R: Gap Statistic for Estimating the Number of Clusters

clusGap {cluster} | R Documentation

## Gap Statistic for Estimating the Number of Clusters

### Description

`clusGap()` calculates a goodness of clustering measure, the “gap” statistic. For each number of clusters k, it compares \log(W(k)) with E^*[\log(W(k))] where the latter is defined via bootstrapping, i.e. simulating from a reference distribution.

`maxSE(f, SE.f)` determines the location of the maximum of `f`, taking a “1-SE rule” into account for the `*SE*` methods. The default method `"firstSEmax"` looks for the smallest k such that its value f(k) is not more than 1 standard error away from the first local maximum. This is similar but not the same as `"Tibs2001SEmax"`, Tibshirani et al's recommendation of determining the number of clusters from the gap statistics and their standard deviations.

### Usage

    clusGap(x, FUNcluster, K.max, B = 100, verbose = interactive(), ...)

    maxSE(f, SE.f,
          method = c("firstSEmax", "Tibs2001SEmax", "globalSEmax",
                     "firstmax", "globalmax"),
          SE.factor = 1)
    ## S3 method for class 'clusGap'
    print(x, method = "firstSEmax", SE.factor = 1, ...)

### Arguments

`x` | numeric matrix or `data.frame`.
`FUNcluster` | a `function` which accepts as first argument a (data) matrix like `x`, second argument, say k, k >= 2, the number of clusters desired, and returns a `list` with a component named (or shortened to) `cluster` which is a vector of length `n = nrow(x)` of integers in `1:k` determining the clustering or grouping of the `n` observations.
`K.max` | the maximum number of clusters to consider, must be at least two.
`B` | integer, number of Monte Carlo (“bootstrap”) samples.
`verbose` | integer or logical, determining if “progress” output should be printed. The default prints one bit per bootstrap sample.
`...` | optionally further arguments for `FUNcluster()`, see `kmeans` example below.
`f` | numeric vector of ‘function values’, of length K, whose (“1 SE respected”) maximum we want.
`SE.f` | numeric vector of length K of standard errors of `f`.
`method` | character string indicating how the “optimal” number of clusters, k^, is computed from the gap statistics (and their standard deviations), or more generally how the location k^ of the maximum of f[k] should be determined. `"globalmax"`: simply corresponds to the global maximum, i.e., is `which.max(f)` `"firstmax"`: gives the location of the first local maximum. `"Tibs2001SEmax"`: uses the criterion, Tibshirani et al (2001) proposed: “the smallest k such that f(k) ≥ f(k+1) - s_{k+1}”. Note that this chooses k = 1 when all standard deviations are larger than the differences f(k+1) - f(k). `"firstSEmax"`: location of the first f() value which is not larger than the first local maximum minus `SE.factor * SE.f[]`, i.e, within an “f S.E.” range of that maximum (see also `SE.factor`). `"globalSEmax"`: (used in Dudoit and Fridlyand (2002), supposedly following Tibshirani's proposition): location of the first f() value which is not larger than the global maximum minus `SE.factor * SE.f[]`, i.e, within an “f S.E.” range of that maximum (see also `SE.factor`). See the examples for a comparison in a simple case.
`SE.factor` | [When `method` contains `"SE"`] Determining the optimal number of clusters, Tibshirani et al. proposed the “1 S.E.”-rule. Using an `SE.factor` f, the “f S.E.”-rule is used, more generally.

### Details

The main result `<res>$Tab[,"gap"]` of course is from bootstrapping aka Monte Carlo simulation and hence random, or equivalently, depending on the initial random seed (see `set.seed()`). On the other hand, in our experience, using `B = 500` gives quite precise results such that the gap plot is basically unchanged after an another run.

### Value

an object of S3 class `"clusGap"`, basically a list with components

`Tab` | a matrix with `K.max` rows and 4 columns, named "logW", "E.logW", "gap", and "SE.sim", where `gap = E.logW - logW`, and `SE.sim` corresponds to the standard error of `gap`, `SE.sim[k]=`s[k], where s[k] := sqrt(1 + 1/B) sd^*(gap[]), and sd^*() is the standard deviation of the simulated (“bootstrapped”) gap values.
`n` | number of observations, i.e., `nrow(x)`.
`B` | input `B`
`FUNcluster` | input function `FUNcluster`

### Author(s)

This function is originally based on the functions `gap` of (Bioconductor) package SAGx by Per Broberg, `gapStat()` from former package SLmisc by Matthias Kohl and ideas from `gap()` and its methods of package lga by Justin Harrington.

The current implementation is by Martin Maechler.

### References

Tibshirani, R., Walther, G. and Hastie, T. (2001). Estimating the number of data clusters via the Gap statistic. Journal of the Royal Statistical Society B, 63, 411–423.

Tibshirani, R., Walther, G. and Hastie, T. (2000). Estimating the number of clusters in a dataset via the Gap statistic. Technical Report. Stanford.

Per Broberg (2006). SAGx: Statistical Analysis of the GeneChip. R package version 1.9.7. http://home.swipnet.se/pibroberg/expression_hemsida1.html

### See Also

`silhouette` for a much simpler less sophisticated goodness of clustering measure.

`cluster.stats()` in package fpc for alternative measures.

### Examples

    ### --- maxSE() methods -------------------------------------------
    (mets <- eval(formals(maxSE)$method))
    fk <- c(2,3,5,4,7,8,5,4)
    sk <- c(1,1,2,1,1,3,1,1)/2
    ## use plot.clusGap():
    plot(structure(class="clusGap", list(Tab = cbind(gap=fk, SE.sim=sk))))
    ## Note that 'firstmax' and 'globalmax' are always at 3 and 6 :
    sapply(c(1/4, 1,2,4), function(SEf)
            sapply(mets, function(M) maxSE(fk, sk, method = M, SE.factor = SEf)))

    ### --- clusGap() -------------------------------------------------
    ## ridiculously nicely separated clusters in 3 D :
    x <- rbind(matrix(rnorm(150,           sd = 0.1), ncol = 3),
               matrix(rnorm(150, mean = 1, sd = 0.1), ncol = 3),
               matrix(rnorm(150, mean = 2, sd = 0.1), ncol = 3),
               matrix(rnorm(150, mean = 3, sd = 0.1), ncol = 3))

    ## Slightly faster way to use pam (see below)
    pam1 <- function(x,k) list(cluster = pam(x,k, cluster.only=TRUE))

    doExtras <- cluster:::doExtras()
    ## or set it explicitly to TRUE for the following
    if(doExtras) {
    ## Note we use  B = 60 in the following examples to keep them "speedy".
    ## ---- rather keep the default B = 500 for your analysis!

    ## note we can  pass 'nstart = 20' to kmeans() :
    gskmn <- clusGap(x, FUN = kmeans, nstart = 20, K.max = 8, B = 60)
    gskmn #-> its print() method
    plot(gskmn, main = "clusGap(., FUN = kmeans, n.start=20, B= 60)")
    set.seed(12); system.time(
      gsPam0 <- clusGap(x, FUN = pam, K.max = 8, B = 60)
    )
    set.seed(12); system.time(
      gsPam1 <- clusGap(x, FUN = pam1, K.max = 8, B = 60)
    )
    ## and show that it gives the same:
    stopifnot(identical(gsPam1[-4], gsPam0[-4]))
    gsPam1
    print(gsPam1, method="globalSEmax")
    print(gsPam1, method="globalmax")
    }

    gs.pam.RU <- clusGap(ruspini, FUN = pam1, K.max = 8, B = 60)
    gs.pam.RU
    plot(gs.pam.RU, main = "Gap statistic for the 'ruspini' data")
    mtext("k = 4 is best .. and  k = 5  pretty close")

    ## This takes a minute..
    ## No clustering ==> k = 1 ("one cluster") should be optimal:
    Z <- matrix(rnorm(256*3), 256,3)
    gsP.Z <- clusGap(Z, FUN = pam1, K.max = 8, B = 200)
    plot(gsP.Z)
    gsP.Z

[Package cluster version 1.15.3 Index]