mice.impute.norm.predict

norm.predict

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.

ridge

Imputes univariate missing data using the predicted value from a linear regression

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.norm.predict: Imputation by Linear Regression, Prediction Method

Description

Usage

Arguments

Value

Details

References