mice (version 2.22)

mice.impute.pmm: Imputation by predictive mean matching

Description

Imputes univariate missing data using predictive mean matching

Usage

mice.impute.pmm(y, ry, x, donors = 5, type = 1, ridge = 1e-05,
  version = "", ...)

Arguments

y

Numeric vector with incomplete data

ry

Response pattern of y (TRUE=observed, FALSE=missing)

x

Design matrix with length(y) rows and p columns containing complete covariates.

donors

The size of the donor pool among which a draw is made. The default is donors = 5. Setting donors = 1 always selects the closest match. Values between 3 and 10 provide the best results. Note: The default was changed from 3 to 5 in

type

Type of matching distance. The default choice type = 1 calculates the distance between the predicted value of yobs and the drawn values of ymis. Other choices are type = 0 (distance between predicted val

ridge

The ridge penalty applied in .norm.draw() to prevent problems with multicollinearity. The default is ridge = 1e-05, which means that 0.01 percent of the diagonal is added to the cross-product. Larger ridges may result in more bia

version

A character variable indicating the version to be used. Specifying version = "2.21" calls .pmm.match() instead of the default matcher() function.

...

Other named arguments.

Value

• Numeric vector of length sum(!ry) with imputations

Details

Imputation of y by predictive mean matching, based on Rubin (1987, p. 168, formulas a and b). The procedure is as follows:

1. Estimate beta and sigma by linear regression
2. Draw beta* and sigma* from the proper posterior
3. Compute predicted values foryobsbetaandymisbeta*
4. For eachymis, finddonorsobservations with closest predicted values, randomly sample one of these, and take its observed value inyas the imputation.
5. Ties are broken by making a random draw among ties. Note: The matching is done on predictedy, NOT on observedy.

References

Little, R.J.A. (1988), Missing data adjustments in large surveys (with discussion), Journal of Business Economics and Statistics, 6, 287--301.

Rubin, D.B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.

Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006) Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, 76, 12, 1049--1064.

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/