spaMM (version 4.5.0)

adjlg: Simulated data set for testing sparse-precision code

Description

This is used in tests/test-adjacency-long.R

Usage

data("adjlg")

Format

Includes an adjacency matrix adjlgMat. and a data frame adjlg with 5474 observations on the following 8 variables.

ID

a factor with levels 1 to 1000

months

a numeric vector

GENDER

a character vector

AGE

a numeric vector

X1

a numeric vector

X2

a numeric vector

month

a numeric vector

BUY

a numeric vector

Examples

data(adjlg)
## See further usage in tests/test-adjacency-long.R
if (FALSE) {
# as produced by:
  library(data.table) ## Included data produced using version 1.10.4.3
  library(igraph) ## Included data produced using version 1.2.1
  
  rsample <- function(N=100, ## size of implied adjacency matrix
                      month_max=10,seed) {
    if (is.integer(seed)) set.seed(seed)
    dt <- data.table(ID=factor(1:N))
    dt$months <- sample(1:month_max,N,replace=T) ## # of liens for each level of ID
    dt$GENDER <- sample(c("MALE","FEMALE"),N,replace=TRUE)
    dt$AGE <- sample(18:99,N,replace=T)
    dt$X1 <- sample(1000:9900,N,replace=T)
    dt$X2 <-  runif(N)
    
    dt <- dt[, c(.SD, month=data.table(seq(from=1, to=months, by = 1))), by = ID] 
    dt[,BUY := 0]
    dt[month.V1==months,BUY := sample(c(0,1),1),by=ID]
    setnames(dt,"month.V1","month")
    
    #### create adjacency matrix
    Network <- data.table(OUT=sample(dt$ID,N*month_max*4/10))
    Network$IN <- sample(dt$ID,N*month_max*4/10)
    Network <- Network[IN != OUT]
    Network <- unique(Network)
    g <- graph.data.frame(Network,directed=F)
    g <- add_vertices(g,sum(!unique(dt$ID) %in% V(g)),
             name=unique(dt[!dt$ID %in% V(g),list(ID)])) # => improper names
    Network <- as_adjacency_matrix(g,sparse = TRUE,type="both")
    colnames(Network) <- rownames(Network) <- seq(nrow(Network)) # post-v3.8.0 names 
    return(list(data=dt,adjMatrix=Network))
  }

  RNGkind("Mersenne-Twister", "Inversion", "Rounding"  )
  set.seed(123)
  adjlg_sam <- rsample(N=1000,seed=NULL) 
  RNGkind("Mersenne-Twister", "Inversion", "Rejection"  )
  #
  adjlg <- as.data.frame(adjlg_sam$data)
  adjlgMat <- adjlg_sam$adjMatrix
}