mi.dichotomous {mi} | R Documentation |
Imputes univariate missing data using bayesglm, an R functions for generalized linear modeling with independent normal, t, or Cauchy prior distribution for the coefficients.
mi.dichotomous(formula, data = NULL, start = NULL, n.iter = 100, draw.from.beta = FALSE, ...) ## S4 method for signature 'mi.dichotomous': resid(object, y) ## S4 method for signature 'mi.dichotomous': residuals(object, y) ## S4 method for signature 'mi.dichotomous, 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. |
n.iter |
Maximum number of iteration for bayesglm. The default is 100. |
draw.from.beta |
Draws from posterior distribution of the betas to add randomness. |
... |
Currently not used. |
object |
mi.dichotomous object. |
x |
mi.dichotomous object. |
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 ys463@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.
# true data x<-rnorm(100,0,1) # N(0,1) y<-rbinom(100,1,invlogit(1+2*x)) # y ~ Bin(1,invlogit(1 + 2*x) # create artificial missingness on y y[seq(1,100,10)]<-NA dat.xy <- data.frame(x,y) # imputation mi.dichotomous(y~x, data = dat.xy)