mice.impute.weighted.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.

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

ridge

imputationWeights

Number of factors in PLS regression (if used). The default is <code>NULL</code>
which means that no PLS regression is used for dimension reduction.

pls.facs

Optional vector of variables for which interactions should be created

interactions

Optional vector of variables which should also be included as quadratic effects.

quadratics

Imputation by a weighted linear normal regression.

mice imputation method

Sampling weights


    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.weighted.norm: Imputation by a Weighted Linear Normal Regression

Description

Usage

Arguments

Value

See Also