mi.scatterplot: Multiple Imputation Scatterplot

In mi: Missing Data Imputation and Model Checking

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 ), 
                              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 [e-mail], M.Grazia Pittau [e-mail], Andrew Gelman [e-mail]

References

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).

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

 
  # 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))

mi documentation built on May 2, 2019, 4:43 p.m.