mice.impute.norm.predict {mice} | R Documentation |
Imputation by linear regression through prediction
Description
Imputes the "best value" according to the linear regression model, also known as regression imputation.
Usage
mice.impute.norm.predict(y, ry, x, wy = NULL, ...)
Arguments
y |
Vector to be imputed |
ry |
Logical vector of length |
x |
Numeric design matrix with |
wy |
Logical vector of length |
... |
Other named arguments. |
Details
Calculates regression weights from the observed data and returns predicted values to as imputations. This method is known as regression imputation.
Value
Vector with imputed data, same type as y
, and of length
sum(wy)
Warning
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.
Author(s)
Gerko Vink, Stef van Buuren, 2018
References
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.
See Also
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.mpmm()
,
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()