SummaryImp: Summary method for objects of class 'imp'

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

Description

Returns some information about the incomplete data set and the imputation process.

Usage

## S3 method for class 'imp'
summary(object,...)

Arguments

object	Either with BBPMM or BBPMM.row generated object.
...	Arguments to be passed to or from other functions.

Details

Returns information about the percentage of missing data as well as about the imputation variant, the number of (multiple) imputations and the number of iterations between two imputations.

Author(s)

Florian Meinfelder

See Also

BBPMM, BBPMM.row

Examples

### sample data set with non-normal variables and two different
### missingness patterns
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 <- as.factor(ifelse(y1>4,5,y1))
y2 <- x1+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)
misrow2 <- sample(n,15)
misrow3 <- sample(n,10)
is.na(data1[misrow1, 4]) <- TRUE
is.na(data1[misrow2, 5]) <- TRUE
is.na(data1[misrow2, 6]) <- TRUE

### imputation
imputed.data <- BBPMM(data1, nIter=5, M=5)
summary(imputed.data)

Example output

Loading required package: Rcpp
Warning in BBPMM(data1, nIter = 5, M = 5) :
  Small data sets can reduce the quality of the predictive mean match.
[1] "number of missing values x1: 0"  "number of missing values x2: 0" 
[3] "number of missing values x3: 0"  "number of missing values y1: 20"
[5] "number of missing values y2: 15" "number of missing values y3: 15"
Imputation 1 of 5: iteration 1 
Variable: y2 y3 y1
 Imputation 1 of 5: iteration 2 
Variable: y2 y3 y1
 Imputation 1 of 5: iteration 3 
Variable: y2 y3 y1
 Imputation 1 of 5: iteration 4 
Variable: y2 y3 y1
 Imputation 1 of 5: iteration 5 
Variable: y2 y3 y1
 Imputation 2 of 5: iteration 1 
Variable: y2 y3 y1
 Imputation 2 of 5: iteration 2 
Variable: y2 y3 y1
 Imputation 2 of 5: iteration 3 
Variable: y2 y3 y1
 Imputation 2 of 5: iteration 4 
Variable: y2 y3 y1
 Imputation 2 of 5: iteration 5 
Variable: y2 y3 y1
 Imputation 3 of 5: iteration 1 
Variable: y2 y3 y1
 Imputation 3 of 5: iteration 2 
Variable: y2 y3 y1
 Imputation 3 of 5: iteration 3 
Variable: y2 y3 y1
 Imputation 3 of 5: iteration 4 
Variable: y2 y3 y1
 Imputation 3 of 5: iteration 5 
Variable: y2 y3 y1
 Imputation 4 of 5: iteration 1 
Variable: y2 y3 y1
 Imputation 4 of 5: iteration 2 
Variable: y2 y3 y1
 Imputation 4 of 5: iteration 3 
Variable: y2 y3 y1
 Imputation 4 of 5: iteration 4 
Variable: y2 y3 y1
 Imputation 4 of 5: iteration 5 
Variable: y2 y3 y1
 Imputation 5 of 5: iteration 1 
Variable: y2 y3 y1
 Imputation 5 of 5: iteration 2 
Variable: y2 y3 y1
 Imputation 5 of 5: iteration 3 
Variable: y2 y3 y1
 Imputation 5 of 5: iteration 4 
Variable: y2 y3 y1
 Imputation 5 of 5: iteration 5 
Variable: y2 y3 y1
 
 16.7 % missing values in the original data

Imputation variant:  multiple imputation 

Number of multiple imputations:  5 

Number of iterations between stored imputations:  5 

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