Generates a synthetic categorical variable using unordered polytomous regression (without or with bootstrap).
syn.polyreg(y, x, xp, proper = FALSE, maxit = 1000, trace = FALSE,
MaxNWts = 10000, ...)an original data vector of length n.
a matrix (n x p) of original covariates.
a matrix (k x p) of synthesised covariates.
for proper synthesis (proper = TRUE)
a multinomial model is fitted to a bootstrapped sample of the original data.
the maximum number of iterations for nnet.
switch for tracing optimization for nnet.
the maximum allowable number of weights for nnet.
additional parameters passed to nnet.
A vector of length k with synthetic values of y.
Generates synthetic categorical variables by the polytomous regression model. 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 syn.polyreg uses the function
multinom from the nnet package.
In order to avoid bias due to perfect prediction, the data are augmented by the method of White, Daniel and Royston (2010).
White, I.R., Daniel, R. and Royston, P. (2010). Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables. Computational Statistics and Data Analysis, 54, 2267--2275.