# R/boot_clusmca.R In clustrd: Methods for Joint Dimension Reduction and Clustering

```boot_clusmca <- function(data, krange, nd=NULL, method = "clusCA", nstart=100, nboot=10, seed=NULL,...)
{
clu={}
#bootstrapping on Z
data = data.frame(data)
data=as.data.frame(lapply(data,as.factor))
x = data.frame(tab.disjonctif(data))

#dummy.data.frame(data, dummy.classes = "ALL")
if(!is.null(seed)) set.seed(seed)
seed <- round(2^31 * runif(nboot, -1, 1))

nk <- length(krange)
nx <- nrow(x)

index1 <- matrix(integer(1), nrow=nx, ncol=nboot)
index2 <- index1

for(b in 1:nboot){
index1[,b] <- sample(1:nx, nx, replace=TRUE)
index2[,b] <- sample(1:nx, nx, replace=TRUE)
}

BFUN <- function(b){

set.seed(seed[b])
cat('\n')
print(paste0("nboot = ", b))

clust1 <- clust2 <- matrix(integer(1), nrow=nx, ncol=nk)
cent1 <- cent2 <- list()
rand <- double(nk)
conc <- double(nk)
for(l in 1:nk)
{
if(nk>1){
if (!is.null(nd)) {
if ((length(nd) >1) & (l==1))  {
cat('\n')
print('Warning: the number of dimensions (nd) must be a single number. Automatically set to the first value in the range.')
}
ndim = nd[1]
}
else
ndim = krange[l]-1
cat('\n')
print(paste0("Running for ",krange[l]," clusters and ",ndim[1]," dimensions."))

x1 = x[index1[,b],,drop=FALSE]
x2 = x[index2[,b],,drop=FALSE]

cl1 <- clusmca(x[index1[,b],,drop=FALSE],nclus=krange[l],ndim=ndim,method = method,nstart=nstart, seed = seed, binary = TRUE)
cl2 <- clusmca(x[index2[,b],,drop=FALSE],nclus=krange[l],ndim=ndim,method = method,nstart=nstart, seed = seed, binary = TRUE)

} else{
if (!is.null(nd)) {
if ((length(nd) >1) & (l==1))  {
cat('\n')
print('Warning: the number of dimensions must be a single number, not a range. Automatically set to the first value.')
}
ndim = nd[1]
}
else{
ndim = krange-1
}
cat('\n')
print(paste0("Running for ",krange," clusters and ",ndim[1]," dimensions."))
x1 = x[index1[,b],,drop=FALSE]
x2 = x[index2[,b],,drop=FALSE]

cl1 <- clusmca(x1,nclus=krange,ndim=ndim,method = method,nstart=nstart, seed = seed, binary = TRUE)
cl2 <- clusmca(x2,nclus=krange,ndim=ndim,method = method,nstart=nstart, seed = seed, binary = TRUE)
}

gm=apply(x1,2,mean)
x1\$clu = cl1\$cluster
clum=(x1 %>% group_by(clu) %>% summarise_all(list(mean)))
bm = data.frame(rbind(clum[,-1],gm))
#rownames(bm) = c(paste("C",1:nrow(clum),sep=""),"all")
cl1\$centers = as.matrix(bm[1:krange[l],])
x1 = x1[,-ncol(x1)]

closest.cluster1 <- function(x) {
cluster.dist <- apply(data.frame(cl1\$centers), 1, function(y) sqrt(sum((x-y)^2)))
return(which.min(cluster.dist)[1])
}
clust1[,l] <- apply(x, 1, closest.cluster1)

#   x2 = dummy.data.frame(x1, dummy.classes = "ALL") # The original super indicator
gm=apply(x2,2,mean)
x2\$clu = cl2\$cluster
clum=(x2 %>% group_by(clu) %>% summarise_all(list(mean)))
bm = data.frame(rbind(clum[,-1],gm))
cl2\$centers = as.matrix(bm[1:krange[l],])
x2 = x2[,-ncol(x2)]

closest.cluster2 <- function(x) {
cluster.dist <- apply(data.frame(cl2\$centers), 1, function(y) sqrt(sum((x-y)^2)))
return(which.min(cluster.dist)[1])
}
clust2[,l] <- apply(x, 1, closest.cluster2)

#     if (measure == "ari") {
rand[l] <- randIndex(clust1[,l], clust2[,l])
#      }
#     if (measure =="conc") {
I = length(unique(clust1[,l]))
J = length(unique(clust2[,l]))
chisq = suppressWarnings(chisq.test(table(clust1[,l],clust2[,l]))\$statistic)
conc[l]<- chisq/(nx*(sqrt(I*J)-1))

#      }

#   if(nrow([email protected]) < k[l]) {
#      extra <- matrix(NA,
#                     ncol=ncol([email protected]),
#                      nrow=k[l]-nrow([email protected]))
#     cent1[[l]] <- rbind([email protected], extra)
#    }

#    if(nrow([email protected]) < k[l]) {
#      extra <- matrix(NA,
#                      ncol=ncol([email protected]),
#                     nrow=k[l]-nrow([email protected]))
#      cent2[[l]] <- rbind([email protected], extra)
#    }
}
list(clust1=clust1, clust2=clust2, rand=rand,conc=conc)

#  list(cent1=cent1, cent2=cent2, clust1=clust1, clust2=clust2,
#       rand=rand)

}

## empirical experiments show parallization does not pay for the
## following (element extraction from list is too fast)
#z <- MClapply(as.list(1:nboot), BFUN, multicore=multicore)

z <- lapply(as.list(1:nboot), BFUN)

clust1 <- unlist(lapply(z, function(x) x\$clust1))
clust2 <- unlist(lapply(z, function(x) x\$clust2))
dim(clust1) <- dim(clust2) <- c(nx, nk, nboot)

#  cent1 <- cent2 <- list()
#  for(l in 1:nk){
#    cent1[[l]] <- unlist(lapply(z, function(x) x\$cent1[[l]]))
#    cent2[[l]] <- unlist(lapply(z, function(x) x\$cent2[[l]]))
#    dim(cent1[[l]]) <- dim(cent2[[l]]) <- c(k[l], ncol(x), nboot)
#  }

if(nk > 1) {
rand <- t(sapply(z, function(x) x\$rand))
conc <- t(sapply(z, function(x) x\$conc))
}
else {
rand <- as.matrix(sapply(z, function(x) x\$rand))
conc <- as.matrix(sapply(z, function(x) x\$conc))
}
colnames(rand) <- krange
colnames(conc) <- krange

out=list()
out\$nclusrange = krange
out\$clust1 = clust1
out\$clust2 = clust2
out\$index1 = index1
out\$index2 = index2
out\$rand = rand
out\$moc = conc

return(out)

# new("bootFlexclust", k=as.integer(k), centers1=cent1, centers2=cent2,
#      cluster1=clust1, cluster2=clust2, index1=index1, index2=index2,
#      rand=rand, call=MYCALL)
}

randIndex <- function (x, y)
{
x <- as.vector(x)
y <- as.vector(y)
if (length(x) != length(y))
stop("arguments must be vectors of the same length")
tab <- table(x, y)
if (all(dim(tab) == c(1, 1)))
return(1)
a <- sum(choose(tab, 2))
b <- sum(choose(rowSums(tab), 2)) - a
c <- sum(choose(colSums(tab), 2)) - a
d <- choose(sum(tab), 2) - a - b - c
ARI <- (a - (a + b) * (a + c)/(a + b + c + d))/((a + b +
a + c)/2 - (a + b) * (a + c)/(a + b + c + d))
return(ARI)
}

# setGeneric("randIndex", function(x, y, correct=TRUE, original=!correct)
#   standardGeneric("randIndex"))
#
# setMethod("randIndex", signature(x="ANY", y="ANY"),
#           function(x, y, correct=TRUE, original=!correct){
#             if(correct)
#               comPart(x, y, type="ARI")
#             else
#               comPart(x, y, type="RI")
#           })
#
# setMethod("randIndex", signature(x="table", y="missing"),
#           doRandIndex <- function(x, y, correct=TRUE, original=!correct)
#           {
#             if(length(dim(x))!=2)
#               stop("Argument x needs to be a 2-dimensional table.")
#
#             n <- sum(x)
#             ni <- apply(x, 1, sum)
#             nj <- apply(x, 2, sum)
#             n2 <- choose(n, 2)
#
#             rand <- NULL
#             if(correct){
#               nis2 <- sum(choose(ni[ni > 1], 2))
#               njs2 <- sum(choose(nj[nj > 1], 2))
#               rand <- c(ARI=c(sum(choose(x[x > 1], 2)) -
#                                 (nis2 * njs2)/n2)/((nis2 + njs2)/2 - (nis2 * njs2)/n2))
#             }
#
#             if(original){
#               rand <- c(rand, RI = 1 + (sum(x^2) - (sum(ni^2) + sum(nj^2))/2)/n2)
#             }
#
#             return(rand)
#           })
#
# ###**********************************************************
#
# countPairs <- function(x, y)
# {
#   if(length(x)!=length(y))
#     stop("x an y must have the same length")
#
#   res <- .C(C_countPairs,
#             as.integer(x),
#             as.integer(y),
#             as.integer(length(x)),
#             res=double(4))[["res"]]
#   matrix(res, nrow=2, dimnames=list(0:1,0:1))
# }

# setMethod("show", "bootFlexclust",
#           function(object){
#             cat("An object of class", sQuote(class(object)),"\n\n")
#             cat("Call:\n")
#             print([email protected])
#             cat("\nNumber of bootstrap pairs:", nrow([email protected]),"\n")
#           })
#
# setMethod("summary", "bootFlexclust",
#           function(object){
#             cat("Call:\n")
#             print([email protected])
#             cat("\nSummary of Rand Indices:\n")
#             print(summary([email protected]))
#           })
#
# setMethod("plot", signature("bootFlexclust","missing"),
#           function(x, y, ...){
#             boxplot(x, ...)
#           })
#
# setMethod("boxplot", "bootFlexclust",
#           function(x, ...){
#             boxplot(as.data.frame([email protected]), ...)
#           })
#
# setMethod("densityplot", "bootFlexclust",
#           function(x, data, ...){
#             Rand <- as.vector([email protected])
#             k <- rep(colnames([email protected]), rep(nrow([email protected]), ncol([email protected])))
#             k <- factor(k, levels=colnames([email protected]))
#
#             densityplot(~Rand|k, as.table=TRUE, to=1, ...)
#           })

disp <- function(x, clus, square = TRUE) {
n <- length(clus)
k <- max(clus)
clus <- as.numeric(clus)
x <- as.matrix(x)
centers <- matrix(nrow = k, ncol = ncol(x))
for (i in 1:k) {
tryCatch(centers[i, ] <- apply(x[clus == i, ], 2, mean),
error = function(e) {print(dim(x))})
}
sumsq <- rep(0, k)
if(square == TRUE)
x <- (x - centers[clus, ])^2
else
x <- abs((x - centers[clus, ]))
for (i in 1:k) {
sumsq[i] <- sum(x[clus == i, ])
}
sumsq
}
```

## Try the clustrd package in your browser

Any scripts or data that you put into this service are public.

clustrd documentation built on May 8, 2019, 5:03 p.m.