mi.pmm {mi}R Documentation

Elementary function: Probability Mean Matching for imputation.

Description

Imputes univariate missing data using bayesglm and probability mean matching.

Usage

mi.pmm(formula, data = NULL, start = NULL, n.iter = 100, ...)

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.
n.iter Maximum number of iteration for bayesglm. The default is 100.
... Currently not used.

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.

Value

model A summary of the bayesian fitted model.
expected The expected values estimated by the model.
random Vector of length n.mis of random predicted values predicted by using the binomial distribution.

Note

see also http://www.stat.columbia.edu/~gelman/standardize/

Author(s)

Masanao Yajima yajima@stat.columbia.edu, M.Grazia Pittau grazia@stat.columbia.edu, Andrew Gelman gelman@stat.columbia.edu

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.

See Also

mi.info, mi.method, mi


[Package mi version 0.04-6 Index]