## ----echo = FALSE, message = FALSE, warning=FALSE-----------------------------
library(knitr)
knitr::opts_chunk$set(
   cache = FALSE,
   dpi = 75,
   fig.width = 6, fig.height = 6,
   fig.keep = 'high',
  # comment = "#>",
  tidy = FALSE)


## ----dataexample--------------------------------------------------------------
library("asymmetry")
data("studentmigration")
idx <- c(3,4,25,27,31) #select five countries
studentmigration[idx,idx]

## ----example------------------------------------------------------------------
q1 <- skewsymmetry(studentmigration[idx,idx])
q1$A

## ----symm---------------------------------------------------------------------
q1$S

## ----example2-----------------------------------------------------------------
summary(q1)

## ----clustering---------------------------------------------------------------
clus <- hclust(as.dist(1/q1$S))
plot(clus,xlab=NA,sub=NA)

## ----linear-------------------------------------------------------------------
   q1$linear

## ----example3, echo=TRUE------------------------------------------------------
hmap(q1, dominance = FALSE, col = cm.colors(n = 9))

## ----englishtowns, fig.width = 8, fig.height = 8------------------------------
data(Englishtowns)
v<-slidevector(Englishtowns, ndim = 2, itmax = 2500, eps = .0000001, verbose = FALSE)
plot(v, col = "blue", ylim = c(-300, 300), xlim = c(-300, 300))


## ----englishtownsdecomp-------------------------------------------------------

q2 <- skewsymmetry(v$resmat)
summary(q2)


## ----migrationunique----------------------------------------------------------

data("studentmigration")
mm<-studentmigration
mm[mm==0]<-.5          # replace zeroes by a small number
mm <- -log(mm/sum(mm)) # convert similarities to dissimilarities
v<-mdsunique(mm, ndim = 2, itmax = 2100, verbose=FALSE, eps = .0000000001)
plot(v, yplus = .3, ylim = c(-4.5, 4), xlim = c(-4.5, 4))