R/concerts.R

In manet: Multiple Allocation Model for Actor-Event Networks

#' Concerts synthetic network
#'
#'Synthetic data matrix of dimension n x d, recording attendances of n=500 people to d=14 concerts from 14 different artists. There are three clusters in the data, each one corresponding to a community of fans of a specific musical genre. Overlaps of these fandoms point towards attendances dictated by artists playing music from sub-genres - such as electropop.
#'
#' \itemize{
#'   \item 14 concerts attendended: "Blondie", "Fleetwood Mac", "Paramore", "Queen", "St.Vincent", "The Queen", "Pet Shop Boys", "M83", "Daft Punk", "Goldfrapp", "Chvrches", "LaRoux", "Robyn", "BANKS"
#'   \item 500 attendees
#' }
#' @format A data frame with 500 rows and 14 variables
#'@importFrom mclust adjustedRandIndex classError
#'@importFrom stats rbinom
#' @examples
#'#DATA GENERATION
#'z_ext <-function(x,nfac){
#'  nq <- length(x)
#'  zx <- hcube(rep(nq,nfac))
#'  zx <- zx[,dim(zx)[2]:1]
#'  z2 <- matrix(x[zx],dim(zx)[1],dim(zx)[2])
#'  return(z2)
#'  }
#'K=3 # main clusters: Rock (cluster h=5), Pop (cluster h=3), Electronic (cluster h=2)
#'K_star=2^K
#'n=500 #attendees
#'set.seed(777)
#'u=z_ext(0:1,K)
#'alpha_star=rep(0,K_star)
#'alpha_star=c(0.05,0.10,0.35,0.15,0.25,0.00,0.10,0.00)
#'index=rep(0,n)
#'for(i in 1:n)
#'  index[i]=sample(1:K_star,1,prob=alpha_star)
#'d=14 #concerts/artists
#'y<-matrix(0,n,d)
#'colnames(y)=c("Blondie", "Fleetwood Mac", "Paramore","Queen","St.Vincent", "The Queen",
#'"Pet Shop Boys","M83","Daft Punk", "Goldfrapp", "Chvrches", "LaRoux", "Robyn","BANKS")
#'pi.greco=matrix(0,K,d)
#'rownames(pi.greco)=c("Rock","Pop","Electronic")
#'colnames(pi.greco)=colnames(y)
#'pi.greco[1,]=c(0.80,0.80,0.80,0.70,0.90,0.80,0.10,0.10,0.05,0.05,0.10,0.05,0.05,0.10)
#'pi.greco[2,]=c(0.10,0.10,0.90,0.80,0.90,0.80,0.05,0.10,0.05,0.70,0.70,0.05,0.80,0.05)
#'pi.greco[3,]=c(0.05,0.05,0.05,0.10,0.05,0.05,0.80,0.90,0.90,0.80,0.70,0.80,0.90,0.90)
#'for (i in 1:n)
#'for(j in 1:d)
#'  y[i,j]<-rbinom(1,1,prob=ifelse(sum(u[index[i],])==0,0.00000001,min(pi.greco[,j]^u[index[i],])))
#'#y is the 500x14 matrix of data
#'
#' #RUNNING MANET
#' \dontrun{
#' data(concerts)
#' start=Sys.time()
#' crt<-manet(concerts,K=3,maxT=5000)
#' finish=Sys.time()
#' finish-start
#' #Time difference of 11.58112 mins
#' plot(crt)
#' summary(crt)
#' alloc<-summary(crt)$actor.allocations[,2]
#' adjustedRandIndex(index,alloc)
#' #0.8420733
#' classError(alloc,index)$errorRate
#' #0.07
#' }
"concerts"