olitos: Olive Oil Data

Description Usage Format Source References Examples

Description

This dataset consists of 120 olive oil samples on measurements on 25 chemical compositions (fatty acids, sterols, triterpenic alcohols) of olive oils from Tuscany, Italy (Armanino et al. 1989). There are 4 classes corresponding to different production areas. Class 1, Class 2, Class 3, and Class 4 contain 50, 25, 34, and 11 observations, respectively.

Usage

1

Format

A data frame with 120 observations on the following 26 variables.

X1

Free fatty acids

X2

Refractive index

X3

K268

X4

delta K

X5

Palmitic acid

X6

Palmitoleic acid

X7

a numeric vector

X8

a numeric vector

X9

a numeric vector

X10

a numeric vector

X11

a numeric vector

X12

a numeric vector

X13

a numeric vector

X14

a numeric vector

X15

a numeric vector

X16

a numeric vector

X17

a numeric vector

X18

a numeric vector

X19

a numeric vector

X20

a numeric vector

X21

a numeric vector

X22

a numeric vector

X23

a numeric vector

X24

a numeric vector

X25

a numeric vector

grp

a factor with levels 1 2 3 4

Source

Prof. Roberto Todeschini, Milano Chemometrics and QSAR Research Group http://michem.disat.unimib.it/chm/download/datasets.htm

References

C. Armanino, R. Leardi, S. Lanteri and G. Modi, 1989. Chemometric analysis of Tuscan olive oils. Cbemometrics and Intelligent Laboratoty Sysiem, 5: 343–354.

R. Todeschini, V. Consonni, A. Mauri, M. Pavan (2004) Software for the calculation of molecular descriptors. Pavan M. Talete slr, Milan, Italy, http://www.talete.mi.it

Examples

1
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3
4
5
data(olitos)
cc <- CSimca(grp~., data=olitos, k=c(3,4,2,2))
cc
pr <- predict(cc, method=2)
tt <- rrcov::mtxconfusion(cc@grp, pr@classification, printit=TRUE)

Example output

Loading required package: rrcov
Loading required package: robustbase
Scalable Robust Estimators with High Breakdown Point (version 1.4-3)

Robust Multivariate Methods for High Dimensional Data (version 0.2-5)

Call:
CSimca(grp ~ ., data = olitos, k = c(3, 4, 2, 2))

Prior Probabilities of Groups:
         1          2          3          4 
0.41666667 0.20833333 0.28333333 0.09166667 

Pca objects for Groups:

Call:
PcaClassic(x = class, k = k[i], trace = trace)
Importance of components:
                           PC1     PC2     PC3
Standard deviation     31.2400 10.9234 7.57563
Proportion of Variance  0.8467  0.1035 0.04979
Cumulative Proportion   0.8467  0.9502 1.00000

Call:
PcaClassic(x = class, k = k[i], trace = trace)
Importance of components:
                          PC1     PC2     PC3     PC4
Standard deviation     30.789 8.08904 6.57069 5.52847
Proportion of Variance  0.872 0.06019 0.03971 0.02811
Cumulative Proportion   0.872 0.93217 0.97189 1.00000

Call:
PcaClassic(x = class, k = k[i], trace = trace)
Importance of components:
                           PC1    PC2
Standard deviation     25.7452 9.1190
Proportion of Variance  0.8885 0.1115
Cumulative Proportion   0.8885 1.0000

Call:
PcaClassic(x = class, k = k[i], trace = trace)
Importance of components:
                           PC1     PC2
Standard deviation     19.7700 12.2278
Proportion of Variance  0.7233  0.2767
Cumulative Proportion   0.7233  1.0000
                          
Apparent error rate 0.2917
Prior frequency.1   0.4167
Prior frequency.2   0.2083
Prior frequency.3   0.2833
Prior frequency.4   0.0917

Classification table 
      Predicted
Actual  1  2  3  4
     1 34  2  3 11
     2  3 18  0  4
     3  4  1 23  6
     4  1  0  0 10

Confusion matrix 
      Predicted
Actual     1     2     3     4
     1 0.680 0.040 0.060 0.220
     2 0.120 0.720 0.000 0.160
     3 0.118 0.029 0.676 0.176
     4 0.091 0.000 0.000 0.909

rrcovHD documentation built on May 29, 2017, 7:14 p.m.