midastouch (version 1.1)

mice.impute.midastouch: Predictive Mean Matching with distance aided selection of donors

Description

Imputes univariate missing data using predictive mean matching

Usage

mice.impute.midastouch(y, ry, x, ridge = 1e-05, 
	midas.kappa = NULL, outout = TRUE, neff = NULL, debug = NULL)

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.

ridge

The ridge penalty applied to prevent problems with multicollinearity. The default is ridge = 1e-05, which means that 0.001 percent of the diagonal is added to the cross-product. Larger ridges may result in more biased estimates. For highly no

midas.kappa

Scalar. If NULL (default) then the optimal kappa gets selected automatically. Alternatively, the user may specify a scalar. Siddique and Belin 2008 find midas.kappa = 3 to be sensible.

outout

Logical. If TRUE (default) one model is estimated for each donor (leave-one-out principle). For speedup choose outout = FALSE, which estimates one model for all observations leading to in-sample predictions for the donors and out

neff

FOR EXPERTS. Null or character string. The name of an existing environment in which the effective sample size of the donors for each loop (CE iterations times multiple imputations) is supposed to be written. The effective sample size is necessary to compu

debug

FOR EXPERTS. Null or character string. The name of an existing environment in which the input is supposed to be written. The objectname is midastouch.inputlist.

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) and Siddique and Belin 2008. The procedure is as follows:

1. Draw a bootstrap sample from the donor pool.
2. Estimate a beta matrix on the bootstrap sample by the leave one out principle.
3. Compute type II predicted values foryobs(nobs x 1) andymis(nmis x nobs).
4. Calculate the distance between allyobsand the correspondingymis.
5. Convert the distances in drawing probabilities.
6. For each recipient draw a donor from the entire pool while considering the probabilities from the model.
7. Take its observed value inyas the imputation.

References

Gaffert, P., Meinfelder, F., Bosch V. (2015) Towards an MI-proper Predictive Mean Matching, Discussion Paper. https://www.uni-bamberg.de/fileadmin/uni/fakultaeten/sowi_lehrstuehle/statistik/Personen/Dateien_Florian/properPMM.pdf

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

Parzen, M., Lipsitz, S. R., Fitzmaurice, G. M. (2005), A note on reducing the bias of the approximate bayesian bootstrap imputation variance estimator. Biometrika 92, 4, 971--974.

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

Siddique, J., Belin, T.R. (2008), Multiple imputation using an iterative hot-deck with distance-based donor selection. Statistics in medicine, 27, 1, 83--102

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/

Examples

## from R:: mice, slightly adapted ##

# do default multiple imputation on a numeric matrix
library(midastouch)
library(mice)
imp <- mice(nhanes, method = 'midastouch')
imp

# list the actual imputations for BMI
imp$imp$bmi

# first completed data matrix
complete(imp)


# imputation on mixed data with a different method per column

mice(nhanes2, method = c('sample','midastouch','logreg','norm'))