block-structured arrays

Generates an array or matrix that includes k fully separated block-clusters.

arsim(n, dim, k, noise = 0, shuffle = TRUE, v = 0.1, minc = 1,
  exp.prop = NULL, min.prop = 1/dim/4, noise.type = "s",
  dimnames = list(LETTERS, 1:max(dim)))

Arguments

n	The number of observations in the array.
dim	The dimension of the array.
k	The number of clusters. 1 for no clusters.
noise	The proportion of noise among the observations. There are two choices for noise.type.
shuffle	Whether or not to shuffle the original category orders randomly.
v	A variability parameter for the assignment of the observations to the block clusters. Small values lea
minc	The minimum number of categories each cluster must have in each variable. E.g. minc = 2 means, that each block cluster covers at least 2 categories in each dimension.
exp.prop	Optional: expected proportions of the observations which fall into the block clusters.
min.prop	Minimum proportion of observations in each cluster.
noise.type	Either "s" or "I". The noise type "s" means that n*noise observations are drawn at random from the block-diagonal matrix. Then for these observations the category labels are permuted at random. "I" adds noise in form of a random sample from the independence matrix with the same marginal totals as the block matrix.
dimnames	A list of 2: The first entry defines the variable labels (default: A,B,C,...) and the second entry defines the category labels (default 1:k).

Value

a simulated data array

Details

Not a very sophisticated way of generating random arrays but it serves for tests and illustrations of the other functions.

Examples

  A <- arsim(1000, c(12,12), 3, shuffle = FALSE)
  fluctile(A)
#> viewport[base] 

  A <- arsim(1000, c(12,12), 3, shuffle = FALSE, dimnames = list(NULL,letters))
  dimnames(A)
#> [[1]]
#>  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l"
#> 
#> [[2]]
#>  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l"
#> 

  A <- arsim(4000, c(11,7,5), 3, shuffle = TRUE, dimnames = list(0:2,letters))
  dimnames(A)
#> [[1]]
#>  [1] "0b" "0j" "0g" "0k" "0d" "0c" "0e" "0f" "0i" "0h" "0a"
#> 
#> [[2]]
#> [1] "1a" "1f" "1b" "1g" "1c" "1d" "1e"
#> 
#> [[3]]
#> [1] "2b" "2e" "2c" "2d" "2a"
#> 

  
# NOT RUN {
  A2<- arsim(1000, c(12,12,12), 3, shuffle = FALSE)
  fluctile3d(A2, shape ="oct")

# }