bush10: Campbell Bushfire Data with added missing data items (10...

In rrcovNA: Scalable Robust Estimators with High Breakdown Point for Incomplete Data

Campbell Bushfire Data with added missing data items (10 percent)

Description

This data set is based on the bushfire data set which was used by Campbell (1984) to locate bushfire scars - see bushfire in package robustbase. The original dataset contains satelite measurements on five frequency bands, corresponding to each of 38 pixels. The data set is very well studied (Maronna and Yohai, 1995; Maronna and Zamar, 2002). There are 12 clear outliers: 33-38, 32, 7-11 and 12 and 13 are suspect.

Usage

data(bush10)

Format

A data frame with 38 observations on 6 variables.

The original data set consists of 38 observations in 5 variables. Based on it four new data sets are created in which some of the data items are replaced by missing values with a simple "missing completely at random " mechanism. For this purpose independent Bernoulli trials are realized for each data item with a probability of success 0.1 where success means that the corresponding item is set to missing.)

Source

Maronna, R.A. and Yohai, V.J. (1995) The Behavoiur of the Stahel-Donoho Robust Multivariate Estimator. Journal of the American Statistical Association 90, 330–341.

Beguin, C. and Hulliger, B. (2004) Multivariate outlier detection in incomplete survey data: the epidemic algorithm and transformed rank correlations. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 127, 2, 275–294.

Examples

## The following code will result in exactly the same output
##  as the one obtained from the original data set
data(bush10)
plot(bush10)
CovNAMcd(bush10)


## Not run: 
##  This is the code with which the missing data were created:
##  Creates a data set with missing values (for testing purposes)
##  from a complete data set 'x'. The probability of
##  each item being missing is 'pr'.
##
getmiss <- function(x, pr=0.1){
    library(Rlab)
    n <- nrow(x)
    p <- ncol(x)
    bt <- rbern(n*p, pr)
    btmat <- matrix(bt, nrow=n)
    btmiss <- ifelse(btmat==1, NA, 0)
    x+btmiss
}

## End(Not run)

rrcovNA documentation built on July 9, 2023, 6:26 p.m.