fluxplot(data, local = names(data), plot = TRUE, labels = TRUE,
xlim = c(0, 1), ylim = c(0, 1), las = 1, xlab = "Influx",
ylab = "Outflux", main = paste("Influx-outflux pattern for",
deparse(substitute(data))), eqscplot = TRUE, pty = "s", ...)data.
The default is to include all columns in the
calculations.par.par.par.par.par.par.par.plot() or
eqscplot().ncol(data) rows and six
columns: pobs = Proportion observed, influx = Influx
outflux = Outflux ainb = Average inbound statistic aout =
Averege outbound statistic fico = Fraction of incomplete
cases among cases with Yj observedInflux is equal to the number of variable pairs (Yj ,
Yk) with Yj missing and Yk observed, divided
by the total number of observed data cells. Influx depends
on the proportion of missing data of the variable. Influx
of a completely observed variable is equal to 0, whereas
for completely missing variables wehave influx = 1. For two
variables with the same proportion of missing data, the
variable with higher influx is better connected to the
observed data, and might thus be easier to impute.
Outflux is equal to the number of variable pairs with
Yj observed and Yk missing, divided by the
total number of incomplete data cells. Outflux is an
indicator of the potential usefulness of Yj for
imputing other variables. Outflux depends on the proportion
of missing data of the variable. Outflux of a completely
observed variable is equal to 1, whereas outflux of a
completely missing variable is equal to 0. For two
variables having the same proportion of missing data, the
variable with higher outflux is better connected to the
missing data, and thus potentially more useful for imputing
other variables.
White, I.R., Carlin, J.B. (2010). Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values. Statistics in Medicine, 29, 2920-2931.
flux, md.pattern,
fico