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.
1 2 3 4 5 6 7 8 | mi.binary(formula, data = NULL, start = NULL, maxit = 100,
draw.from.beta = TRUE, missing.index = NULL, ...)
## S4 method for signature 'mi.binary'
resid(object, y)
## S4 method for signature 'mi.binary'
residuals(object, y)
## S4 method for signature 'mi.binary,ANY'
plot( x, y, main=deparse( substitute( y ) ), gray.scale = FALSE, ...)
|
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. |
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 |
|
x |
|
y |
Observed values. |
main |
main title of the plot. |
gray.scale |
When set to TRUE, makes the plot into gray scale with predefined color and line type. |
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
Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
1 2 3 4 5 6 7 8 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.