mi.categorical {mi}R Documentation

Elementary function: multinomial log-linear models to impute a categorical variable.

Description

Imputes missing data in a categorical variable using multinomial Log-linear Models.

Usage

  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, ...)

Arguments

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.

Details

multinom calls the library nnet. See multinom for other details.

Value

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.

Author(s)

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

References

Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.

See Also

mi.info, mi.method, mi

Examples

  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)

[Package mi version 0.04-6 Index]