mice.impute.mpmm {mice} | R Documentation |
Imputation by multivariate predictive mean matching
Description
Imputes multivariate incomplete data among which there are specific relations, for instance, polynomials, interactions, range restrictions and sum scores.
Usage
mice.impute.mpmm(data, format = "imputes", ...)
Arguments
data |
matrix with exactly two missing data patterns |
format |
A character vector specifying the type of object that should
be returned. The default is |
... |
Other named arguments. |
Details
This function implements the predictive mean matching and applies canonical regression analysis to select donors fora set of missing variables. In general, canonical regressionanalysis looks for a linear combination of covariates that predicts a linear combination of outcomes (a set of missing variables) optimally in a least-square sense (Israels, 1987). The predicted value of the linear combination of the set of missing variables would be applied to perform predictive mean matching.
Value
A matrix with imputed data, which has ncol(y)
columns and
sum(wy)
rows.
Note
The function requires variables in the block have the same missingness pattern. If there are more than one missingness pattern, the function will return a warning.
Author(s)
Mingyang Cai and Gerko Vink
See Also
mice.impute.pmm
Van Buuren, S. (2018).
Flexible Imputation of Missing Data. Second Edition.
Chapman & Hall/CRC. Boca Raton, FL.
Other univariate imputation functions:
mice.impute.cart()
,
mice.impute.lasso.logreg()
,
mice.impute.lasso.norm()
,
mice.impute.lasso.select.logreg()
,
mice.impute.lasso.select.norm()
,
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.pmm()
,
mice.impute.polr()
,
mice.impute.polyreg()
,
mice.impute.quadratic()
,
mice.impute.rf()
,
mice.impute.ri()
Examples
# simulate data
beta2 <- beta1 <- .5
x <- rnorm(1000)
e <- rnorm(1000, 0, 1)
y <- beta1 * x + beta2 * x^2 + e
dat <- data.frame(y = y, x = x, x2 = x^2)
m <- as.logical(rbinom(1000, 1, 0.25))
dat[m, c("x", "x2")] <- NA
# impute
blk <- list("y", c("x", "x2"))
meth <- c("", "mpmm")
imp <- mice(dat, blocks = blk, method = meth, print = FALSE,
m = 2, maxit = 2)
# analyse and check
summary(pool(with(imp, lm(y ~ x + x2))))
with(dat, plot(x, x2, col = mdc(1)))
with(complete(imp), points(x[m], x2[m], col = mdc(2)))