Imputation by linear regression without parameter uncertainty

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.

Value

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

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.

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.

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

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.lasso.logreg(), mice.impute.lasso.norm(), mice.impute.lasso.select.logreg(), mice.impute.lasso.select.norm(), mice.impute.lda(), mice.impute.logreg(), mice.impute.logreg.boot(), mice.impute.mean(), mice.impute.midastouch(), mice.impute.mnar.logreg(), mice.impute.mpmm(), mice.impute.norm(), mice.impute.norm.boot(), mice.impute.norm.predict(), mice.impute.pmm(), mice.impute.polr(), mice.impute.polyreg(), mice.impute.quadratic(), mice.impute.rf(), mice.impute.ri()

Author

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