missing.pattern.plot {mi}R Documentation

Missing Pattern Plot

Description

Function to plot a missing pattern plot.

Usage

missing.pattern.plot ( data, y.order = FALSE, x.order = FALSE, 
                                    xlab = "Index", ylab = "Variable", 
                                    main = NULL, gray.scale = FALSE,
                                    obs.col = "blue", mis.col = "red", ... )

Arguments

data data.frame or matrix of data with missing data coded as "NA".
y.order if TRUE, orders the variable by number of missing value. Default is FALSE.
x.order if TRUE, orders the data by number of missing value. Default is FALSE.
xlab a title for the x axis: see 'title'.
ylab a title for the y axis: see 'title'.
main an overall title for the plot: see 'title'.
gray.scale if TRUE, makes the plot into black and white. This option overwrites the color specification.
obs.col color used for observed values. Default is "blue".
mis.col color used for missing values. Default is "red".
... additional parameters passed to 'image' function.

Details

Color image with different color for missing and observed value in the dataset is plotted. By default the observed is in "blue" and missing is in "red".

Value

Plot to visualize pattern of missingness in the data.

Author(s)

Masanao Yajima yajima@stat.columbia.edu, M.Grazia Pittau grazia@stat.columbia.edu, Andrew Gelman gelman@stat.columbia.edu

References

Kobi Abayomi, Andrew Gelman and Marc Levy. (2008). “Diagnostics for multivariate imputations”. Applied Statistics 57, Part 3: 273–291.

Andrew Gelman and Maria Grazia Pittau. “A flexible program for missing-data imputation and model checking.” Technical report. Columbia University, New York.

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

See Also

mi, plot

Examples

  data(CHAIN)
  missing.pattern.plot(CHAIN)

[Package mi version 0.04-6 Index]