mice (version 2.46.0)

mice.impute.quadratic: Imputation of quadratric terms

Description

Imputes incomplete variable that appears as both main effect and quadratic effect in the complete-data model.

Usage

mice.impute.quadratic(y, ry, x, wy = NULL, ...)

Arguments

y

Vector to be imputed

ry

Logical vector of length length(y) indicating the the subset y[ry] of elements in y to which the imputation model is fitted. The ry generally distinguishes the observed (TRUE) and missing values (FALSE) in y.

x

Numeric design matrix with length(y) rows with predictors for y. Matrix x may have no missing values.

wy

Logical vector of length length(y). A TRUE value indicates locations in y for which imputations are created.

...

Other named arguments.

Value

Vector with imputed data, same type as y, and of length sum(wy)

Details

This function implements the "polynomial combination" method. First, the polynomial combination \(Z = Y \beta_1 + Y^2 \beta_2\) is formed. \(Z\) is imputed by predictive mean matching, followed by a decomposition of the imputed data \(Z\) into components \(Y\) and \(Y^2\). See Van Buuren (2012, pp. 139-141) and Vink et al (2012) for more details. The method ensures that 1) the imputed data for \(Y\) and \(Y^2\) are mutually consistent, and 2) that provides unbiased estimates of the regression weights in a complete-data linear regression that use both \(Y\) and \(Y^2\).

References

van Buuren, S. (2012). Flexible Imputation of Missing Data. Boca Raton, FL: Chapman & Hall/CRC Press.

Vink, G., van Buuren, S. (2013). Multiple Imputation of Squared Terms. Sociological Methods & Research, 42:598-607.

See Also

mice.impute.pmm

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.norm.boot, mice.impute.norm.nob, mice.impute.norm.predict, mice.impute.norm, mice.impute.pmm, mice.impute.polr, mice.impute.polyreg, mice.impute.rf, mice.impute.ri

Examples

# NOT RUN {
require(lattice)

# Create Data
B1 = .5
B2 = .5
X <- rnorm(1000)
XX <- X^2
e <- rnorm(1000, 0, 1)
Y <- B1 * X + B2 * XX + e
dat <- data.frame(x = X, xx = XX, y = Y)

# Impose 25 percent MCAR Missingness
dat[0 == rbinom(1000, 1, 1 -.25), 1:2] <- NA

# Prepare data for imputation
ini <- mice(dat, maxit = 0)
meth <- c("quadratic", "~I(x^2)", "")
pred <- ini$pred
pred[, "xx"] <- 0

# Impute data
imp <- mice(dat, meth = meth, pred = pred)

# Pool results
pool(with(imp, lm(y ~ x + xx)))

# Plot results
stripplot(imp)
plot(dat$x, dat$xx, col = mdc(1), xlab = "x", ylab = "xx")
cmp <- complete(imp)
points(cmp$x[is.na(dat$x)], cmp$xx[is.na(dat$x)], col = mdc(2))
# }