sim_hdchange_nbd {L2hdchange}

Simulate data with neighbourhood

Description

Simulate data with neighbourhood

Usage

sim_hdchange_nbd(
  n = 300,
  p = 70,
  nbd_info = list((1:9), (2:31), (32:41), (42:70), (3:15), (16:35), (31:55)),
  sp_tp_break = rbind(c(2, 50), c(4, 150), c(2, 250)),
  dist_info = list(dist = "normal", dependence = "iid", param = 1),
  jump_max = 1
)

Arguments

n

Number of time series observations.

p

Number of individual.

nbd_info

A list containing the neighbourhood information. See ts_hdchange().

sp_tp_break

A K \times 2 matrix indicating the spatial-temporal break location.

dist_info

A list specifying the distribution of the innovation.

jump_max

Maximum jump size of the breaks.

Details

'sp_tp_break' should be a K \times 2 matrix with first column indicating the neighbourhoods and the second column indicating the time stamps. For example, 'sp_tp_break = rbind(c(2, 50), c(4, 150), c(2, 250))' means that the second neighbourhood has two breaks taking place at i = 50, 250 and the fourth neighbourhood has one break taking place at i = 150.

'dist_info' should be a list containing the following items:

• dist: distribution of the innovations, either "normal" or "t".

• dependence: iid or MA(\infty), either "iid" or "MA_inf".

• param = parameter of the distribution, standard deviation for normal distribution and degree of freedom for t distribution

'jump_max' is set equal in nbd case for convenience.

See ts_hdchange() for example.

Value

A p \times n simulated data matrix.

Examples

data_nbd <- sim_hdchange_nbd(n = 300,
p = 70,
nbd_info =
 list(
   (1:9), (2:31), (32:41), (42:70),
   (3:15), (16:35), (31:55)
 ),
sp_tp_break = rbind(c(2, 50), c(4, 150), c(2, 250)),
dist_info =
  list(dist = "t", dependence = "iid", param = 5),
jump_max = 1)