mice.impute.polyreg {mice} | R Documentation |
Imputes missing data in a categorical variable using polytomous regression
mice.impute.polyreg(y, ry, x, nnet.maxit=100, nnet.trace=FALSE, nnet.maxNWts=1500, ...) mice.impute.polr(y, ry, x, nnet.maxit=100, nnet.trace=FALSE, nnet.maxNWts=1500, ...)
y |
Incomplete data vector of length |
ry |
Vector of missing data pattern ( |
x |
Matrix ( |
nnet.maxit |
Tuning parameter for |
nnet.trace |
Tuning parameter for |
nnet.maxNWts |
Tuning parameter for |
... |
Other named arguments. |
By default, factors with more than two levels are imputed by
mice.impute.polyreg
(for unordered factors) and
mice.impute.polr
(for ordered factors).
The function mice.impute.polyreg
imputation for categorical
response variables by the Bayesian
polytomous regression model. See J.P.L. Brand (1999), Chapter 4,
Appendix B.
The method consists of the following steps:
Fit categorical response as a multinomial model
Compute predicted categories
Add appropriate noise to predictions.
The algorithm of
mice.impute.polyreg
uses the function multinom()
from the nnet
package.
The function mice.impute.polr
imputes for ordered categorical
response variables by the proportional odds logistic
regression (polr) model. The function repeatedly applies
logistic regression on the successive splits. The model is also
known as the cumulative link model.
The algorithm of mice.impute.polr
uses the function polr()
from the MASS
package.
In order to avoid bias due to perfect prediction, both algorithms augment the data according to the method of White, Daniel and Royston (2010).
The call to polr
might fail, usually because the data are
very sparse. In that case, multinom
is tried as a fallback,
and a record is written to the loggedEvents
component of the
mids
object.
A vector of length nmis
with imputations.
Stef van Buuren, Karin Groohuis-Oudshoorn, 2000-2010
Van Buuren, S., Groothuis-Oudshoorn, K. (2011).
mice
: Multivariate Imputation by Chained Equations in R
.
Journal of Statistical Software, 45(3), 1-67.
http://www.jstatsoft.org/v45/i03/
Brand, J.P.L. (1999) Development, implementation and evaluation of multiple imputation strategies for the statistical analysis of incomplete data sets. Dissertation. Rotterdam: Erasmus University.
White, I.R., Daniel, R. Royston, P. (2010). Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables. Computational Statistics and Data Analysis, 54, 2267-2275.
Venables, W.N. & Ripley, B.D. (2002). Modern applied statistics with S-Plus (4th ed). Springer, Berlin.