R: Imputation by linear regression through prediction

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

## Imputation by linear regression through prediction

### Description

Imputes the "best value" according to the linear regression model, also known as regression imputation.

### Usage

    mice.impute.norm.predict(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

Calculates regression weights from the observed data and returns predicted values to as imputations. This method is known as regression imputation.

### Value

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

### Warning

THIS METHOD SHOULD NOT BE USED FOR DATA ANALYSIS. This method is seductive because it imputes the most likely value according to the model. However, it ignores the uncertainty of the missing values and artificially amplifies the relations between the columns of the data. Application of richer models having more parameters does not help to evade these issues. Stochastic regression methods, like `mice.impute.pmm` or `mice.impute.norm`, are generally preferred.

At best, prediction can give reasonable estimates of the mean, especially if normality assumptions are plausible. See Little and Rubin (2002, p. 62-64) or Van Buuren (2012, p. 11-13, p. 45-46) for a discussion of this method.

### Author(s)

Gerko Vink, Stef van Buuren, 2018

### References

Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis with Missing Data. New York: John Wiley and Sons.

Van Buuren, S. (2018). Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL.

### See Also

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.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.nob()`, `mice.impute.norm()`, `mice.impute.pmm()`, `mice.impute.polr()`, `mice.impute.polyreg()`, `mice.impute.quadratic()`, `mice.impute.rf()`, `mice.impute.ri()`

[Package mice version 3.14.0 Index]