R: Imputation by linear regression without parameter uncertainty

mice.impute.norm.nob {mice} | R Documentation

## Imputation by linear regression without parameter uncertainty

### Description

Imputes univariate missing data using linear regression analysis without accounting for the uncertainty of the model parameters.

### Usage

    mice.impute.norm.nob(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

This function creates imputations using the spread around the fitted linear regression line of `y` given `x`, as fitted on the observed data.

This function is provided mainly to allow comparison between proper (e.g., as implemented in `mice.impute.norm` and improper (this function) normal imputation methods.

For large data, having many rows, differences between proper and improper methods are small, and in those cases one may opt for speed by using `mice.impute.norm.nob`.

### Value

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

### Warning

The function does not incorporate the variability of the regression weights, so it is not 'proper' in the sense of Rubin. For small samples, variability of the imputed data is therefore underestimated.

### Author(s)

Gerko Vink, Stef van Buuren, Karin Groothuis-Oudshoorn, 2018

### 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.

### See Also

`mice`, `mice.impute.norm`

Other univariate imputation functions: `mice.impute.cart()`, `mice.impute.lda()`, `mice.impute.logreg.boot()`, `mice.impute.logreg()`, `mice.impute.mean()`, `mice.impute.midastouch()`, `mice.impute.mnar.logreg()`, `mice.impute.norm.boot()`, `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 3.8.0 Index]