Imputes the "best value" according to the linear regression model, also known as regression imputation.
mice.impute.norm.predict(y, ry, x, wy = NULL, ridge = 1e-05, ...)Vector to be imputed
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.
Numeric design matrix with length(y) rows with predictors for
y. Matrix x may have no missing values.
Logical vector of length length(y). A TRUE value
indicates locations in y for which imputations are created.
The ridge penalty used in .norm.draw() to prevent
problems with multicollinearity. The default is ridge = 1e-05,
which means that 0.01 percent of the diagonal is added to the cross-product.
Larger ridges may result in more biased estimates. For highly noisy data
(e.g. many junk variables), set ridge = 1e-06 or even lower to
reduce bias. For highly collinear data, set ridge = 1e-04 or higher.
Other named arguments.
Vector with imputed data, same type as y, and of length
sum(wy)
THIS METHOD SHOULD NOT BE USED FOR DATA ANALYSIS.
This method is seductive because it imputes the most
likely value according to the model. However, it ignores the uncertainty
of the missing values and artificially
amplifies the relations between the columns of the data. Application of
richer models having more parameters does not help to evade these issues.
Stochastic regression methods, like mice.impute.pmm or
mice.impute.norm, are generally preferred.
At best, prediction can give reasonble estimates of the mean, especially if normality assumptions are plausble. See Little and Rubin (2002, p. 62-64) or Van Buuren (2012, p. 11-13, p. 45-46) for a discussion of this method.
Calculates regression weights from the observed data and returns predicted
values to as imputations. The ridge parameter adds a penalty term
ridge*diag(xtx) to the variance-covariance matrix xtx. This
method is known as regression imputation.
Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis with Missing Data. New York: John Wiley and Sons.
Van Buuren, S. (2012). Flexible Imputation of Missing Data. CRC/Chapman & Hall, FL: Boca Raton.
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,
mice.impute.pmm,
mice.impute.polr,
mice.impute.polyreg,
mice.impute.quadratic,
mice.impute.rf,
mice.impute.ri