mice (version 2.1)

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

Description

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

Usage

mice.impute.norm.predict(y, ry, x, ridge=0.00001, ...)

Arguments

y

Incomplete data vector of length n

ry

Vector of missing data pattern (FALSE=missing, TRUE=observed)

x

Matrix (n x p) of complete covariates.

ridge

Ridge parameter

...

Other named arguments.

Value

• A vector of length nmis with imputations.

Details

Calculates regression weights from the observed data and and return predicted values to as imputations. The ridge parameter adds a penalty term ridge*diag(xtx) to the variance-covariance matrix xtx.

References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011) MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, forthcoming. http://www.stefvanbuuren.nl/publications/MICE in R - Draft.pdf