Nothing
library(fnets)
set.seed(123)
n <- 500
p <- 20
idio <- sim.var(n, p)
x <- idio$data
test_that("var ds executes", {
skip_on_cran()
fv <- fnets.var(x,
center = TRUE, method = "ds", var.order = 1,
tuning.args = list(tuning = "cv", n.folds = 1, path.length = 10),
n.cores = 1
)
plot(fv)
plot(fv, display = 'tuning')
plot(fv, display = 'heatmap')
predict(fv)
par.lrpc(fv, n.cores = 1)
expect_equal(attr(fv, "class"), "fnets")
})
test_that("var high order", {
skip_on_cran()
fv <- fnets.var(x,
center = TRUE, method = "lasso", var.order = 5,
tuning.args = list(tuning = "cv", n.folds = 1, path.length = 10),
n.cores = 1
)
plot(fv)
plot(fv, display = 'tuning')
plot(fv, display = 'heatmap')
predict(fv)
predict(fv, n.ahead = 10)
predict(fv, newdata = x, n.ahead = 10)
expect_equal(attr(fv, "class"), "fnets")
})
test_that("threshold", {
skip_on_cran()
fv <- fnets.var(x,
center = TRUE, method = "lasso", var.order = 1, do.threshold = TRUE,
tuning.args = list(tuning = "cv", n.folds = 1, path.length = 10),
n.cores = 1
)
th <- threshold(fv$beta)
th
plot(th)
})
test_that("var cv executes", {
skip_on_cran()
fv <- fnets.var(x,
center = TRUE, method = "lasso", var.order = 1:2,
tuning.args = list(tuning = "cv", n.folds = 1, path.length = 10),
n.cores = 1
)
expect_equal(attr(fv, "class"), "fnets")
})
test_that("var bic executes", {
skip_on_cran()
fv <- fnets.var(x,
center = TRUE, method = "lasso", var.order = 1:2,
tuning.args = list(tuning = "bic", n.folds = 1, path.length = 10),
n.cores = 1
)
plot(fv, display = 'tuning')
plot(fv, display = "heatmap", groups = rep(c(1, 2), each = p/2), group.colours = c("red", "blue"))
expect_equal(attr(fv, "class"), "fnets")
})
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