Imputes univariate missing data using linear discriminant analysis
mice.impute.lda(y, ry, x, ...)Incomplete data vector of length n
Vector of missing data pattern (FALSE=missing,
TRUE=observed)
Matrix (n x p) of complete covariates.
Other named arguments.
A vector of length nmis with imputations.
The function does not incorporate the variability of the
discriminant weight, so it is not 'proper' in the sense of Rubin. For small
samples and rare categories in the y, variability of the imputed data
could therefore be somewhat underestimated.
Imputation of categorical response variables by linear discriminant analysis.
This function uses the Venables/Ripley functions lda() and
predict.lda() to compute posterior probabilities for each incomplete
case, and draws the imputations from this posterior.
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/
Brand, J.P.L. (1999). Development, Implementation and Evaluation of Multiple Imputation Strategies for the Statistical Analysis of Incomplete Data Sets. Ph.D. Thesis, TNO Prevention and Health/Erasmus University Rotterdam. ISBN 90-74479-08-1.
Venables, W.N. & Ripley, B.D. (1997). Modern applied statistics with S-PLUS (2nd ed). Springer, Berlin.