mice (version 2.25)

mice.impute.lda: Imputation by linear discriminant analysis

Description

Imputes univariate missing data using linear discriminant analysis

Usage

mice.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.

...

Other named arguments.

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., 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.

See Also

mice, link{mice.impute.polyreg}, lda