R: Imputation by linear discriminant analysis

mice.impute.lda {mice} | R Documentation

## Imputation by linear discriminant analysis

### Description

Imputes univariate missing data using linear discriminant analysis

### Usage

    mice.impute.lda(y, ry, x, wy = NULL, ...)

### Arguments

`y` | Vector to be imputed
`ry` | Logical vector of length `length(y)` indicating the the subset `y[ry]` of elements in `y` to which the imputation model is fitted. The `ry` generally distinguishes the observed (`TRUE`) and missing values (`FALSE`) in `y`.
`x` | Numeric design matrix with `length(y)` rows with predictors for `y`. Matrix `x` may have no missing values.
`wy` | Logical vector of length `length(y)`. A `TRUE` value indicates locations in `y` for which imputations are created.
`...` | Other named arguments. Not used.

### 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.

This function can be called from within the Gibbs sampler by specifying `"lda"` in the `method` argument of `mice()`. This method is usually faster and uses fewer resources than calling the function, but the statistical properties may not be as good (Brand, 1999). `mice.impute.polyreg`.

### Value

Vector with imputed data, of type factor, and of length `sum(wy)`

### 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 underestimated.

Added: SvB June 2009 Tried to include bootstrap, but disabled since bootstrapping may easily lead to constant variables within groups.

### Author(s)

Stef van Buuren, Karin Groothuis-Oudshoorn, 2000

### References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: Multivariate Imputation by Chained Equations in `R`. Journal of Statistical Software, 45(3), 1-67. https://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`

Other univariate imputation functions: `mice.impute.cart()`, `mice.impute.logreg.boot()`, `mice.impute.logreg()`, `mice.impute.mean()`, `mice.impute.midastouch()`, `mice.impute.mnar.logreg()`, `mice.impute.norm.boot()`, `mice.impute.norm.nob()`, `mice.impute.norm.predict()`, `mice.impute.norm()`, `mice.impute.pmm()`, `mice.impute.polr()`, `mice.impute.polyreg()`, `mice.impute.quadratic()`, `mice.impute.rf()`, `mice.impute.ri()`

[Package mice version 3.12.0 Index]