Missing Pattern Plot

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Description

Function to plot a missing pattern plot.

Usage

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missing.pattern.plot ( data, y.order = FALSE, x.order = FALSE, clustered = TRUE, 
                                    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.

clustered

if TRUE, data are grouped together with similiar missingness patterns.

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

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

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

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

See Also

mi, plot

Examples

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