Description

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

Usage

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()