mi (version 0.10-2)

mi.hist: Multiple Imputation Histogram

Description

A function for plotting the histogram of each variable and of its observed and imputed values.

Usage

mi.hist( object, Yobs, ...)
## S3 method for class 'mi.method,ANY':
mi.hist( object, Yobs, \dots)
## S3 method for class 'mi.categorical,ANY':
mi.hist( object, Yobs, \dots)
## S3 method for class 'mi.binary,ANY':
mi.hist( object, Yobs,\dots)
## S3 method for class 'mi.polr,ANY':
mi.hist( object, Yobs, \dots)
## S3 method for class 'mi.pmm,ANY':
mi.hist( object, Yobs, \dots)

Arguments

Yobs

observed values.

object

imputed values or member object of mi.method object family.

...

Other options for plot function.

Value

• The histogram (in black) of the complete variable, the histogram (in blue) of the observed values and the histogram (in red) of the imputed values.

References

Kobi Abayomi, Andrew Gelman and Marc Levy. (2008). Diagnostics for multivariate imputations. Applied Statistics 57, Part 3: 273--291. 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). Andrew Gelman and Jennifer Hill. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.

See Also

plot.mi, hist

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(2,100,10)]<-NA
  dat.xy <- data.frame(x,y)
  # imputation
  dat.cont.mi <- mi.continuous(y~x, data = dat.xy)
  mi.hist( dat.cont.mi, y)

  # imputation
  #dat.mi <- mi(dat.xy)
  #mi.hist( imp(dat.mi,1)[["y"]], y)