missing.pattern.plot {mi} | R Documentation |
Function to plot a missing pattern plot.
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", ... )
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. |
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".
Plot to visualize pattern of missingness in the data.
Masanao Yajima yajima@stat.columbia.edu, M.Grazia Pittau grazia@stat.columbia.edu, Andrew Gelman gelman@stat.columbia.edu
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.
data(CHAIN) missing.pattern.plot(CHAIN)