# R/ContinuousDistances.R In MultBiplotR: Multivariate Analysis Using Biplots in R

#### Documented in ContinuousDistances

```# Autor: Jose Luis Vicente Villardon
# Gower, J. (2006) Similarity, dissimilarity and distance - Measures of. Encyclopedia of Statistical Sciences. Vol12. 2nd Edition. Wiley.
ContinuousDistances <- function(x, y=NULL,  coef = "Pythagorean", r = 1) {
distances = c("Pythagorean", "Taxonomic", "City", "Minkowski", "Divergence", "dif_sum", "Camberra", "Bray_Curtis", "Soergel", "Ware_Hedges", "Gower")
if (is.numeric(coef)) coef = distances[coef]
if (is.null(y)) y=x
n = nrow(x)
p = ncol(x)
s = nrow(y)
q=ncol(y)
NamesX=rownames(x)
NamesY=rownames(y)

print(NamesX)

if (coef=="Gower") rank=apply(rbind(y,x),2,max)-apply(rbind(y,x),2,min)
if (!(p==q)) stop("The matrices should have the same number of columns")

dis=matrix(0,s,n)
for (i in 1:s) for (j in 1:n) {
switch(coef, Pythagorean = {
dis[i, j] = sqrt(sum((y[i, ] - x[j, ])^2))
},Taxonomic = {
dis[i,j]=sqrt(sum(((y[i,]-x[j,])^2)/r^2))
},City = {
dis[i,j]=sum(abs(y[i,]-x[j,]))
},Minkowski = {
dis[i,j]=(sum(abs(y[i,]-x[j,])^r))^(1/r)
},Divergence = {
dis[i,j]=sqrt(sum((y[i,]-x[j,])^2/(y[i,]+x[j,])^2))
},dif_sum = {
dis[i,j]=sum(abs(y[i,]-x[j,])/abs(y[i,]+x[j,]))
},Camberra = {
dis[i,j]=sum(abs(y[i,]-x[j,])/(abs(y[i,])+abs(x[j,])))
},Bray_Curtis = {
dis[i,j]=sum(abs(y[i,]-x[j,]))/sum(y[i,]+x[j,])
},Soergel = {
dis[i,j]=sum(abs(y[i,]-x[j,]))/sum(apply(rbind(y[i,],x[j,]),2,max))
},Ware_Hedges = {
dis[i,j]=sum(1-apply(rbind(y[i,],x[j,]),2,min)/apply(rbind(y[i,],x[j,]),2,max))
},Gower = {
dis[i,j]=sum(abs(y[i,]-x[j,])/rank)
})
}

rownames(dis)=NamesY
colnames(dis)=NamesX
class(dis) = "proximities"
return(dis)
}
```

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MultBiplotR documentation built on April 6, 2021, 9:08 a.m.