fill_simple: Imputation by Simple Rules

In filling: Matrix Completion, Imputation, and Inpainting Methods

Description

One of the most simplest ways to fill in the missing entries is to apply any simple rule for each variable. In this example, we provide 3 options, "mean", "median", and "random". It assumes that every column has at least one non-missing entries in that for each column, the rule is applied from the subset of non-missing values.

Usage

fill.simple(A, method = c("mean", "median", "random"))

Arguments

A	an (n\times p) partially observed matrix.
method	simple rule to fill in the missing entries in a columnwise manner.

Value

a named list containing

X

an (n\times p) matrix after completion.

References

\insertRef

gelman_data_2007filling

Examples

## load image data of 'lena128'
data(lena128)

## transform 5% of entries into missing
A <- aux.rndmissing(lena128, x=0.05)

## apply all three methods#'
fill1 <- fill.simple(A, method="mean")
fill2 <- fill.simple(A, method="median")
fill3 <- fill.simple(A, method="random")

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2), pty="s")
image(A, col=gray((0:100)/100), axes=FALSE, main="original")
image(fill1$X, col=gray((0:100)/100), axes=FALSE, main="method:mean")
image(fill2$X, col=gray((0:100)/100), axes=FALSE, main="method:median")
image(fill3$X, col=gray((0:100)/100), axes=FALSE, main="method:random")
par(opar)

filling documentation built on Aug. 21, 2021, 5:09 p.m.