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

Imputation by Linear Regression (non Bayesian)

Description

Imputes univariate missing data using linear regression analysis (non Bayesian version)

Usage

    mice.impute.norm.nob(y, ry, x, ...)

Arguments

y

Incomplete data vector of length n

ry

Vector of missing data pattern (FALSE=missing, TRUE=observed)

x

Matrix (n x p) of complete covariates.

...

Other named arguments.

Details

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

Value

A vector of length nmis with imputations.

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.

Note

This function is provided mainly to allow comparison between proper and improper norm methods. Also, it may be useful to impute large data containing many rows.

Author(s)

Stef van Buuren, Karin Groothuis-Oudshoorn, 2000

References

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


[Package mice version 2.11 Index]