Imputes the "best value" according to the linear regression model, also known as regression imputation.
mice.impute.norm.predict(y, ry, x, wy = NULL, ...)Vector with imputed data, same type as y, and of length
sum(wy)
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.
Other named arguments.
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 reasonable estimates of the mean, especially if normality assumptions are plausible. See Little and Rubin (2002, p. 62-64) or Van Buuren (2012, p. 11-13, p. 45-46) for a discussion of this method.
Gerko Vink, Stef van Buuren, 2018
Calculates regression weights from the observed data and returns predicted values to as imputations. 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. (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(),
mice.impute.logreg.boot(),
mice.impute.mean(),
mice.impute.midastouch(),
mice.impute.mnar.logreg(),
mice.impute.mpmm(),
mice.impute.norm(),
mice.impute.norm.boot(),
mice.impute.norm.nob(),
mice.impute.pmm(),
mice.impute.polr(),
mice.impute.polyreg(),
mice.impute.quadratic(),
mice.impute.rf(),
mice.impute.ri()