mi.pmm: Elementary function: Predictive Mean Matching for imputation.
Description
Imputes univariate missing data using bayesglm and predictive mean matching.Usage
mi.pmm(formula, data = NULL, start = NULL, maxit = 100, missing.index = NULL, ...)
Arguments
formula
an object of class '"formula"' (or one that can be coerced to that class): a symbolic description of the model to be fitted. See bayesglm 'formula' for details.
data
A data frame containing the incomplete data and the matrix of the complete predictors.
start
Starting value for bayesglm.
maxit
Maximum number of iteration for bayesglm. The default is 100.
missing.index
The index of missing units of the outcome variable
Value
- modelA summary of the bayesian fitted model.
- expectedThe expected values estimated by the model.
- randomVector of length n.mis of random predicted values predicted by using the binomial distribution.
Details
In bayesglm default the prior distribution is Cauchy with center 0 and scale 2.5
for all coefficients (except for the intercept, which has a prior scale of 10).
See also glm for other details.References
Andrew Gelman and Jennifer Hill,
Data Analysis Using Regression and Multilevel/Hierarchical Models,
Cambridge University Press, 2007.
Van Buuren, S. and Oudshoorn, C.G.M. (2000). Multivariate Imputation
by Chained Equations: MICE V1.0 User's manual. Report
PG/VGZ/00.038, TNO Prevention and Health, Leiden.
Rubin, D.B. (1987). Multiple imputation for nonresponse in
surveys. New York: Wiley.