pulpfiber: Pulp Fiber and Paper Data

In robustbase: Basic Robust Statistics

Pulp Fiber and Paper Data

Description

Measurements of aspects pulp fibers and the paper produced from them. Four properties of each are measured in sixty-two samples.

Usage

data(pulpfiber, package="robustbase")

Format

A data frame with 62 observations on the following 8 variables.

X1

numeric vector of arithmetic fiber length

X2

numeric vector of long fiber fraction

X3

numeric vector of fine fiber fraction

X4

numeric vector of zero span tensile

Y1

numeric vector of breaking length

Y2

numeric vector of elastic modulus

Y3

numeric vector of stress at failure

Y4

numeric vector of burst strength

Details

Cited from the reference article: The dataset contains measurements of properties of pulp fibers and the paper made from them. The aim is to investigate relations between pulp fiber properties and the resulting paper properties. The dataset contains n = 62 measurements of the following four pulp fiber characteristics: arithmetic fiber length, long fiber fraction, fine fiber fraction, and zero span tensile. The four paper properties that have been measured are breaking length, elastic modulus, stress at failure, and burst strength.

The goal is to predict the q = 4 paper properties from the p = 4 fiber characteristics.

Author(s)

port to R and this help page: Martin Maechler

Source

Rousseeuw, P. J., Van Aelst, S., Van Driessen, K., and Agulló, J. (2004) Robust multivariate regression; Technometrics 46, 293–305.

Till 2016 available from http://users.ugent.be/~svaelst/data/pulpfiber.txt

References

Lee, J. (1992) Relationships Between Properties of Pulp-Fibre and Paper, unpublished doctoral thesis, U. Toronto, Faculty of Forestry.

Examples

data(pulpfiber)
str(pulpfiber)

pairs(pulpfiber, gap=.1)
## 2 blocks of 4 ..
c1 <- cov(pulpfiber)
cR <- covMcd(pulpfiber)
## how different are they: The robust estimate has more clear high correlations:
symnum(cov2cor(c1))
symnum(cov2cor(cR$cov))

robustbase documentation built on Nov. 1, 2024, 3 p.m.