INCA index

Description

INCAindex helps to estimate the number of clusters in a dataset.

Usage

INCAindex(d, pert_clus)

Arguments

Value

Returns an object of class incaix which is a list containing the following components:

Note

For a correct geometrical interpretation it is convenient to verify whether the distance matrix d is Euclidean. It admits the associated methods summary and plot. The first simply returns the percentage of well-classified units and the second offers a barchart with the percentages of well classified units for each group in the given partition.

Author(s)

Itziar Irigoien itziar.irigoien@ehu.eus; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain.

Conchita Arenas carenas@ub.edu; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Arenas, C. and Cuadras, C.M. (2002). Some recent statistical methods based on distances. Contributions to Science, 2, 183–191.

Irigoien, I. and Arenas, C. (2008). INCA: New statistic for estimating the number of clusters and identifying atypical units. Statistics in Medicine, 27(15), 2948–2973.

See Also

estW, INCAtest

Examples

#generate 3 clusters, each of them with 20 objects in dimension 5.
mu1 <- sample(1:10, 5, replace=TRUE)
x1 <- matrix(rnorm(20*5, mean = mu1, sd = 1),ncol=5, byrow=TRUE)
mu2 <- sample(1:10, 5, replace=TRUE)
x2 <- matrix(rnorm(20*5, mean = mu2, sd = 1),ncol=5, byrow=TRUE)
mu3 <- sample(1:10, 5, replace=TRUE)
x3 <- matrix(rnorm(20*5, mean = mu3, sd = 1),ncol=5, byrow=TRUE)
x <- rbind(x1,x2,x3)

# Euclidean distance between units.
d <- dist(x)

# given the right partition, calculate the percentage of well classified objects.
partition <- c(rep(1,20), rep(2,20), rep(3,20))
INCAindex(d, partition)


# In order to estimate the number of cluster in data, try several 
#  partitions and compare the results
library(cluster)
T <- rep(NA, 5)
for (l in 2:5){
	part <- pam(d,l)$clustering
	T[l] <- INCAindex(d,part)$Total
}

plot(T, type="b",xlab="Number of clusters", ylab="INCA", xlim=c(1.5, 5.5))

Estimation of Number of Clusters in Data

Description

INCAnumclu helps to estimate the number of clusters in a dataset. The INCA index associated to different partitions with different number of clusters is calculated.

Usage

INCAnumclu(d, K, method = "pam", pert, L= NULL, noise=NULL)

Arguments

Value

Returns an object of class incanc which is a numeric vector containing the INCA index associated to each of the k (k=2,...,K) partitions. When noise is no null, the function returns a list with the INCA index for each partition, which is calculated without noise units as well as with noise units. The associated plot returns INCA index plot, both, with and without noise.

Author(s)

Itziar Irigoien itziar.irigoien@ehu.eus; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain.

Conchita Arenas carenas@ub.edu; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Irigoien, I. and Arenas, C. (2008). INCA: New statistic for estimating the number of clusters and identifying atypical units. Statistics in Medicine, 27(15), 2948–2973.

Arenas, C. and Cuadras, C.M. (2002). Some recent statistical methods based on distances. Contributions to Science, 2, 183–191.

See Also

INCAindex, estW

Examples

#------- Example 1 --------------------------------------
#generate 3 clusters, each of them with 20 objects in dimension 5.
mu1 <- sample(1:10, 5, replace=TRUE)
x1 <- matrix(rnorm(20*5, mean = mu1, sd = 1),ncol=5, byrow=TRUE)
mu2 <- sample(1:10, 5, replace=TRUE)
x2 <- matrix(rnorm(20*5, mean = mu2, sd = 1),ncol=5, byrow=TRUE)
mu3 <- sample(1:10, 5, replace=TRUE)
x3 <- matrix(rnorm(20*5, mean = mu3, sd = 1),ncol=5, byrow=TRUE)
x <- rbind(x1,x2,x3)

# calculte euclidean distance between them
d <- dist(x)

# calculate the INCA index associated to partitions with k=2, ..., k=5 clusters.
INCAnumclu(d, K=5)
out <- INCAnumclu(d, K=5)
plot(out)

#------- Example 1 cont. --------------------------------
# With hypothetical noise elements
noiseunits <- rep(FALSE, 60)
noiseunits[sample(1:60, 20)] <- TRUE
out <- INCAnumclu(d, K=5, L="custom", noise=noiseunits)
plot(out)

INCA Test

Description

Assume that n units are divided into k groups C1,...,Ck. Function INCAtest performs the typicality INCA test. Therein, the null hypothesis that a new unit x0 is a typical unit with respect to a previously fixed partition is tested versus the alternative hypothesis that the unit is atypical.

Usage

INCAtest(d, pert, d_test, np = 1000, alpha = 0.05, P = 1)

Arguments

Value

A list with class "incat" containing the following components:

Note

To obtain the INCA statistic distribution, under the null hypothesis, the program can consume long time. For a correct geometrical interpretation it is convenient to verify whether the distance matrix d is Euclidean.

Author(s)

Itziar Irigoien itziar.irigoien@ehu.es; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV-EHU), Donostia, Spain.

Conchita Arenas carenas@ub.edu; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Irigoien, I. and Arenas, C. (2008). INCA: New statistic for estimating the number of clusters and identifying atypical units. Statistics in Medicine, 27(15), 2948–2973.

Arenas, C. and Cuadras, C.M. (2002). Some recent statistical methods based on distances. Contributions to Science, 2, 183–191.

See Also

estW, INCAindex

Examples

#generate 3 clusters, each of them with 20 objects in dimension 5.
mu1 <- sample(1:10, 5, replace=TRUE)
x1 <- matrix(rnorm(20*5, mean = mu1, sd = 1),ncol=5, byrow=TRUE)
mu2 <- sample(1:10, 5, replace=TRUE)
x2 <- matrix(rnorm(20*5, mean = mu2, sd = 1),ncol=5, byrow=TRUE)
mu3 <- sample(1:10, 5, replace=TRUE)
x3 <- matrix(rnorm(20*5, mean = mu3, sd = 1),ncol=5, byrow=TRUE)
x <- rbind(x1,x2,x3)

# Euclidean distance between units in matrix x.
d <- dist(x)
# given the right partition
partition <- c(rep(1,20), rep(2,20), rep(3,20))

# x0 contains a unit from one group, as for example group 1.
x0 <-  matrix(rnorm(1*5, mean = mu1, sd = 1),ncol=5, byrow=TRUE)

# distances between x0 and the other units.
dx0 <- rep(0,60)
for (i in 1:60){
	dif <-x0-x[i,]
	dx0[i] <- sqrt(sum(dif*dif))
}

INCAtest(d, partition, dx0, np=10)


# x0 contains a unit from a new group.
x0 <-  matrix(rnorm(1*5, mean = sample(1:10, 5, replace=TRUE),
        sd = 1), ncol=5, byrow=TRUE)

# distances between x0 and the other units in matrix x.
dx0 <- rep(0,60)
for (i in 1:60){
	dif <-x0-x[i,]
	dx0[i] <- sqrt(sum(dif*dif))
}

INCAtest(d, partition, dx0, np=10)

Synthetic Time Course data

Description

Sythetic time course data where 210 genes profiles along 6 time points are reported and where the genes are drawn from 8 different populations.

Usage

data(SyntheticTimeCourse)

Format

Data frame with 120 rows and 7 columns.

Details

Attribute information: Column cl: the class that the gen belongs to. Columns t1 - t6: gene's expression along the t1, ..., t6 time points considered.

Examples

data(SyntheticTimeCourse)
x <- SyntheticTimeCourse[, 2:7]
cl <- SyntheticTimeCourse[, 1]
par(mfrow=c(3,3))
for (g in 1:8){ 
   xx <- t(x[cl==g,] )
   yy <- matrix(c(1:6 ), nrow=6, ncol=15, byrow=FALSE)
   matplot(yy,xx,  pch=21, type="b", axes=FALSE,  
        ylim=c(0,3.5), xlim=c(0.5,6.5), xlab="", ylab="", col="black", main=paste("G",g)) 
   abline(h=0)  
   abline(v=0.5) 
   mtext("Time", side=1) 
   mtext("Expression", side=2)  
}

Chowdary Database

Description

The original authors compared pairs of snap-frozen and RNAlater preservative-suspended tissue from lymph node-negative breast tumors (B) and Dukes' B colon tumors (C). The actual data set, by de Souto et. al (2008), is build with purpose of separating B from C.

Usage

data(chowdary)

Format

Data frame with 183 rows and 104 columns.

Source

Original source from ‘National Center for Biotechnology Information’ from the United States of America, query GSE3726.

References

de Souto MCP, Costa IG, de Araujo DSA, Ludermir TB, and Schliep A (2008). Clustering Cancer Gene Expression Data: a Comparative Study. BMC Bioinformatics, 8, 497–511.

Chowdary D, Lathrop J, Skelton J, Curtin K, Briggs T, Zhang Y, Yu J, Wang X, and Mazumder A (2006). Prognostic gene expression signatures can be measured in tissues collected in RNAlater preservative. Journal Molecular Diagnosis, 8, 31–39.

Examples

data(chowdary)

tumor <- as.factor(as.matrix(chowdary[1,]))
x <- as.matrix(chowdary[-1,])
mode(x) <- "numeric"

s <- sample(row.names(x),1)
boxplot( x[s,] ~ tumor , ylab=s)

Bhattacharyya Distance

Description

dbhatta computes and returns the Bhattacharyya distance matrix between the rows of a data matrix. This distance is defined between two units i=(p_{i1},...,p_{im}) and j=(p_{j1},...,p_{jm}) being p_{kl} frequencies with p_{kl}>=0 and p_{k1}+...+p_{km}=1.

Usage

dbhatta(x)

Arguments

Value

A dist object with distance information.

Author(s)

Itziar Irigoien itziar.irigoien@ehu.eus; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV-EHU), Donostia, Spain.

Conchita Arenas carenas@ub.edu; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Bhattacharyya, A. (1946). On a measure of divergence of two multinomial populations. Sankhya: The Indian Journal of Statistics, Series A. 14, 177-136.

See Also

dist, dmahal, dgower, dcor, dproc2

Examples

#5 individuals represented by their relative frequencies of 4 characteristics (M1-M4):
f <- matrix(c(0.36, 0.21, 0.23, 0.20,
              0.66, 0.18, 0.11, 0.05,
              0.01, 0.24, 0.62, 0.13,
              0.43, 0.38, 0.08, 0.11,
              0.16, 0.07, 0.09, 0.68), 
              byrow=TRUE, nrow=5, dimnames=list(1:5, paste("M", 1:4, sep="")))

# Bhattacharyya distances between pairs 
d <- dbhatta(f)

Correlation Distance

Description

dcor computes and returns the Correlation distance matrix between the rows of a data matrix. This distance is defined by d=\sqrt{1-r}.

Usage

dcor(x)

Arguments

Value

A dist object with distance information.

Author(s)

Itziar Irigoien itziar.irigoien@ehu.eus; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV-EHU), Donostia, Spain.

Conchita Arenas carenas@ub.edu; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Gower, J.C. (1985). Measures of similarity, dissimilarity and distance. In: Encyclopedia of Statistical Sciences, volume 5, 397–405. J. Wiley and Sons.

See Also

dist, dmahal, dgower, dbhatta, dproc2

Examples

#Generate 10 objects in dimension 8
n <- 10
mu <- sample(1:10, 8, replace=TRUE)
x <- matrix(rnorm(n*8, mean=mu, sd=1), nrow=n, byrow=TRUE)


# Correlation distances between pairs 
d <- dcor(x)

Distance Between Groups

Description

Assume that n units are divided into k groups C1,...,Ck . Function deltas computes and returns the distance between each pair of groups. It uses the distances between pairs of units.

Usage

deltas(d, pert = "onegroup")

Arguments

Value

A matrix containing the distances between each pair of groups.

Author(s)

Itziar Irigoien itziar.irigoien@ehu.eus; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain.

Conchita Arenas carenas@ub.edu; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Arenas, C. and Cuadras, C.M. (2002). Some recent statistical methods based on distances. Contributions to Science, 2, 183–191.

Cuadras, C.M., Fortiana, J. and Oliva, F. (1997). The proximity of an individual to a population with applications in discriminant analysis. Journal of Classification, 14, 117–136.

See Also

vgeo, proxi

Examples

data(iris)
d <- dist(iris[,1:4])
deltas(d,iris[,5])

Dermatology Database

Description

Data from a dermatology study provided by H.A. Guvenir (Dpt. Computer Engineering and Information Science, Bilkent University, Turkey).The data set contains 366 instances presenting 34 different clinical attributes (12 clinical features as age or family history and 22 histopathological features obtained from a biopsy), and a class variable indicating the disease. There are 8 missing values. This data set has been used extensively for classification tasks.

Usage

data(dermatology)

Format

Matrix with 366 rows.

Details

Attribute information obtained from the UCI KDD data repository:

Clinical Attributes: (they take values 0, 1, 2, 3, unless otherwise indicated)

1: erythema; 2: scaling; 3: definite borders; 4: itching; 5: koebner phenomenon; 6: polygonal papules; 7: follicular papules; 8: oral mucosal involvement; 9: knee and elbow involvement; 10: scalp involvement; 11: family history, (0 or 1); 34: Age.

Histopathological Attributes: (they take values 0, 1, 2, 3)

12: melanin incontinence; 13: eosinophils in the infiltrate; 14: PNL infiltrate; 15: fibrosis of the papillary dermis; 16: exocytosis; 17: acanthosis; 18: hyperkeratosis; 19: parakeratosis; 20: clubbing of the rete ridges; 21: elongation of the rete ridges; 22: thinning of the suprapapillary epidermis; 23: spongiform pustule; 24: munro microabcess; 25: focal hypergranulosis; 26: disappearance of the granular layer; 27: vacuolisation and damage of basal layer; 28: spongiosis; 29: saw-tooth appearance of retes; 30: follicular horn plug; 31: perifollicular parakeratosis; 32: inflammatory monoluclear inflitrate; 33: band-like infiltrate.

The considered diseases are: 1 - psoriasis, 2 - seboreic dermatitis, 3- lichen planus, 4 - pityriasis rosea, 5 - chronic dermatitis, 6 - pityriasis rubra pilaris.

Source

The UCI KDD Archive.

References

Guvenir H, Demiroz G, Ilter N (1998). Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artificial Intelligence in Medicine, 13, 147–165.

Irigoien I, Arenas C (2008). INCA: New statistic for estimating the number of clusters and identifying atypical units. Statistics in Medicine, 27, 2948–2973.

Examples

data(dermatology)
x <- dermatology[, 1:34]
group <- as.factor(dermatology[,35])

plot(group)

Gower Distance for Mixed Variables

Description

dgower computes and returns the Gower distance matrix for mixed variables.

Usage

dgower(x, type = list())

Arguments

Details

The distance between two pairs of objects i and j is obtained as \sqrt{2(1-s_{ij})} where s_{ij} is the Gower's similarity coefficient for mixed data. This function allows to include missing values (as NA) and therefore calculates distances based on Gower's weighted similarity coefficient.

Value

A dist object with distance information.

Note

There is the function daisy() in cluster package which can perform the Gower distance for mixed variables. The difference is that in daisy() the distance is calculated as d(i,j)=1-s_{ij} and in dgower() it is calculated as dij=sqrt(1-s_{ij}).

Author(s)

Itziar Irigoien itziar.irigoien@ehu.eus; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain.

Conchita Arenas carenas@ub.edu; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Gower, J.C. (1971). A general coefficient of similarity and some of its properties. Biometrics, 27, 857–871.

See Also

dist, dmahal, dbhatta, dcor, dproc2

Examples

#Generate 10 objects in dimension 6
# Quantitative variables
mu <- sample(1:10, 2, replace=TRUE)
xc <- matrix(rnorm(10*2, mean = mu, sd = 1), ncol=2, byrow=TRUE)

# Binary variables
xb <- cbind(rbinom(10, 1, 0.1), rbinom(10, 1, 0.5), rbinom(10, 1, 0.9))

# Nominal variables
xn <- matrix(sample(1:3, 10, replace=TRUE), ncol=1)

x <- cbind(xc, xb, xn)

# Distances
d <- dgower(x, type=list(cuant=1:2, bin=3:5, nom=6))

Mahalanobis Distance

Description

dmahal computes and returns the Mahalanobis distance matrix between the rows of a data matrix.

Usage

dmahal(datos, S)

Arguments

Value

A dist object with distance information.

Note

There is a function mahalanobis() in stats package which can perform the Mahalanobis distance. While mahalanobis() calculates the Mahalanobis distance with respect to given a center, function dmahal() is designed to calculate the distance between each pair of units given a data matrix.

Author(s)

Itziar Irigoien itziar.irigoien@ehu.eus; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain.

Conchita Arenas carenas@ub.edu; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Everitt B. S. and Dunn G. (2001) Applied Multivariate Data Analysis. 2 edition, Edward Arnold, London.

See Also

dist, dbhatta, dgower, dcor, dproc2

Examples

#Generate 10 objects in dimension 2
mu <- rep(0, 2)
Sigma <- matrix(c(10,3,3,2),2,2)

x <- mvrnorm(n=10, rep(0, 2), Sigma)

d <- dmahal(x, Sigma)

Modified Procrustes distance

Description

dproc2 computes and returns all the pairwise procrustes distances between genes in a time course experiment, using their expression profile.

Usage

dproc2(x, timepoints = NULL)

Arguments

Details

Each row i of matrix x is arranged in a two column matrix Xi. In Xi, the first column contains the time points and the second column the observed gene expression values (xi1...).

Value

A dist object with distance information.

Author(s)

Itziar Irigoien itziar.irigoien@ehu.eus; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain.

Conchita Arenas carenas@ub.edu; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Irigoien, I. , Vives, S. and Arenas, C. (2011). Microarray Time Course Experiments: Finding Profiles. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(2), 464–475.

Gower, J. C. and Dijksterhuis, G. B. (2004) Procrustes Problems. Oxford University Press.

Sibson, R. (1978). Studies in the Robustness of Multidimensional Scaling: Procrustes statistic. Journal of the Royal Statistical Society, Series B, 40, 234–238.

See Also

dist, dmahal, dgower, dcor dbhatta

Examples

# Given  10  hypothetical time course profiles
# over 6 time points at 1, 2, ..., 6 hours.
x <- matrix(c(0.38, 0.39, 0.38, 0.37, 0.385, 0.375,
              0.99, 1.19, 1.50, 1.83, 2.140, 2.770,
              0.38, 0.50, 0.71, 0.72, 0.980, 1.010,
              0.20, 0.40, 0.70, 1.06, 2.000, 2.500,
              0.90, 0.95, 0.97, 1.50, 2.500, 2.990,
              0.64, 2.61, 1.51, 1.34, 1.330 ,1.140,
              0.71, 1.82, 2.28, 1.72, 1.490, 1.060,
              0.71, 1.82, 2.28, 1.99, 1.975, 1.965,
              0.49, 0.78, 1.00, 1.27, 0.590, 0.340,
              0.71,1.00, 1.50, 1.75, 2.090, 1.380), nrow=10, byrow=TRUE)

# Graphical representation
matplot(t(x), type="b")

# Distance matrix between them
d <- dproc2(x)

INCA Statistic

Description

Assume that n units are divided into k clusters C1,...,Ck, and consider a fixed unit x0. Function estW calculates the INCA statistic W(x0) and the related U_i statistics.

Usage

estW(d, dx0, pert = "onegroup")

Arguments

Value

The function returns an object of class incaest which is a list containing the following components:

Note

For a correct geometrical interpretation it is convenient to verify whether the distance matrix d is Euclidean.

Author(s)

Itziar Irigoien itziar.irigoien@ehu.eus; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain.

Conchita Arenas carenas@ub.edu; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Arenas, C. and Cuadras, C.M. (2002). Some recent statistical methods based on distances. Contributions to Science, 2, 183–191.

Irigoien, I. and Arenas, C. (2008). INCA: New statistic for estimating the number of clusters and identifying atypical units. Statistics in Medicine, 27(15), 2948–2973.

See Also

vgeo, proxi , deltas

Examples

data(iris)
d <- dist(iris[,1:4])

# characteristics of a specific flower (likely group 1)
x0 <- c(5.3, 3.6, 1.1, 0.1) 
# distances between  flower x0 and the rest of flowers in iris
dx0 <- rep(0,150)
for (i in 1:150){
	dif <-x0-iris[i,1:4]
	dx0[i] <- sqrt(sum(dif*dif))
}
estW(d, dx0, iris[,5])

Limphatic Database

Description

This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data. The data are available at the UCI KDD data repository (Hettich S and Bay SD, 1999).

The data set consists of 148 instances presenting 18 different mixed attributes (1 cuantitative, 9 binaries and 9 nominals), and a class variable indicating the diagnostic. There are not missing values.

Usage

data(lympha)

Format

Data frame with 148 instances and 19 features.

Details

Attribute information:

— NOTE: All attribute values in the database have been entered as numeric values corresponding to their index in the list of attribute values for that attribute domain as given below.

1. class: normal find, metastases, malign lymph, fibrosis

2. lymphatics: normal, arched, deformed, displaced

3. block of affere: no, yes

4. bl. of lymph. c: no, yes

5. bl. of lymph. s: no, yes

6. by pass: no, yes

7. extravasates: no, yes

8. regeneration of: no, yes

9. early uptake in: no, yes

10. lym.nodes dimin: 0-3

11. lym.nodes enlar: 1-4

12. changes in lym.: bean, oval, round

13. defect in node: no, lacunar, lac. marginal, lac. central

14. changes in node: no, lacunar, lac. margin, lac. central

15. changes in stru: no, grainy, drop-like, coarse, diluted, reticular, stripped, faint

16. special forms: no, chalices, vesicles

17. dislocation of: no, yes

18. exclusion of no: no, yes

19. no. of nodes in: 0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, >=70

Source

The UCI KDD Archive.

References

Hettich S and Bay SD (1999). The UCI KDD Archive. Department of Information and Computer Science. University of California at Irvine, Irvine, USA.

Examples

data(lympha)
aux <- table(lympha[,1])
barplot(aux, names.arg=c("normal", "metastases", "malign lymph", "fibrosis"))

Proximity Function

Description

Assume that n units are divided into k groups C1,...,Ck. The function calculates the proximity function from a specific unit x0 to the groups Cj.

Usage

proxi(d, dx0, pert = "onegroup")

Arguments

Value

k-vector containing the proximity function value from x0 to each group.

Author(s)

Itziar Irigoien itziar.irigoien@ehu.eus; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain.

Conchita Arenas carenas@ub.edu; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Arenas, C. and Cuadras, C.M. (2002). Some recent statistical methods based on distances. Contributions to Science, 2, 183–191.

Cuadras, C.M., Fortiana, J. and Oliva, F. (1997). The proximity of an individual to a population with applications in discriminant analysis. Journal of Classification, 14, 117–136.

See Also

vgeo, deltas

Examples

data(iris)
d <- dist(iris[,1:4])

# xo contains a unit from one group, as for example group 1.
x0 <- c(5.3, 3.6, 1.1, 0.1) 
# distances between x0 and the other units.
dx0 <- rep(0,150)
for (i in 1:150){
	dif <-x0-iris[i,1:4]
	dx0[i] <- sqrt(sum(dif*dif))
}

proxi(d, dx0, iris[,5])


# xo contains a unit from one group, as for example group 2.
x0 <- c(6.4, 3.0, 4.8, 1.3) 
# distances between x0 and the other units.
dx0 <- rep(0,150)
for (i in 1:150){
	dif <-x0-iris[i,1:4]
	dx0[i] <- sqrt(sum(dif*dif))
}

proxi(d, dx0, iris[,5])

Geometric Variability

Description

Assume that n units are divided into k groups C1,...,Ck. The function calculates the geometrical variability for each group in data.

Usage

vgeo(d, pert = "onegroup")

Arguments

Value

It is a matrix containing the geometric variability for each group.

Author(s)

Itziar Irigoien itziar.irigoien@ehu.eus; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain.

Conchita Arenas carenas@ub.edu; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Arenas, C. and Cuadras, C.M. (2002). Some recent statistical methods based on distances. Contributions to Science, 2, 183–191.

Cuadras, C.M. (1992). Some examples of distance based discrimination. Biometrical Letters, 29(1), 3–20.

See Also

deltas, proxi

Examples

data(iris)
d <- dist(iris[,1:4])
vgeo(d,iris[,5])