mice.impute.lda {mice} | R Documentation |
Imputes univariate missing data using linear discriminant analysis
mice.impute.lda(y, ry, x)
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. |
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.
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 mice.imputed data could therefore be somewhat underestimated.
This function can be called from within the Gibbs sampler by specifying
'lda' in the imputationMethod
argument of mice()
.
This method is usually faster and uses fewer resources than
mice.impute.polyreg
.
Stef van Buuren, Karin Groothuis-Oudshoorn, 2000
Van Buuren, S., Groothuis-Oudshoorn, C.G.M. (2000) Multivariate Imputation by Chained Equations: MICE V1.0 User's manual. Leiden: TNO Quality of Life. http://www.stefvanbuuren.nl/publications/MICE V1.0 Manual TNO00038 2000.pdf
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.