column number or variable name of the variable to
overimpute.
subset
an optional vector specifying a subset of observations
to be used in the overimputation.
legend
a logical value indicating if a legend should be
plotted.
xlab
the label for the x-axis. The default is "Observed Values."
ylab
the label for the y-axis. The default is "Imputed Values."
main
main title of the plot. The default is to smartly title the plot
using the variable name.
frontend
a logical value used internally for the Amelia GUI.
...
further graphical parameters for the plot.
Details
This function temporarily treats each observed value in
var as missing and imputes that value based on the imputation
model of output. The dots are the mean imputation and the
vertical lines are the 90% percent confidence intervals for
imputations of each observed value. The diagonal line is the y=x
line. If all of the imputations were perfect, then our points would
all fall on the line. A good imputation model would have about 90% of
the confidence intervals containing the truth; that is, about 90% of
the vertical lines should cross the diagonal.
The color of the vertical lines displays the fraction of missing
observations in the pattern of missingness for that
observation. The legend codes this information. Obviously, the
imputations will be much tighter if there are more observed covariates
to use to impute that observation.
The subset argument evaluates in the environment of the
data. That is, it can but is not required to refer to variables in the
data frame as if it were attached.
Value
A 5-column matrix that contains (1) the row in the original data,
(2) the observed value of that observation, (2) the mean of the
overimputations, (3) the lower bound of the 95% confidence interval of
the overimputations, (4) the upper bound of the 95% confidence interval
of the overimputations, and (5) the fraction of the variables that were
missing for that observation in the original data.
See Also
Other imputation diagnostics are
compare.density, disperse, and
tscsPlot.