Imputes univariate missing data using logistic regression.

`mice.impute.logreg(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.

Vector with imputed data, same type as `y`

, and of length
`sum(wy)`

Imputation for binary response variables by the Bayesian logistic regression model (Rubin 1987, p. 169-170). The Bayesian method consists of the following steps:

Fit a logit, and find (bhat, V(bhat))

Draw BETA from N(bhat, V(bhat))

Compute predicted scores for m.d., i.e. logit-1(X BETA)

Compare the score to a random (0,1) deviate, and impute.

The method relies on the
standard `glm.fit`

function. Warnings from `glm.fit`

are
suppressed. Perfect prediction is handled by the data augmentation
method.

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.

White, I., Daniel, R. and Royston, P (2010). Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables. Computational Statistics and Data Analysis, 54:22672275.

Other univariate imputation functions: `mice.impute.cart`

,
`mice.impute.lda`

,
`mice.impute.logreg.boot`

,
`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`