dmbc (version 1.0.3)

bmds: Bayesian multidimensional scaling (BMDS) using Markov Chain Monte Carlo (MCMC).

Description

bmds computes the Bayesian multidimensional scaling (BMDS) solutions using Markov Chain Monte Carlo for a range of specified latent space dimensions.

Usage

bmds(
  D,
  min_p = 1,
  max_pm1 = 6,
  burnin = 0,
  nsim = 13000,
  ic = TRUE,
  verbose = TRUE
)

Value

A list with the following elements:

x.chain

MCMC chain of the latent configuration coordinates.

sigma.chain

MCMC chain of the random error.

lambda.chain

MCMC chain of the latent configuration variances.

stress

Numeric vector of the stress function values.

mdsIC

List with two elements, the MDSIC and BIC values for the required solutions.

accept

Numeric matrix of acceptance rates.

Arguments

D

Observed dissimilarities (provided as a distance matrix).

min_p

A length-one numeric vector providing the minimum value of the latent space dimension to use.

max_pm1

A length-one numeric vector providing the maximum value of the latent space dimension to use (minus 1).

burnin

A length-one numeric vector providing the number of iterations to use for burnin.

nsim

A length-one numeric vector providing the number of iterations to use in the MCMC simulation after burnin.

ic

Logical scalar. If TRUE computes the MDS information criterion (MDSIC) for all solution requested.

verbose

Logical scalar. If TRUE prints information regarding the evolution of the simulation.

Author

Sergio Venturini

References

Oh, M.-S., Raftery, A. E. (2001), "Bayesian Multidimensional Scaling and Choice of Dimension", Journal of the American Statistical Association, 96, 1031-1044.

See Also

cmdscale for classical (metric) multidimensional scaling.

Examples

if (FALSE) {
# Airline Distances Between Cities
airline <- read.csv(file = system.file("extdata", "airline.csv",
  package = "dmbc"))
airline.nm <- airline[, 1]
airline <- airline[, 2:31]
colnames(airline) <- airline.nm
airline <- as.dist(airline)

min_p <- 1
max_p <- 4
burnin <- 200
nsim <- 1000
totiter <- burnin + nsim

airline.mds <- cmdscale(airline, max_p)
airline.bmds <- bmds(airline, min_p, max_p, burnin, nsim)

opar <- par(mfrow = c(1, 2))
plot(min_p:max_p, airline.bmds$mdsIC$mdsic, type = "b",
  main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC")
MDSICmin <- which.min(airline.bmds$mdsIC$mdsic)
points((min_p:max_p)[MDSICmin], airline.bmds$mdsIC$mdsic[MDSICmin],
  col = "red", pch = 10, cex = 1.75, lwd = 1.5)

airline.bmds.x.mode <- bmds_get_x_mode(airline, airline.bmds, MDSICmin,
  min_p, max_p, start = (burnin + 1), end = totiter)
airline.bmds.d <- dist(airline.bmds.x.mode)
airline.mds.d <- dist(airline.mds[, 1:((min_p:max_p)[MDSICmin])])
plot(airline, airline.bmds.d, type = "n", xlab = "observed",
  ylab = "estimated", main = "Airline Distances \n Between Cities",
  xlim = c(0, max(airline, airline.bmds.d)),
  ylim = c(0, max(airline, airline.bmds.d)))
abline(0, 1, lty = 2, col = "gray")
points(airline, airline.mds.d, pch = 19, col = "cyan", cex = .5)
points(airline, airline.bmds.d, pch = 19, col = "magenta", cex = .5)
legend(x = "bottomright", legend = c("Classical MDS", "Bayesian MDS"),
  pch = c(19, 19), col = c("cyan", "magenta"))
par(opar)

# Careers of Lloyds Bank Employees
lloyds <- read.csv(file = system.file("extdata", "lloyds.csv",
  package = "dmbc"))
lloyds.nm <- lloyds[, 1]
lloyds <- lloyds[, 2:81]
colnames(lloyds) <- lloyds.nm
lloyds <- as.dist(lloyds)

min_p <- 1
max_p <- 12
burnin <- 200
nsim <- 1000
totiter <- burnin + nsim

lloyds.mds <- cmdscale(lloyds, max_p)
lloyds.bmds <- bmds(lloyds, min_p, max_p, burnin, nsim)

opar <- par(mfrow = c(1, 2))
plot((min_p:max_p), lloyds.bmds$mdsIC$mdsic, type = "b",
  main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC")
MDSICmin <- which.min(lloyds.bmds$mdsIC$mdsic)
points((min_p:max_p)[MDSICmin], lloyds.bmds$mdsIC$mdsic[MDSICmin],
  col = "red", pch = 10, cex = 1.75, lwd = 1.5)

lloyds.bmds.x.mode <- bmds_get_x_mode(lloyds, lloyds.bmds, MDSICmin,
  min_p, max_p, start = (burnin + 1), end = totiter)
lloyds.bmds.d <- dist(lloyds.bmds.x.mode)
lloyds.mds.d <- dist(lloyds.mds[, 1:((min_p:max_p)[MDSICmin])])
plot(lloyds, lloyds.bmds.d, type = "n", xlab = "observed",
  ylab = "estimated", main = "Careers of Lloyds \n Bank Employees, 1905-1950",
  xlim = c(0, max(lloyds, lloyds.bmds.d)),
  ylim = c(0, max(lloyds, lloyds.bmds.d)))
abline(0, 1, lty = 2, col = "gray")
points(lloyds, lloyds.mds.d, pch = 19, col = "cyan", cex = .5)
points(lloyds, lloyds.bmds.d, pch = 19, col = "magenta", cex = .5)
legend(x = "topleft", legend = c("Classical MDS", "Bayesian MDS"),
  pch = c(19, 19), col = c("cyan", "magenta"))
par(opar)

# Road distances (in km) between 21 cities in Europe
data(eurodist, package = "datasets")

min_p <- 1
max_p <- 10
burnin <- 200
nsim <- 1000
totiter <- burnin + nsim

eurodist.mds <- cmdscale(eurodist, max_p)
eurodist.bmds <- bmds(eurodist, min_p, max_p, burnin, nsim)

opar <- par(mfrow = c(1, 2))
plot((min_p:max_p), eurodist.bmds$mdsIC$mdsic, type = "b",
  main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC")
MDSICmin <- which.min(eurodist.bmds$mdsIC$mdsic)
points((min_p:max_p)[MDSICmin], eurodist.bmds$mdsIC$mdsic[MDSICmin],
  col = "red", pch = 10, cex = 1.75, lwd = 1.5)

eurodist.bmds.x.mode <- bmds_get_x_mode(eurodist, eurodist.bmds,
  MDSICmin, min_p, max_p, start = (burnin + 1), end = totiter)
eurodist.bmds.d <- dist(eurodist.bmds.x.mode)
eurodist.mds.d <- dist(eurodist.mds[, 1:((min_p:max_p)[MDSICmin])])
plot(eurodist, eurodist.bmds.d, type = "n", xlab = "observed",
  ylab = "estimated", main = "Road distances (in km) \n between 21 cities in Europe",
  xlim = c(0, max(eurodist, eurodist.bmds.d)),
  ylim = c(0, max(eurodist, eurodist.bmds.d)))
abline(0, 1, lty = 2, col = "gray")
points(eurodist, eurodist.mds.d, pch = 19, col = "cyan", cex = .5)
points(eurodist, eurodist.bmds.d, pch = 19, col = "magenta", cex = .5)
legend(x = "topleft", legend = c("Classical MDS", "Bayesian MDS"),
  pch = c(19, 19), col = c("cyan", "magenta"))
par(opar)
}