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# Q = \tau (R2 \otimes R1)
## first
(n1 <- nrow(
G1 <- sparseMatrix(
i = c(2, 3, 1, 4, 1, 4, 5, 2, 3, 3),
j = c(1, 1, 2, 2, 3, 3, 3, 4, 4, 5),
)
))
R1 <- Diagonal(n = n1, x = colSums(G1)) - G1
R1
## 2nd
(n2 <- nrow(
R2 <- sparseMatrix(
i = c(1L, 1L, 2L, 2L, 2L, 3L, 3L),
j = c(1L, 2L, 1L, 2L, 3L, 3L, 2L),
x = c(2,-1, -1,3,-1, 4, -1))
))
R2
## cgeneric models
cg1 <- cgeneric(
model = "generic0", R = R1,
constr = FALSE, scale = FALSE,
param = c(1, 0.5)) ## P(sigma > 1) = 0.5
cg2 <- cgeneric(
model = "generic0", R = R2,
constr = FALSE, scale = FALSE,
param = c(1, NA)) ## fix sigma, simga = 1
## Kronecker of cgeneric models 1 and 2
cg12 <- kronecker(cg1, cg2)
all.equal(Sparse(R12),
Sparse(prec(cg12, theta = 0)))
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