set.seed(1)
n <- 300
p <- 3
y_count <- 2
x <- mvtnorm::rmvnorm(n, rep(0, p), diag(p))
theta_0 <- array(NA, dim = c(3, y_count, 3))
theta_0[, , 1] <- cbind(c(1, 1.2, -1), c(-1, 0, 0.5))
theta_0[, , 2] <- cbind(c(-1, 0, 0.5), c(0.5, -0.3, 0.2))
theta_0[, , 3] <- cbind(c(0.5, -0.3, 0.2), c(1, 1.2, -1))
y <- rbind(
x[1:100, ] %*% theta_0[, , 1],
x[101:200, ] %*% theta_0[, , 2],
x[201:n, ] %*% theta_0[, , 3]
) + matrix(rnorm(n * y_count), ncol = y_count)
multi_response_linear_loss <- function(data) {
x <- data[, (ncol(data) - p + 1):ncol(data)]
y <- data[, 1:(ncol(data) - p)]
if (nrow(data) <= p) {
x_t_x <- diag(p)
} else {
x_t_x <- crossprod(x)
}
norm(y - x %*% solve(x_t_x, t(x)) %*% y, type = "F")^2 / 2
}
result <- fastcpd(
formula = y ~ x - 1,
data = data.frame(y = y, x = x),
beta = (2 * p + 1) * log(n) / 2,
cost = multi_response_linear_loss,
cp_only = TRUE,
r.progress = FALSE
)
testthat::expect_equal(result@cp_set, c(102, 195))
