mi.pmm: Elementary function: Predictive Mean Matching for imputation.
In mi: Missing Data Imputation and Model Checking

Description Usage Arguments Details Value Note Author(s) References See Also

Imputes univariate missing data using bayesglm and predictive mean matching.

1	mi.pmm(formula, data = NULL, start = NULL, maxit = 100, missing.index = NULL, ...)

`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
`...`	Currently not used.

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.

`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.

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

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

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.

mi.info, mi.method, mi

mi documentation built on May 2, 2019, 4:43 p.m.