Influx and outflux are statistics of the missing data pattern. These statistics are useful in selecting predictors that should go into the imputation model.

## Usage

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",
lwd = 1,
...
)

## Arguments

data

A data frame or a matrix containing the incomplete data. Missing values are coded as NA's.

local

A vector of names of columns of data. The default is to include all columns in the calculations.

plot

Should a graph be produced?

labels

Should the points be labeled?

xlim

See par.

ylim

See par.

las

See par.

xlab

See par.

ylab

See par.

main

See par.

eqscplot

Should a square plot be produced?

pty

See par.

lwd

See par. Controls axis line thickness and diagonal

...

Further arguments passed to plot() or eqscplot().

## Value

An invisible data frame with ncol(data) rows and six columns: pobs = Proportion observed, influx = Influx outflux = Outflux ainb = Average inbound statistic aout = Average outbound statistic fico = Fraction of incomplete cases among cases with Yj observed

## Details

Infux and outflux have been proposed by Van Buuren (2012), chapter 4.

Influx 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 we have 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.

Van Buuren, S. (2018). Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL.

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.