# Calculates the best overlap Uses multiple datasets, measures overlaps between class-related convex hulls and reports the best dataset, the best overlap table and summary with confidence intervals. Can be used to assess bootstrap or jackknife results, to compare different dimension reduction and/or clustering methods, and to average results of stochastic methods. ```r BestOverlap(xylabels, ci="95%", round=4) ``` ## Arguments - `xylabels`: List of data frames, each with at least 3 columns named exactly as: "x" for x coordinates, "y" for y coordinates and "labels" for class labels - `ci`: Confidence interval (character string with percent sign) - `round`: How to round numbers in summary table ## Details 'BestOverlap()' requires object, typically created after bootstrapping or similar procedure (see below for examples). This 'xylabels' object must contain at least three columns named exactly as c("x", "y", "labels"), in any order. Please note that label types must be the same between data frames inside 'xylabels' list. For consistency, first data frame is used as a label standard. If any next data frame contain label types different from standard, it will be ignored. ## Returns List with three components: 'best' data frame, 'best.overlap' table and 'summary' data frame. ## Author(s) Alexey Shipunov ## Examples ```r ## Bootstrap PCA B <- 100 xylabels <- vector("list", length=0) for (n in 1:B) { ROWS <- sample(nrow(iris), replace=TRUE) tmp <- prcomp(iris[ROWS, -5])$x[, 1:2] xylabels[[n]] <- data.frame(x=tmp[, 1], y=tmp[, 2], labels=iris[ROWS, 5]) } BestOverlap(xylabels) ## Jacknife PCA B <- nrow(iris) xylabels <- vector("list", length=0) for (n in 1:B) { ROWS <- (1:B)[-n] tmp <- prcomp(iris[ROWS, -5])$x[, 1:2] xylabels[[n]] <- data.frame(x=tmp[, 1], y=tmp[, 2], labels=iris[ROWS, 5]) } BestOverlap(xylabels) ## Stochastic method: Stochastic Proximity Embedding library(tapkee) B <- 100 xylabels <- vector("list", length=0) for (n in 1:B) { tmp <- Tapkee(iris[, -5], method="spe") xylabels[[n]] <- data.frame(x=tmp[, 1], y=tmp[, 2], labels=iris[, 5]) } BestOverlap(xylabels) ## Diverse dimension reduction methods library(tapkee) B <- c("lle", "npe", "ltsa", "lltsa", "hlle", "la", "lpp", "dm", "isomap", "l-isomap") xylabels <- vector("list", length=0) for (n in B) { tmp <- Tapkee(iris[, -5], method=n, add="-k 50") xylabels[[n]] <- data.frame(x=tmp[, 1], y=tmp[, 2], labels=iris[, 5]) } BestOverlap(xylabels) ## One dimension reduction but many clusterings B <- 100 xylabels <- vector("list", length=0) tmp1 <- prcomp(iris[, -5])$x[, 1:2] for (n in 1:B) { tmp2 <- kmeans(iris[, -5], centers=3)$cluster xylabels[[n]] <- data.frame(x=tmp1[, 1], y=tmp1[, 2], labels=letters[tmp2]) } BestOverlap(xylabels) ```

BestOverlap function