Imputation by predictive mean matching with distance aided donor selection
Imputes univariate missing data using predictive mean matching.
mice.impute.midastouch( y, ry, x, wy = NULL, ridge = 1e-05, midas.kappa = NULL, outout = TRUE, neff = NULL, debug = NULL, ... )
y |
Vector to be imputed |
ry |
Logical vector of length |
x |
Numeric design matrix with |
wy |
Logical vector of length |
ridge |
The ridge penalty used in |
midas.kappa |
Scalar. If |
outout |
Logical. If |
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 compute the correction for the
total variance as originally suggested by Parzen, Lipsitz and
Fitzmaurice 2005. The objectname is |
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 |
... |
Other named arguments. |
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:
Draw a bootstrap sample from the donor pool.
Estimate a beta matrix on the bootstrap sample by the leave one out principle.
Compute type II predicted values for yobs (nobs x 1) and ymis (nmis x nobs).
Calculate the distance between all yobs and the corresponding ymis.
Convert the distances in drawing probabilities.
For each recipient draw a donor from the entire pool while considering the probabilities from the model.
Take its observed value in y as the imputation.
Vector with imputed data, same type as y, and of
length sum(wy)
Philipp Gaffert, Florian Meinfelder, Volker Bosch 2015
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. https://www.jstatsoft.org/v45/i03/
Other univariate imputation functions:
mice.impute.cart(),
mice.impute.lda(),
mice.impute.logreg.boot(),
mice.impute.logreg(),
mice.impute.mean(),
mice.impute.mnar.logreg(),
mice.impute.norm.boot(),
mice.impute.norm.nob(),
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()
# do default multiple imputation on a numeric matrix
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"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.