagnostic plots are available to inspect the quality of the imputations.

mice.impute.sample: Imputation by simple random sampling

Description

Usage

Arguments

Value

Details

References