mi.categorical: Elementary function: multinomial log-linear models to impute...
In mi: Missing Data Imputation and Model Checking

Description Usage Arguments Details Value Author(s) References See Also Examples

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

  mi.categorical( formula, data = NULL, maxit = 100,
                  MaxNWts = 1500, missing.index = NULL, ...)
  ## 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.
`maxit`	Maximum number of iteration.
`MaxNWts`	The maximum allowable number of weights. See nnet for detail.
`missing.index`	The index of missing units of the outcome variable
`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.
`...`	Currently not used.

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 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.

Yu-Sung Su, Andrew Gelman, Jennifer Hill, Masanao Yajima. (2011). “Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box”. Journal of Statistical Software 45(2).

mi.info, mi.method, mi

  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)