mi.categorical {mi} | R Documentation |
Imputes missing data in a categorical variable using multinomial Log-linear Models.
mi.categorical( formula, data = NULL, n.iter = 100, MaxNWts = 1500, ...) ## S4 method for signature 'mi.categorical': residuals(object, y) ## S4 method for signature 'mi.categorical, 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 or a
matrix with K columns, which will be interpreted as counts
for each of K classes. A log-linear model is fitted, with
coefficients zero for the first class. An offset can be
included: it should be a numeric matrix with K columns if the
response is either a matrix with K columns or a factor with K
> 2 classes, or a numeric vector for a response factor with 2
levels. See the documentation of formula() for other
details. |
data |
A data frame containing the incomplete data and the matrix of the complete predictors. |
n.iter |
Maximum number of iteration. |
MaxNWts |
The maximum allowable number of weights. See nnet for detail. |
... |
Currently not used. |
object |
mi.categorical object. |
x |
mi.categorical 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. |
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.
x<-rnorm(100,0,1) y<-x+4 y<-round(y) y[y<0]<-0 # create artificial missingness on y y[seq(1,100,10)]<-NA dat.xy <- data.frame(x,y) mi.categorical(formula =y~x,data=dat.xy)