Imputes univariate missing data using linear discriminant analysis

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

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.

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

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 to include bootstrap - disabled since

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`

.

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.

`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.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`