mi (version 0.10-2)

mi.categorical: 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, maxit = 100,
                  MaxNWts = 1500, missing.index = NULL, ...)
  ## S3 method for class 'mi.categorical':
residuals(object, y)
  ## S3 method for class '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-li

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.

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.

Details

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

References

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).

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)