# Fit new points to an existing NMDS configuration. Uses a brute force algorithm to find the location for each new point that minimizes overall stress. ```r addord(origconf, fulldat, fulldist, isTrain, bfstep = 10, maxit = 50, epsilon = 1e-12) ``` ## Arguments - `origconf`: The original ordination configuration. - `fulldat`: The dataset containing original and new points. - `fulldist`: A dissimilarity matrix calculated on `fulldat`. - `isTrain`: A boolean vector of length `nrow(fulldat)` indicating which rows were training data used in determining `origconf` (TRUE), or are new points (FALSE). - `bfstep`: A tuning parameter for the brute force algorithm describing the size of grid to use. - `maxit`: The maximum number of iterations to use. - `epsilon`: Tolerance value for convergence. ## Details A region comprising the original ordination configuration plus one standard deviation is divided into a grid of `bfstep` rows and columns. For a new point, the grid cell with the lowest stress is identified. That cell is divided into a finer grid, and the lowest-stress cell identified. This process is repeated up to `maxit` times, or until stress changes less than `epsilon`. ## Returns - **fullfitconf**: The new ordination configuration containing training and new points. - **stress**: The stress value for each point. - **isTrain**: The boolean vector indicating training set membership, for reference. ## Author(s) Sarah Goslee ## Examples ```r 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) # examine fit by number of dimensions plot(iris.nmds) # choose the best two-dimensional solution to work with iris.nmin <- min(iris.nmds, dims=2) # rotate the configuration to maximize variance iris.rot <- princomp(iris.nmin)$scores # rotation preserves distance apart in ordination space cor(dist(iris.nmin), dist(iris.rot)) # fit the data to the ordination as vectors ### vf() is timeconsuming, so this was generated ### in advance and saved. ### set.seed(1234) ### iris.vf <- vf(iris.nmin, iris[,1:4], nperm=1000) ### save(iris.vf, file="ecodist/data/iris.vf.rda") data(iris.vf) # repeat for the rotated ordination ### vf() is timeconsuming, so this was generated ### in advance and saved. ### set.seed(1234) ### iris.vfrot <- vf(iris.rot, iris[,1:4], nperm=1000) ### save(iris.vfrot, file="ecodist/data/iris.vfrot.rda") data(iris.vfrot) par(mfrow=c(1,2)) plot(iris.nmin, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="NMDS") plot(iris.vf) plot(iris.rot, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="Rotated NMDS") plot(iris.vfrot) ####### addord example # generate new data points to add to the ordination # this might be new samples, or a second dataset iris.new <- structure(list(Sepal.Length = c(4.6, 4.9, 5.4, 5.2, 6, 6.5, 6, 6.8, 7.3), Sepal.Width = c(3.2, 3.5, 3.6, 2.3, 2.8, 3, 2.7, 3.1, 3.2), Petal.Length = c(1.2, 1.5, 1.5, 3.5, 4.1, 4.2, 4.8, 5, 5.7), Petal.Width = c(0.26, 0.26, 0.26, 1.2, 1.3, 1.4, 1.8, 2, 2), Species = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("setosa", "versicolor", "virginica"), class = "factor")), .Names = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species"), class = "data.frame", row.names = c(NA, -9L)) # provide a dist object containing original and new data # provide a logical vector indicating which samples were used to # construct the original configuration iris.full <- rbind(iris, iris.new) all.d <- dist(iris.full[,1:4]) is.orig <- c(rep(TRUE, nrow(iris)), rep(FALSE, nrow(iris.new))) ### addord() is timeconsuming, so this was generated ### in advance and saved. ### set.seed(1234) ### iris.fit <- addord(iris.nmin, iris.full[,1:4], all.d, is.orig, maxit=100) ### save(iris.fit, file="ecodist/data/iris.fit.rda") data(iris.fit) plot(iris.fit$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) ```

addord function