impute.lda: Elementary Imputation Method: Linear Discriminant Analysis
Description
Imputes univariate missing data using linear discriminant analysis
Usage
impute.lda(y, ry, x)
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.
Value
A vector of length nmis with imputations.
Warning
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.
Details
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.
References
Van Buuren, S. & Oudshoorn, C.G.M. (2000). Multivariate Imputation by Chained Equations:
MICE V1.0 User's manual. Report PG/VGZ/00.038, TNO Prevention and Health, Leiden.
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. (1999). Modern applied statistics with S-Plus (3rd ed). Springer, Berlin.