SummaryImpprep: Summary method for objects of class 'impprep'

In BaBooN: Bayesian Bootstrap Predictive Mean Matching - Multiple and Single Imputation for Discrete Data

Description

Returns an overview of missing-data patterns, in particular of missing-by-design patterns.

Usage

## S3 method for class 'impprep'
summary(object, nNames=10L,...)

Arguments

object	An object generated by rowimpPrep.
nNames	Number of variable names per block to be printed (Default = 10).
...	Arguments to be passed to or from other functions.

Details

Returns the number of identified missing-data patterns, the first nNames variable names per block and the names of the completely observed variables.

Author(s)

Florian Meinfelder

See Also

rowimpPrep

Examples

### sample data set with non-normal variables and a single
### missingness pattern
set.seed(1000)
n <- 50
x1 <- round(runif(n,0.5,3.5))
x2 <- as.factor(c(rep(1,10),rep(2,25),rep(3,15)))
x3 <- round(rnorm(n,0,3))
y1 <- round(x1-0.25*(x2==2)+0.5*x3+rnorm(n,0,1))
y1 <- ifelse(y1<1,1,y1)
y1 <- ifelse(y1>4,5,y1)
y2 <- y1+rnorm(n,0,0.5)
y3 <- round(x3+rnorm(n,0,2))
data1 <- as.data.frame(cbind(x1,x2,x3,y1,y2,y3))
misrow1 <- sample(n,20)
is.na(data1[misrow1, c(4:6)]) <- TRUE

### preparation step
impblock <- rowimpPrep(data1)

summary(impblock)

BaBooN documentation built on May 2, 2019, 9:30 a.m.