Imputes univariate missing data using linear discriminant analysis

## 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`

.

## 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.

## References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`

:
Multivariate Imputation by Chained Equations in `R`

. *Journal of
Statistical Software*, **45**(3), 1-67.
doi:10.18637/jss.v045.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.lasso.logreg()`

,
`mice.impute.lasso.norm()`

,
`mice.impute.lasso.select.logreg()`

,
`mice.impute.lasso.select.norm()`

,
`mice.impute.logreg.boot()`

,
`mice.impute.logreg()`

,
`mice.impute.mean()`

,
`mice.impute.midastouch()`

,
`mice.impute.mnar.logreg()`

,
`mice.impute.mpmm()`

,
`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()`