Imputes univariate missing data using linear regression with boostrap
mice.impute.norm.boot(y, ry, x, ridge = 1e-05, ...)Incomplete data vector of length n
Vector of missing data pattern (FALSE=missing,
TRUE=observed)
Matrix (n x p) of complete covariates.
Ridge parameter
Other named arguments.
A vector of length nmis with imputations.
Draws a bootstrap sample from x[ry,] and y[ry], calculates
regression weights and imputes with normal residuals. The ridge
parameter adds a penalty term ridge*diag(xtx) to the
variance-covariance matrix xtx.
Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice:
Multivariate Imputation by Chained Equations in R. Journal of
Statistical Software, 45(3), 1-67.
http://www.jstatsoft.org/v45/i03/