artifNA.cv: Introduce MCAR Missing Values in a matrix for cross...

In wNNSel: Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables

Description

This function introduces additional missing values in a missing data matrix artificially. The missing values are introduced under missing completely at random (MCAR) mechanism.

Usage

artifNA.cv(x, testNA.prop = 0.1)

Arguments

x	a matrix, in which missing values are to be created.
testNA.prop	proportion of missing values

Value

a list contatining a matrix with artifical missing values, removed indices and the provided x matrix

See Also

cv.wNNSel

Examples

 set.seed(3)
 x = matrix(rnorm(100),10,10)
 ## create 10% missing values in x
 x.miss<- artifNA(x, 0.10)
 ## create another 10% missing values in x
 x.miss.cv<- artifNA.cv(x, 0.10)
 summary(x.miss)
 summary(x.miss.cv)

wNNSel documentation built on May 2, 2019, 2:49 p.m.