mice.impute.2l.contextual.norm

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.

Type of predictor variables. <code>type=-2</code> refers to the cluster variable,
<code>type=2</code> denotes a variable for which also a contextual effect is
included and <code>type=1</code> denotes all other variables which are
included as 'ordinary'

type

Ridge parameter in the diagonal of $\bold{X}'\bold{X}$

ridge

imputationWeights

Vector of variable names used for creating interactions

interactions

Vector of variable names used for creating quadratic terms

quadratics

This imputation method imputes a variable using linear regression
with normally distributed residuals. Including a contextual effects
means that an aggregated variable at a cluster level is included
as a further covariate.

mice imputation method


    Contains some auxiliary functions for multiple imputation which 
    complements existing functionality in R.
    In addition to some utility functions, main features include 
    plausible value imputation, multilevel imputation functions,
    imputation using partial least squares (PLS) for high dimensional 
    predictors, nested multiple imputation, and two-way imputation.

mice.impute.2l.contextual.norm: Imputation by Normal Linear Regression with Contextual Variables

Description

Usage

Arguments

Value

See Also