mi.polr {mi} | R Documentation |
Imputes missing data in a categorical variable using multinomial Log-linear Models.
mi.polr(formula, data = NULL, drop.unused.levels = TRUE, start = NULL, n.iter = 100, ...) ## S4 method for signature 'mi.polr': residuals(object, y) ## S4 method for signature 'mi.polr, ANY': plot( x, y, main=deparse( substitute( y ) ), gray.scale = FALSE, ... )
formula |
a formula expression as for regression models, of the form
response ~ predictors . The response should be a factor
(preferably an ordered factor), which will be interpreted as
an ordinal response, with levels ordered as in the factor.
The model must have an intercept: attempts to remove one will
lead to a warning and be ignored. An offset may be used.
See the documentation of 'formula' for other details. |
data |
A data frame containing the incomplete data and the matrix of the complete predictors. |
drop.unused.levels |
Drops unused levels. |
start |
Starting value for bayespolr. |
n.iter |
Maximum number of iteration for bayespolr. The default is 100. |
... |
Currently not used. |
object |
mi.polr object. |
x |
mi.polr 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. |
multinom
calls the library nnet. See multinom for other details.
model |
A summary of the multinomial fitted model |
expected |
The expected values estimated by the model |
random |
Vector of length n.mis of random predicted values predicted by using the multinomial distribution |
residual |
The residual vector of length same as y |
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<-(1+2*x)+rnorm(100,0,1) y<-round(y) y[y<0]<-0 # create artificial missingness on y y[seq(1,100,10)]<-NA dat.xy <- data.frame(x,y) # imputation polr.imp <- mi.polr(factor(y)~x, data = dat.xy)