mice.impute.logreg.boot {mice} | R Documentation |
Imputation by logistic regression using the bootstrap
Description
Imputes univariate missing data using logistic regression
by a bootstrapped logistic regression model.
The bootstrap method draws a simple bootstrap sample with replacement
from the observed data y[ry]
and x[ry, ]
.
Usage
mice.impute.logreg.boot(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. |
Value
Vector with imputed data, same type as y
, and of length
sum(wy)
Author(s)
Stef van Buuren, Karin Groothuis-Oudshoorn, 2000, 2011
References
Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice
:
Multivariate Imputation by Chained Equations in R
. Journal of
Statistical Software, 45(3), 1-67.
doi:10.18637/jss.v045.i03
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()
,
mice.impute.mean()
,
mice.impute.midastouch()
,
mice.impute.mnar.logreg()
,
mice.impute.mpmm()
,
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()