MISS {CondMVT} | R Documentation |
Creating Missing Values at Random in Multivariate Datasets
Description
This function randomly creates missing values in a multivariate dataset. The resultant missing data mechanism is missing at random (MAR). The percentage of missingness has to be specified. This percentage is computed as a proportion of the sample size. In addition, the function allows for more than one missing value in any given case. It is set such that in a p
-variate dataset, for any i^{th}
case, the maximum allowable number of missing values is p-1
. This helps avoid a situation where a case has no observed value.
Usage
MISS (TT, Percent)
Arguments
TT |
n×p complete dataset. |
Percent |
the proportion of missing values, which must be specified. |
Value
Data Y
of size n×p
with missing values (NA
) created at random. The missing values are logical in nature.
Examples
# 3-dimensional multivariate t distribution
n <- 10
p=3
df=3
mu=c(1:3)
A <- matrix(rt(p^2,df), p, p)
A <- tcrossprod(A,A) #A %*% t(A)
Y7 <-mvtnorm::rmvt(n, delta=mu, sigma=A, df=df)
Y7
TT=Y7 #Complete Dataset
#Introduce MAR Data
Y8= MISS(TT,20) #The newly created incomplete dataset.
Y8
[Package CondMVT version 0.1.0 Index]