data(iris.fit)

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

### nmds() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4)
### save(iris.nmds, file="ecodist/data/iris.nmds.rda")
data(iris.nmds)

# choose the best two-dimensional solution to work with
iris.nmin <- min(iris.nmds, dims=2)

# generate new data points to add to the ordination
# this might be new samples, or a second dataset

iris.new <- structure(list(Sepal.Length = c(4.6, 4.9, 5.4, 5.2, 6, 6.5, 6, 
6.8, 7.3), Sepal.Width = c(3.2, 3.5, 3.6, 2.3, 2.8, 3, 2.7, 3.1, 
3.2), Petal.Length = c(1.2, 1.5, 1.5, 3.5, 4.1, 4.2, 4.8, 5, 
5.7), Petal.Width = c(0.26, 0.26, 0.26, 1.2, 1.3, 1.4, 1.8, 2, 
2), Species = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("setosa", 
"versicolor", "virginica"), class = "factor")), .Names = c("Sepal.Length", 
"Sepal.Width", "Petal.Length", "Petal.Width", "Species"), class = "data.frame",
row.names = c(NA, -9L))

# provide a dist object containing original and new data
# provide a logical vector indicating which samples were used to
# construct the original configuration

iris.full <- rbind(iris, iris.new)
all.d <- dist(iris.full[,1:4])
is.orig <- c(rep(TRUE, nrow(iris)), rep(FALSE, nrow(iris.new)))

### addord() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.fit <- addord(iris.nmin, iris.full[,1:4], all.d, is.orig, maxit=100)
### save(iris.fit, file="ecodist/data/iris.fit.rda")
data(iris.fit)

plot(iris.fit$conf, col=iris.full$Species, pch=c(18, 4)[is.orig + 1],
    xlab="NMDS 1", ylab="NMDS 2")
title("Demo: adding points to an ordination")
legend("bottomleft", c("Training set", "Added point"), pch=c(4, 18))
legend("topright", levels(iris$Species), fill=1:3)