machines | R Documentation |
A data set containing relative CPU performance data of 209 machines on 8 variables.
are predictive, one (PRP
) is the goal field and one (ERP
) is the
linear regression's guess. The estimated relative performance values were
estimated by the authors using a linear regression method. See their article
(Ein-Dor and Feldmesser, CACM 4/87, pp 308-317) for more details on how the
relative performance values were set.
data(machines)
A data frame with 209 rows and 8 variables The variables are as follows:
MMIN: minimum main memory in kilobytes (integer)
MMAX: maximum main memory in kilobytes (integer)
CACH: cache memory in kilobytes (integer)
CHMIN: minimum channels in units (integer)
CHMAX: maximum channels in units (integer)
PRP: published relative performance (integer)
ERP: estimated relative performance from the original article (integer)
Phillip Ein-Dor and Jacob Feldmesser (1987), Attributes of the performance of central processing units: A relative performance prediction model, Communications of the ACM, 30, 4, pp 308-317.
M. Hubert, P. J. Rousseeuw and T. Verdonck (2009), Robust PCA for skewed data and its outlier map, Computational Statistics & Data Analysis, 53, 2264–2274.
data(machines)
## Compute the medcouple of each variable of the Computer hardware data
data.frame(MC=round(apply(machines, 2, mc),2))
## Plot a pairwise scaterplot matrix
pairs(machines[,1:6])
mcd <- CovMcd(machines[,1:6])
plot(mcd, which="pairs")
## Remove the rownames (too long)
rownames(machines) <- NULL
## Start with robust PCA based on MCD (P << n)
(pca1 <- PcaHubert(machines, k=3))
plot(pca1, main="ROBPCA-MCD", off=0.03)
## PCA with the projection algoritm of Hubert
(pca2 <- PcaHubert(machines, k=3, mcd=FALSE))
plot(pca2, main="ROBPCA-SD", off=0.03)
## PCA with the adjusted for skewness algorithm of Hubert et al (2009)
(pca3 <- PcaHubert(machines, k=3, mcd=FALSE, skew=TRUE))
plot(pca3, main="ROBPCA-AO", off=0.03)
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