mi.scatterplot {mi}R Documentation

Multiple Imputation Scatterplot

Description

A function for plotting observed and imputed values for a variable .

Usage

mi.scatterplot( Yobs, Yimp, X = NULL, xlab = NULL, ylab = NULL, 
                            main = "Imputed Variable Scatter Plot", 
                             display.zero = TRUE, gray.scale = FALSE, 
                              obs.col = rgb( 0, 0, 1, alpha = 0.3 ), 
                              imp.col = rgb( 1, 0, 0 ), 
                              obs.pch = 20 , imp.pch = 20, 
                              obs.cex = 0.3, imp.cex = 0.3, 
                              obs.lty = 1  , imp.lty = 1, 
                              obs.lwd = 2.5, imp.lwd = 2.5, ... )
marginal.scatterplot ( data, object, use.imputed.X = FALSE, ...  )

Arguments

Yobs observed values.
Yimp imputed values.
X variable to plot on the x axis.
xlab label on the x axis.
ylab label on the y axis.
display.zero if set to FALSE zeros will not be displayed. Default is TRUE.
main main title of the plot.
gray.scale When set to TRUE, makes the plot into gray scale with predefined color and line type.
obs.col color for the observed variable. Default is "blue".
imp.col color for the imputed variable. Default is "red".
obs.pch data symbol for observed variable. Default is 20.
imp.pch data symbol for imputed variable. Default is 20.
obs.cex text size for observed variable. Default is 0.3.
imp.cex text size for imputed variable. Default is 0.3.
obs.lty line type for observed variable. Default is 1.
imp.lty line type for imputed variable. Default is 1.
obs.lwd line width for observed variable. Default is 2.5.
imp.lwd line width for imputed variable. Default is 2.5.
... Other options for 'plot' function.
data missing data.
object mi object.
use.imputed.X If you want to use the imputed X. Default is FALSE.

Details

Since several data points can have the same data values, especially in discrete variables, small random number is added to each value so that points do not fall on top of each other. See help on jitter for more details. Lowess line is fitted to both imputed and observed data.

Value

A scatterplot with the observed and the imputed values plotted against a chosen variable.

Note

By default imputed values are in red, while the observed values are in blue.

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

 
  # true data
  x<-rnorm(100,0,1) # N(0,1)
  y<-rnorm(100,(1+2*x),1.2) # y ~ 1 + 2*x + N(0,1.2)
  # create artificial missingness on y
  y[seq(1,100,10)]<-NA
  dat.xy <- data.frame(x,y)
  # imputation
  imp.cont<-mi.continuous(y~x, data = dat.xy)
  mi.scatterplot(y,imputed(imp.cont,y))

[Package mi version 0.04-6 Index]