R: Imputation by logistic regression

mice.impute.logreg {mice} | R Documentation

## Imputation by logistic regression

### Description

Imputes univariate missing data using logistic regression.

### Usage

    mice.impute.logreg(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.

### Details

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

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

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

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

4. 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.

### Value

Vector with imputed data, same type as `y`, and of length `sum(wy)`

### Author(s)

Stef van Buuren, Karin Groothuis-Oudshoorn

### 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.

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.

### See Also

`mice`, `glm`, `glm.fit`

Other univariate imputation functions: `mice.impute.cart()`, `mice.impute.lda()`, `mice.impute.logreg.boot()`, `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 3.13.0 Index]