Imputation by predictive mean matching
Imputation by predictive mean matching
mice.impute.pmm( y, ry, x, wy = NULL, donors = 5L, matchtype = 1L, ridge = 1e-05, use.matcher = FALSE, ... )
y |
Vector to be imputed |
ry |
Logical vector of length |
x |
Numeric design matrix with |
wy |
Logical vector of length |
donors |
The size of the donor pool among which a draw is made.
The default is |
matchtype |
Type of matching distance. The default choice
( |
ridge |
The ridge penalty used in |
use.matcher |
Logical. Set |
... |
Other named arguments. |
Imputation of y by predictive mean matching, based on
van Buuren (2012, p. 73). The procedure is as follows:
Calculate the cross-product matrix S=X_{obs}'X_{obs}.
Calculate V = (S+{diag}(S)κ)^{-1}, with some small ridge parameter κ.
Calculate regression weights \hatβ = VX_{obs}'y_{obs}.
Draw q independent N(0,1) variates in vector \dot z_1.
Calculate V^{1/2} by Cholesky decomposition.
Calculate \dotβ = \hatβ + \dotσ\dot z_1 V^{1/2}.
Calculate \dotη(i,j)=|X_{{obs},[i]|}\hatβ-X_{{mis},[j]}\dotβ with i=1,…,n_1 and j=1,…,n_0.
Construct n_0 sets Z_j, each containing d candidate donors, from Y_obs such that ∑_d\dotη(i,j) is minimum for all j=1,…,n_0. Break ties randomly.
Draw one donor i_j from Z_j randomly for j=1,…,n_0.
Calculate imputations \dot y_j = y_{i_j} for j=1,…,n_0.
The name predictive mean matching was proposed by Little (1988).
Vector with imputed data, same type as y, and of length
sum(wy)
Stef van Buuren, Karin Groothuis-Oudshoorn
Little, R.J.A. (1988), Missing data adjustments in large surveys (with discussion), Journal of Business Economics and Statistics, 6, 287–301.
Morris TP, White IR, Royston P (2015). Tuning multiple imputation by predictive mean matching and local residual draws. BMC Med Res Methodol. ;14:75.
Van Buuren, S. (2018). Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL.
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.midastouch(),
mice.impute.mnar.logreg(),
mice.impute.norm.boot(),
mice.impute.norm.nob(),
mice.impute.norm.predict(),
mice.impute.norm(),
mice.impute.polr(),
mice.impute.polyreg(),
mice.impute.quadratic(),
mice.impute.rf(),
mice.impute.ri()
# We normally call mice.impute.pmm() from within mice()
# But we may call it directly as follows (not recommended)
set.seed(53177)
xname <- c("age", "hgt", "wgt")
r <- stats::complete.cases(boys[, xname])
x <- boys[r, xname]
y <- boys[r, "tv"]
ry <- !is.na(y)
table(ry)
# percentage of missing data in tv
sum(!ry) / length(ry)
# Impute missing tv data
yimp <- mice.impute.pmm(y, ry, x)
length(yimp)
hist(yimp, xlab = "Imputed missing tv")
# Impute all tv data
yimp <- mice.impute.pmm(y, ry, x, wy = rep(TRUE, length(y)))
length(yimp)
hist(yimp, xlab = 'Imputed missing and observed tv')
plot(jitter(y), jitter(yimp),
main = 'Predictive mean matching on age, height and weight',
xlab = 'Observed tv (n = 224)',
ylab = 'Imputed tv (n = 224)')
abline(0, 1)
cor(y, yimp, use = 'pair')Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.