Description Usage Arguments Details Value Note Author(s) References See Also Examples
Imputes univariate missing data using bayesglm, an R functions for generalized linear modeling with independent normal, t, or Cauchy prior distribution for the coefficients.
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formula |
an object of class |
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. |
draw.from.beta |
Draws from posterior distribution of the betas to add randomness. |
missing.index |
The index of missing units of the outcome variable |
... |
Currently not used. |
object |
|
y |
Observed values. |
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, Yu-Sung Su suyusung@tsinghua.edu.cn, M.Grazia Pittau grazia@stat.columbia.edu, Andrew Gelman gelman@stat.columbia.edu
Yu-Sung Su, Andrew Gelman, Jennifer Hill, Masanao Yajima. (2011). “Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box”. Journal of Statistical Software 45(2).
Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
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