# NOT RUN {
model <- BuildModel(
p.map = list(A = "1", B = "R", t0 = "1", mean_v = "M", sd_v = "M",
st0 = "1"),
match.map = list(M = list(s1 = 1, s2 = 2)),
constants = c(sd_v.false = 1, st0 = 0),
factors = list(S = c("s1","s2")),
responses = c("r1", "r2"),
type = "norm")
#######################30
## Example 1
#######################30
GetNsim(model, ns = 2, n = 1)
# [,1] [,2]
# [1,] 1 1
# [2,] 1 1
#######################30
## Example 2
#######################30
n <- matrix(c(1:2), ncol = 1)
# [,1]
# [1,] 1 ## subject 1 has 1 response for each cell
# [2,] 2 ## subject 2 has 2 responses for each cell
GetNsim(model, ns = 2, n = n)
# [,1] [,2]
# [1,] 1 1
# [2,] 2 2
#######################30
## Example 3
#######################30
n <- matrix(c(1:2), nrow = 1)
# [,1] [,2]
# [1,] 1 2
GetNsim(model, ns = 2, n = n)
# [,1] [,2]
# [1,] 1 2 ## subject 1 has 1 response for cell 1 and 2 responses for cell 2
# [2,] 1 2 ## subject 2 has 1 response for cell 1 and 2 responses for cell 2
#######################30
## Example 4
#######################30
n <- matrix(c(1:4), nrow=2)
# [,1] [,2]
# [1,] 1 3
# [2,] 2 4
ggdmc::GetNsim(model, ns = 2, n = n)
# [,1] [,2]
# [1,] 1 3 ## subject 1 has 1 response for cell 1 and 3 responses for cell 2
# [2,] 2 4 ## subject 2 has 2 responses for cell 1 and 4 responses for cell 2
# }
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