pitprops14 {whitening}

Pitprops Correlation Data for 14 Variables

Description

Pit prop timber is used in construction to build mining tunnels. The pitprops14 data is described in Jeffers (1967) and is a correlation matrix that was calculated from measuring 14 physical properties of 180 pit props made from wood from Corsican pines grown in East Anglia, UK.

Usage

data(pitprops14)

Format

A correlation matrix of dimension 14 times 14.

Details

The 14 variables are described in Jeffers (1967) as follows:

topdiam: the top diameter of the prop in inches; length: the length of the prop in inches; moist: the moisture content of the prop, expressed as a percentage of the dry weight; testsg: the specific gravity of the timber at the time of the test; ovensg: the oven-dry specific gravity of the timber; rinotop: the number of annual rings at the top of the prop; ringbut: the number of annual rings at the base of the prop; bowmax: the maximum bow in inches; bowdist: the distance of the point of maximum bow from the top of the prop in inches; whorls: the number of knot whorls; clear: the length of clear prop from the top of the prop in inches; knots: the average number of knots per whorl; diaknot: the average diameter of the knots in inches; maxcs: the maximum compressive strength in lb per square inch.

Source

The data set is printed in Jeffers (1967) in Table 2 and Table 5.

References

Jeffers, J. N. R. 1967. Two case studies in the application of principal component analysis. JRSS C (Applied Statistics) 16: 225-236. <DOI:10.2307/2985919>

Examples

# load whitening library
library("whitening")

# load pitprops14 data set
data(pitprops14)
colnames(pitprops14)

# correlation matrix for the first 13 variables
pitprops13 = pitprops14[1:13, 1:13]

# correlation loadings for PCA whitening
Psi = whiteningLoadings(pitprops13, "PCA")$Psi

# corresponding explained variation
Psi.explained = explainedVariation(Psi)

# the first six whitened variables account for 87% of the variation
cumsum(Psi.explained)/13*100