amelia output.overimpute(output, var, subset, legend = TRUE, xlab, ylab, main,
frontend = FALSE, ...)amelia.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.
compare.density, disperse, and
tscsPlot.