R: Imputation by the mean

mice.impute.mean {mice} | R Documentation

## Imputation by the mean

### Description

Imputes the arithmetic mean of the observed data

### Usage

    mice.impute.mean(y, ry, x = NULL, 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.

### Value

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

### Warning

Imputing the mean of a variable is almost never appropriate. See Little and Rubin (2002, p. 61-62) or Van Buuren (2012, p. 10-11)

### References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: Multivariate Imputation by Chained Equations in `R`. Journal of Statistical Software, 45(3), 1-67. doi: 10.18637/jss.v045.i03

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

`mice`, `mean`

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.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 3.14.0 Index]