SimIm: Introduce some missing values into a data matrix

In imputeR: A General Multivariate Imputation Framework

Description

This function randomly introduce some amount of missing values into a matrix.

Usage

SimIm(data, p = 0.1)

Arguments

data	a data matrix to simulate
p	the percentage of missing values introduced into the data matrix it should be a value between 0 and 1.

Value

the same size matrix with simulated missing values.

Examples

# Create data without missing values as example
simdata <- matrix(rnorm(100), 10, 10)

# Now let's introduce some missing values into the dataset
missingdata <- SimIm(simdata, p = 0.15)

# count the number of missing values afterwards
sum(is.na(missingdata))

#------------------

# There is no missing values in the original parkinson data
data(parkinson)

# Let's introduce some missing values into the dataset
missdata <- SimIm(parkinson, 0.1)

# count the number of missing values afterwards
sum(is.na(missdata))

imputeR documentation built on Jan. 21, 2020, 1:06 a.m.