tests/testthat/test-precision-matrices.R
In jtipton25/BayesMRA: Bayesian Multi-Resolution Gaussian Process Approximations
context("testing precision matrices")
# test_that("make_Q", {
#
# expect_error(make_Q(4, "aaa"), "phi must be between -1 and 1")
# expect_error(make_Q(4, NA), "phi must be between -1 and 1")
# expect_error(make_Q(4, -2), "phi must be between -1 and 1")
# expect_error(make_Q(4, 2), "phi must be between -1 and 1")
# make_Q(4, 0.9)
# # expect_error(wendland_basis(1:10, 1:10), "radius must be a single positive numeric value")
# # expect_error(wendland_basis(-10:10, 5), "d must be nonnegative")
# # expect_error(wendland_basis(c(1:10, NA), 2), "d must not contain missing values")
# # expect_equal(wendland_basis(1:5, 3), c(0.37717827566936, 0.013971447441955, 0, 0, 0))
# })
test_that("make_Q_alpha_2d", {
# expect_identical(
# make_Q_alpha_2d(4, 0.5),
# structure(list(new("spam",
# entries = c(2, -0.5, -0.5, -0.5, 3, -0.5, -0.5,
# -0.5, 3, -0.5, -0.5, -0.5, 2, -0.5, -0.5, 3, -0.5, -0.5, -0.5,
# -0.5, 4, -0.5, -0.5, -0.5, -0.5, 4, -0.5, -0.5, -0.5, -0.5, 3,
# -0.5, -0.5, 3, -0.5, -0.5, -0.5, -0.5, 4, -0.5, -0.5, -0.5, -0.5,
# 4, -0.5, -0.5, -0.5, -0.5, 3, -0.5, -0.5, 2, -0.5, -0.5, -0.5,
# 3, -0.5, -0.5, -0.5, 3, -0.5, -0.5, -0.5, 2),
# colindices = c(1L,
# 2L, 5L, 1L, 2L, 3L, 6L, 2L, 3L, 4L, 7L, 3L, 4L, 8L, 1L, 5L, 6L,
# 9L, 2L, 5L, 6L, 7L, 10L, 3L, 6L, 7L, 8L, 11L, 4L, 7L, 8L, 12L,
# 5L, 9L, 10L, 13L, 6L, 9L, 10L, 11L, 14L, 7L, 10L, 11L, 12L, 15L,
# 8L, 11L, 12L, 16L, 9L, 13L, 14L, 10L, 13L, 14L, 15L, 11L, 14L,
# 15L, 16L, 12L, 15L, 16L),
# rowpointers = c(1L, 4L, 8L, 12L, 15L,
# 19L, 24L, 29L, 33L, 37L, 42L, 47L, 51L, 54L, 58L, 62L, 65L),
# dimension = c(16L, 16L))),
# class = "Q_alpha"
# )
# )
n_dims <- c(4, 16)
phi <- rep(1, 3)
expect_error(make_Q_alpha_2d(n_dims, phi), "n_dims and phi must both be vectors of length M.")
n_dims <- c(4, 16, 32)
phi <- rep(1, 2)
expect_error(make_Q_alpha_2d(n_dims, phi), "n_dims and phi must both be vectors of length M.")
n_dims <- c(4, 16)
phi <- rep(NA, 2)
expect_error(make_Q_alpha_2d(n_dims, phi), "phi must be a numeric vector of length M with values between -1 and 1.")
phi <- rep(2, 2)
expect_error(make_Q_alpha_2d(n_dims, phi), "phi must be a numeric vector of length M with values between -1 and 1.")
phi <- rep(-2, 2)
expect_error(make_Q_alpha_2d(n_dims, phi), "phi must be a numeric vector of length M with values between -1 and 1.")
expect_error(make_Q_alpha_2d(4, 1.5), "phi must be a numeric vector of length M with values between -1 and 1.")
expect_error(make_Q_alpha_2d(4, -1.5), "phi must be a numeric vector of length M with values between -1 and 1.")
expect_error(make_Q_alpha_2d(c(2, 4), c(0.5, 1.5)), "phi must be a numeric vector of length M with values between -1 and 1.")
expect_error(make_Q_alpha_2d(c(2, 4), c(0.5, NA)), "phi must be a numeric vector of length M with values between -1 and 1.")
expect_error(make_Q_alpha_2d(c(2, 4), c(0.5, "aaa")), "phi must be a numeric vector of length M with values between -1 and 1.")
expect_error(make_Q_alpha_2d(c(2, NA), c(0.5, 0.5)), "n_dims must be a vector of integers of length M.")
expect_error(make_Q_alpha_2d(c(2, "aaa"), c(0.5, 0.5)), "n_dims must be a vector of integers of length M.")
expect_error(make_Q_alpha_2d(c(2, 3.5), c(0.5, 0.5)), "n_dims must be a vector of integers of length M.")
n_dims <- c(4, 16)
phi <- rep(1, 2)
expect_error(make_Q_alpha_2d(n_dims, phi, use_spam = "TRUE"), "use_spam must be either TRUE or FALSE.")
expect_error(make_Q_alpha_2d(n_dims, phi, use_spam = 3.5), "use_spam must be either TRUE or FALSE.")
expect_error(make_Q_alpha_2d(n_dims, phi, use_spam = NA), "use_spam must be either TRUE or FALSE.")
expect_error(make_Q_alpha_2d(n_dims, phi, prec_model = "AAA"), 'The only valid options for prec_model are \"CAR\" and \"SAR\".')
expect_error(make_Q_alpha_2d(n_dims, phi, prec_model = NA), 'The only valid options for prec_model are \"CAR\" and \"SAR\".')
expect_error(make_Q_alpha_2d(n_dims, phi, prec_model = 32), 'The only valid options for prec_model are \"CAR\" and \"SAR\".')
})
test_that("make_Q_alpha_tau2", {
n_dims <- c(4, 16)
phi <- c(1, 1)
tau2 <- c(2, 2)
Q_alpha <- make_Q_alpha_2d(n_dims, phi)
class(Q_alpha) <- "aaa"
expect_error(make_Q_alpha_tau2(Q_alpha, tau2), 'Q_alpha must by of class "Q_alpha" which is the output of make_Q_alpha_2d\\(\\)')
class(Q_alpha) <- "Q_alpha"
tau2 <- 1:3
expect_error(make_Q_alpha_tau2(Q_alpha, tau2), "Q_alpha must be a list of length M and tau2 must be a positive numeric vector of length M.")
tau2 <- c(1, NA)
expect_error(make_Q_alpha_tau2(Q_alpha, tau2), "tau2 must be a positive numeric vector of length M.")
tau2 <- c(1, "aaa")
expect_error(make_Q_alpha_tau2(Q_alpha, tau2), "tau2 must be a positive numeric vector of length M.")
tau2 <- c(1, -1)
expect_error(make_Q_alpha_tau2(Q_alpha, tau2), "tau2 must be a positive numeric vector of length M.")
tau2 <- 1:2
expect_error(make_Q_alpha_tau2(Q_alpha, tau2, use_spam = "TRUE"), "use_spam must be either TRUE or FALSE.")
expect_error(make_Q_alpha_tau2(Q_alpha, tau2, use_spam = 3.5), "use_spam must be either TRUE or FALSE.")
expect_error(make_Q_alpha_tau2(Q_alpha, tau2, use_spam = NA), "use_spam must be either TRUE or FALSE.")
expect_equal({
n_dims <- c(2, 4)
phi <- c(1, 1)
Q_alpha <- make_Q_alpha_2d(n_dims, phi, prec_model = "SAR")
tau2 <- c(3, 5)
make_Q_alpha_tau2(Q_alpha, tau2)
},
{
new("spam", entries = c(4.5, -3, -3, 1.5, -3, 4.5, 1.5, -3, -3,
1.5, 4.5, -3, 1.5, -3, -3, 4.5, 7.5, -4.16666666666667, 0.833333333333333,
-4.16666666666667, 1.25, 0.833333333333333, -4.16666666666667,
6.66666666666667, -3.33333333333333, 0.833333333333333, 1.11111111111111,
-2.91666666666667, 0.833333333333333, 0.416666666666667, 0.833333333333333,
-3.33333333333333, 6.66666666666667, -4.16666666666667, 0.833333333333333,
-2.91666666666667, 1.11111111111111, 0.416666666666667, 0.833333333333333,
-4.16666666666667, 7.5, 1.25, -4.16666666666667, 0.833333333333333,
-4.16666666666667, 1.11111111111111, 6.66666666666667, -2.91666666666667,
0.416666666666667, -3.33333333333333, 0.833333333333333, 0.833333333333333,
1.25, -2.91666666666667, 0.833333333333333, -2.91666666666667,
6.25, -2.5, 0.416666666666667, 0.833333333333333, -2.5, 0.625,
0.416666666666667, 0.833333333333333, -2.91666666666667, 1.25,
0.416666666666667, -2.5, 6.25, -2.91666666666667, 0.625, -2.5,
0.833333333333333, 0.416666666666667, 1.11111111111111, -4.16666666666667,
0.416666666666667, -2.91666666666667, 6.66666666666667, 0.833333333333333,
-3.33333333333333, 0.833333333333333, 0.833333333333333, -3.33333333333333,
0.833333333333333, 6.66666666666667, -2.91666666666667, 0.416666666666667,
-4.16666666666667, 1.11111111111111, 0.416666666666667, 0.833333333333333,
-2.5, 0.625, -2.91666666666667, 6.25, -2.5, 0.416666666666667,
1.25, -2.91666666666667, 0.833333333333333, 0.416666666666667,
0.625, -2.5, 0.833333333333333, 0.416666666666667, -2.5, 6.25,
-2.91666666666667, 0.833333333333333, -2.91666666666667, 1.25,
0.833333333333333, 0.833333333333333, -3.33333333333333, 0.416666666666667,
-2.91666666666667, 6.66666666666667, 1.11111111111111, -4.16666666666667,
0.833333333333333, -4.16666666666667, 1.25, 7.5, -4.16666666666667,
0.833333333333333, 0.416666666666667, 1.11111111111111, -2.91666666666667,
0.833333333333333, -4.16666666666667, 6.66666666666667, -3.33333333333333,
0.833333333333333, 0.416666666666667, 0.833333333333333, -2.91666666666667,
1.11111111111111, 0.833333333333333, -3.33333333333333, 6.66666666666667,
-4.16666666666667, 0.833333333333333, 1.25, -4.16666666666667,
0.833333333333333, -4.16666666666667, 7.5),
colindices = c(1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 9L, 10L, 13L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 14L, 5L,
6L, 7L, 8L, 10L, 11L, 12L, 15L, 6L, 7L, 8L, 11L, 12L, 16L, 5L,
6L, 9L, 10L, 11L, 13L, 14L, 17L, 5L, 6L, 7L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 18L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 14L, 15L,
16L, 19L, 7L, 8L, 10L, 11L, 12L, 15L, 16L, 20L, 5L, 9L, 10L,
13L, 14L, 15L, 17L, 18L, 6L, 9L, 10L, 11L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 7L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 18L, 19L,
20L, 8L, 11L, 12L, 14L, 15L, 16L, 19L, 20L, 9L, 13L, 14L, 17L,
18L, 19L, 10L, 13L, 14L, 15L, 17L, 18L, 19L, 20L, 11L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 12L, 15L, 16L, 18L, 19L, 20L),
rowpointers = c(1L,
5L, 9L, 13L, 17L, 23L, 31L, 39L, 45L, 53L, 64L, 75L, 83L, 91L,
102L, 113L, 121L, 127L, 135L, 143L, 149L),
dimension = c(20L,
20L)
)
})
expect_identical({
n_dims <- c(2, 4)
phi <- c(1, 1)
Q_alpha <- make_Q_alpha_2d(n_dims, phi)
tau2 <- c(3, 5)
make_Q_alpha_tau2(Q_alpha, tau2)
},
{
new("spam",
entries = c(6, -3, -3, -3, 6, -3, -3, 6, -3, -3,
-3, 6, 10, -5, -5, -5, 15, -5, -5, -5, 15, -5, -5, -5, 10, -5,
-5, 15, -5, -5, -5, -5, 20, -5, -5, -5, -5, 20, -5, -5, -5, -5,
15, -5, -5, 15, -5, -5, -5, -5, 20, -5, -5, -5, -5, 20, -5, -5,
-5, -5, 15, -5, -5, 10, -5, -5, -5, 15, -5, -5, -5, 15, -5, -5,
-5, 10),
colindices = c(1L, 2L, 3L, 1L, 2L, 4L, 1L, 3L, 4L, 2L,
3L, 4L, 5L, 6L, 9L, 5L, 6L, 7L, 10L, 6L, 7L, 8L, 11L, 7L, 8L,
12L, 5L, 9L, 10L, 13L, 6L, 9L, 10L, 11L, 14L, 7L, 10L, 11L, 12L,
15L, 8L, 11L, 12L, 16L, 9L, 13L, 14L, 17L, 10L, 13L, 14L, 15L,
18L, 11L, 14L, 15L, 16L, 19L, 12L, 15L, 16L, 20L, 13L, 17L, 18L,
14L, 17L, 18L, 19L, 15L, 18L, 19L, 20L, 16L, 19L, 20L),
rowpointers = c(1L,
4L, 7L, 10L, 13L, 16L, 20L, 24L, 27L, 31L, 36L, 41L, 45L, 49L,
54L, 59L, 63L, 66L, 70L, 74L, 77L),
dimension = c(20L, 20L))
})
expect_equal({
n_dims <- c(2, 4)
phi <- c(1, 1)
Q_alpha <- make_Q_alpha_2d(n_dims, phi, prec_model = "SAR", use_spam = FALSE)
tau2 <- c(3, 5)
make_Q_alpha_tau2(Q_alpha, tau2, use_spam = FALSE)
},
{
new("dgCMatrix",
i = c(0L, 1L, 2L, 3L, 0L, 1L, 2L, 3L, 0L, 1L,
2L, 3L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 8L, 9L, 12L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 13L, 4L, 5L, 6L, 7L, 9L, 10L, 11L, 14L, 5L,
6L, 7L, 10L, 11L, 15L, 4L, 5L, 8L, 9L, 10L, 12L, 13L, 16L, 4L,
5L, 6L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 17L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 13L, 14L, 15L, 18L, 6L, 7L, 9L, 10L, 11L, 14L,
15L, 19L, 4L, 8L, 9L, 12L, 13L, 14L, 16L, 17L, 5L, 8L, 9L, 10L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 6L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 17L, 18L, 19L, 7L, 10L, 11L, 13L, 14L, 15L, 18L, 19L,
8L, 12L, 13L, 16L, 17L, 18L, 9L, 12L, 13L, 14L, 16L, 17L, 18L,
19L, 10L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 11L, 14L, 15L, 17L,
18L, 19L),
p = c(0L, 4L, 8L, 12L, 16L, 22L, 30L, 38L, 44L, 52L,
63L, 74L, 82L, 90L, 101L, 112L, 120L, 126L, 134L, 142L, 148L),
Dim = c(20L, 20L),
Dimnames = list(NULL, NULL),
x = c(4.5,
-3, -3, 1.5, -3, 4.5, 1.5, -3, -3, 1.5, 4.5, -3, 1.5, -3,
-3, 4.5, 7.5, -4.16666666666667, 0.833333333333333, -4.16666666666667,
1.25, 0.833333333333333, -4.16666666666667, 6.66666666666667,
-3.33333333333333, 0.833333333333333, 1.11111111111111, -2.91666666666667,
0.833333333333333, 0.416666666666667, 0.833333333333333,
-3.33333333333333, 6.66666666666667, -4.16666666666667, 0.833333333333333,
-2.91666666666667, 1.11111111111111, 0.416666666666667, 0.833333333333333,
-4.16666666666667, 7.5, 1.25, -4.16666666666667, 0.833333333333333,
-4.16666666666667, 1.11111111111111, 6.66666666666667, -2.91666666666667,
0.416666666666667, -3.33333333333333, 0.833333333333333,
0.833333333333333, 1.25, -2.91666666666667, 0.833333333333333,
-2.91666666666667, 6.25, -2.5, 0.416666666666667, 0.833333333333333,
-2.5, 0.625, 0.416666666666667, 0.833333333333333, -2.91666666666667,
1.25, 0.416666666666667, -2.5, 6.25, -2.91666666666667, 0.625,
-2.5, 0.833333333333333, 0.416666666666667, 1.11111111111111,
-4.16666666666667, 0.416666666666667, -2.91666666666667,
6.66666666666667, 0.833333333333333, -3.33333333333333, 0.833333333333333,
0.833333333333333, -3.33333333333333, 0.833333333333333,
6.66666666666667, -2.91666666666667, 0.416666666666667, -4.16666666666667,
1.11111111111111, 0.416666666666667, 0.833333333333333, -2.5,
0.625, -2.91666666666667, 6.25, -2.5, 0.416666666666667,
1.25, -2.91666666666667, 0.833333333333333, 0.416666666666667,
0.625, -2.5, 0.833333333333333, 0.416666666666667, -2.5,
6.25, -2.91666666666667, 0.833333333333333, -2.91666666666667,
1.25, 0.833333333333333, 0.833333333333333, -3.33333333333333,
0.416666666666667, -2.91666666666667, 6.66666666666667, 1.11111111111111,
-4.16666666666667, 0.833333333333333, -4.16666666666667,
1.25, 7.5, -4.16666666666667, 0.833333333333333, 0.416666666666667,
1.11111111111111, -2.91666666666667, 0.833333333333333, -4.16666666666667,
6.66666666666667, -3.33333333333333, 0.833333333333333, 0.416666666666667,
0.833333333333333, -2.91666666666667, 1.11111111111111, 0.833333333333333,
-3.33333333333333, 6.66666666666667, -4.16666666666667, 0.833333333333333,
1.25, -4.16666666666667, 0.833333333333333, -4.16666666666667,
7.5),
factors = list())
})
expect_identical({
n_dims <- c(2, 4)
phi <- c(1, 1)
Q_alpha <- make_Q_alpha_2d(n_dims, phi, use_spam = FALSE)
tau2 <- c(3, 5)
make_Q_alpha_tau2(Q_alpha, tau2, use_spam = FALSE)
},
{
new("dgCMatrix",
i = c(0L, 1L, 2L, 0L, 1L, 3L, 0L, 2L, 3L, 1L,
2L, 3L, 4L, 5L, 8L, 4L, 5L, 6L, 9L, 5L, 6L, 7L, 10L, 6L, 7L,
11L, 4L, 8L, 9L, 12L, 5L, 8L, 9L, 10L, 13L, 6L, 9L, 10L, 11L,
14L, 7L, 10L, 11L, 15L, 8L, 12L, 13L, 16L, 9L, 12L, 13L, 14L,
17L, 10L, 13L, 14L, 15L, 18L, 11L, 14L, 15L, 19L, 12L, 16L, 17L,
13L, 16L, 17L, 18L, 14L, 17L, 18L, 19L, 15L, 18L, 19L),
p = c(0L,
3L, 6L, 9L, 12L, 15L, 19L, 23L, 26L, 30L, 35L, 40L, 44L, 48L,
53L, 58L, 62L, 65L, 69L, 73L, 76L),
Dim = c(20L, 20L),
Dimnames = list(NULL, NULL),
x = c(6, -3, -3, -3, 6, -3, -3, 6, -3, -3, -3,
6, 10, -5, -5, -5, 15, -5, -5, -5, 15, -5, -5, -5, 10, -5, -5,
15, -5, -5, -5, -5, 20, -5, -5, -5, -5, 20, -5, -5, -5, -5, 15,
-5, -5, 15, -5, -5, -5, -5, 20, -5, -5, -5, -5, 20, -5, -5, -5,
-5, 15, -5, -5, 10, -5, -5, -5, 15, -5, -5, -5, 15, -5, -5, -5,
10),
factors = list()
)
})
# locs <- matrix(1:20, 10, 2)
# MRA <- mra_wendland_2d(locs)
# locs_pred <- matrix(NA, 20, 2)
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), "locs_pred must be a numeric matrix with N rows and 2 columns")
#
# locs_pred <- matrix(1:30, 10, 3)
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), "locs_pred must be a numeric matrix with N rows and 2 columns")
#
# locs_pred <- matrix("11", 10, 2)
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), "locs_pred must be a numeric matrix with N rows and 2 columns")
#
# locs_pred <- 1:10
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), "locs_pred must be a numeric matrix with N rows and 2 columns")
#
# locs <- matrix(1:30, 10, 3)
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), "locs must be a numeric matrix with N rows and 2 columns")
#
# locs <- matrix(1:20, 10, 2)
# locs_pred <- matrix(1:20, 10, 2)
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA, use_spam = 3.5), "use_spam must be either TRUE or FALSE")
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA, use_spam = NA), "use_spam must be either TRUE or FALSE")
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA, use_spam = "aaa"), "use_spam must be either TRUE or FALSE")
#
# class(MRA) <- NULL
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), 'MRA must be of class "mra_wendland_2d"')
#
# class(MRA) <- "XXX"
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), 'MRA must be of class "mra_wendland_2d"')
})
test_that("test_constraint", {
locs <- matrix(1:20, 10, 2)
MRA <- mra_wendland_2d(locs, M = 2, n_coarse_grid = 4)
expect_equal(make_constraint(MRA, constraint = "unconstrained"),
list(A_constraint = NULL, a_constraint = NULL))
expect_equal(make_constraint(MRA, constraint = "overall"),
list(A_constraint = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1.08601623125006e-05, 0.000112496007290721, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000625061236957296, 0.0225489224875791,
0.0633262258769764, 0.0312738163856595, 0.00121730932738073,
0, 0, 0, 0, 0, 0, 0, 0, 1.08601623125006e-05, 0.0225489224875791,
0.321435192757127, 0.857954760387478, 0.622740078658578, 0.113913040814712,
0.00180999093529261, 0, 0, 0, 0, 0, 0, 0, 0.000112496007290721,
0.0633262258769764, 0.857954760387478, 2.69320117577294, 2.6738302846565,
0.995571956296118, 0.1245002417436, 0.00180999093529261, 0, 0,
0, 0, 0, 0, 1.08654289635091e-05, 0.0312738163856595, 0.622740078658578,
2.6738302846565, 4.30156261189159, 3.04319377260997, 0.995571956296118,
0.113913040814712, 0.00121730932738073, 0, 0, 0, 0, 0, 0, 0.00121730932738073,
0.113913040814712, 0.995571956296118, 3.04319377260997, 4.30156261189159,
2.67383028465651, 0.622740078658578, 0.0312738163856595, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0.00180999093529261, 0.1245002417436, 0.995571956296118,
2.67383028465651, 2.69320117577294, 0.857954760387479, 0.0633262258769764,
0.000112496007290721, 0, 0, 0, 0, 0, 0, 0, 0.00180999093529261,
0.113913040814712, 0.622740078658578, 0.857954760387479, 0.321435192757127,
0.0225489224875791, 1.08601623125006e-05, 0, 0, 0, 0, 0, 0, 0,
0, 0.00121730932738073, 0.0312738163856595, 0.0633262258769764,
0.0225489224875791, 0.000625061236957296, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1.08654289635091e-05, 0.000112496007290721, 1.08601623125006e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316797e-10,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.4496711175134e-05,
0.0015701089925862, 0.003896397104994, 0.00157213610851392, 3.45007275290915e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.44967111751343e-05, 0.00872963127077592,
0.0661544886375021, 0.124017651192734, 0.0759808782995632, 0.0120248586792744,
9.61902739128458e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157010899258622,
0.0661544886375023, 0.375690548876713, 0.723386677289052, 0.549050844946753,
0.168170211647942, 0.0153085619417029, 9.53250421932223e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316932e-10, 0.00389639710499403,
0.124017651192734, 0.723386677289053, 1.59744074560367, 1.53908699828218,
0.744223319422889, 0.178596350346924, 0.0153052083946846, 9.57044116187268e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157213610851394, 0.0759808782995634,
0.549050844946754, 1.53908699828218, 2.16411927874826, 1.67691161703102,
0.75100032952569, 0.178611592076099, 0.015317207123465, 9.6417509205864e-05,
0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290925e-05, 0.0120248586792744,
0.168170211647942, 0.744223319422889, 1.67691161703102, 2.19167865690685,
1.67725643503247, 0.751057520686108, 0.17863784828745, 0.0153172071234649,
9.57044116187268e-05, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128473e-05,
0.0153085619417029, 0.178596350346924, 0.751000329525689, 1.67725643503247,
2.19421288675405, 1.67673692684187, 0.751057520686108, 0.178611592076099,
0.0153052083946846, 9.53250421932245e-05, 0, 0, 0, 0, 0, 0, 0,
0, 9.53250421932241e-05, 0.0153052083946846, 0.178611592076099,
0.751057520686108, 1.67673692684187, 2.19421288675405, 1.67725643503247,
0.751000329525689, 0.178596350346924, 0.0153085619417029, 9.61902739128473e-05,
0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187261e-05, 0.0153172071234649,
0.178637848287449, 0.751057520686107, 1.67725643503247, 2.19167865690685,
1.67691161703102, 0.74422331942289, 0.168170211647943, 0.0120248586792745,
3.45007275290925e-05, 0, 0, 0, 0, 0, 0, 0, 0, 9.64175092058634e-05,
0.015317207123465, 0.178611592076099, 0.751000329525689, 1.67691161703101,
2.16411927874826, 1.53908699828218, 0.549050844946754, 0.0759808782995635,
0.00157213610851394, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187275e-05,
0.0153052083946846, 0.178596350346925, 0.744223319422889, 1.53908699828218,
1.59744074560367, 0.723386677289053, 0.124017651192734, 0.00389639710499402,
5.05673652316932e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.53250421932245e-05,
0.015308561941703, 0.168170211647942, 0.549050844946754, 0.723386677289053,
0.375690548876713, 0.0661544886375023, 0.00157010899258621, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128465e-05, 0.0120248586792744,
0.0759808782995633, 0.124017651192734, 0.0661544886375021, 0.00872963127077592,
3.4496711175134e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290909e-05,
0.00157213610851391, 0.00389639710499397, 0.00157010899258619,
3.44967111751334e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 5.05673652316797e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(1L, 520L)), a_constraint = 0))
expect_equal(make_constraint(MRA, constraint = "resolution"),
list(A_constraint = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.08601623125006e-05, 0,
0.000112496007290721, 0, 1.08654289635091e-05, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000625061236957296,
0, 0.0225489224875791, 0, 0.0633262258769764, 0, 0.0312738163856595,
0, 0.00121730932738073, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1.08601623125006e-05, 0, 0.0225489224875791, 0, 0.321435192757127,
0, 0.857954760387478, 0, 0.622740078658578, 0, 0.113913040814712,
0, 0.00180999093529261, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.000112496007290721, 0, 0.0633262258769764, 0, 0.857954760387478,
0, 2.69320117577294, 0, 2.6738302846565, 0, 0.995571956296118,
0, 0.1245002417436, 0, 0.00180999093529261, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1.08654289635091e-05, 0, 0.0312738163856595,
0, 0.622740078658578, 0, 2.6738302846565, 0, 4.30156261189159,
0, 3.04319377260997, 0, 0.995571956296118, 0, 0.113913040814712,
0, 0.00121730932738073, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.00121730932738073, 0, 0.113913040814712, 0, 0.995571956296118,
0, 3.04319377260997, 0, 4.30156261189159, 0, 2.67383028465651,
0, 0.622740078658578, 0, 0.0312738163856595, 0, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00180999093529261, 0,
0.1245002417436, 0, 0.995571956296118, 0, 2.67383028465651, 0,
2.69320117577294, 0, 0.857954760387479, 0, 0.0633262258769764,
0, 0.000112496007290721, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.00180999093529261, 0, 0.113913040814712, 0, 0.622740078658578,
0, 0.857954760387479, 0, 0.321435192757127, 0, 0.0225489224875791,
0, 1.08601623125006e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.00121730932738073, 0, 0.0312738163856595, 0,
0.0633262258769764, 0, 0.0225489224875791, 0, 0.000625061236957296,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1.08654289635091e-05, 0, 0.000112496007290721, 0, 1.08601623125006e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 5.05673652316797e-10, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 3.4496711175134e-05, 0, 0.0015701089925862, 0, 0.003896397104994,
0, 0.00157213610851392, 0, 3.45007275290915e-05, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3.44967111751343e-05, 0, 0.00872963127077592, 0, 0.0661544886375021,
0, 0.124017651192734, 0, 0.0759808782995632, 0, 0.0120248586792744,
0, 9.61902739128458e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157010899258622, 0, 0.0661544886375023,
0, 0.375690548876713, 0, 0.723386677289052, 0, 0.549050844946753,
0, 0.168170211647942, 0, 0.0153085619417029, 0, 9.53250421932223e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316932e-10,
0, 0.00389639710499403, 0, 0.124017651192734, 0, 0.723386677289053,
0, 1.59744074560367, 0, 1.53908699828218, 0, 0.744223319422889,
0, 0.178596350346924, 0, 0.0153052083946846, 0, 9.57044116187268e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157213610851394,
0, 0.0759808782995634, 0, 0.549050844946754, 0, 1.53908699828218,
0, 2.16411927874826, 0, 1.67691161703102, 0, 0.75100032952569,
0, 0.178611592076099, 0, 0.015317207123465, 0, 9.6417509205864e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290925e-05,
0, 0.0120248586792744, 0, 0.168170211647942, 0, 0.744223319422889,
0, 1.67691161703102, 0, 2.19167865690685, 0, 1.67725643503247,
0, 0.751057520686108, 0, 0.17863784828745, 0, 0.0153172071234649,
0, 9.57044116187268e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 9.61902739128473e-05, 0, 0.0153085619417029, 0,
0.178596350346924, 0, 0.751000329525689, 0, 1.67725643503247,
0, 2.19421288675405, 0, 1.67673692684187, 0, 0.751057520686108,
0, 0.178611592076099, 0, 0.0153052083946846, 0, 9.53250421932245e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.53250421932241e-05,
0, 0.0153052083946846, 0, 0.178611592076099, 0, 0.751057520686108,
0, 1.67673692684187, 0, 2.19421288675405, 0, 1.67725643503247,
0, 0.751000329525689, 0, 0.178596350346924, 0, 0.0153085619417029,
0, 9.61902739128473e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 9.57044116187261e-05, 0, 0.0153172071234649, 0,
0.178637848287449, 0, 0.751057520686107, 0, 1.67725643503247,
0, 2.19167865690685, 0, 1.67691161703102, 0, 0.74422331942289,
0, 0.168170211647943, 0, 0.0120248586792745, 0, 3.45007275290925e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.64175092058634e-05,
0, 0.015317207123465, 0, 0.178611592076099, 0, 0.751000329525689,
0, 1.67691161703101, 0, 2.16411927874826, 0, 1.53908699828218,
0, 0.549050844946754, 0, 0.0759808782995635, 0, 0.00157213610851394,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187275e-05,
0, 0.0153052083946846, 0, 0.178596350346925, 0, 0.744223319422889,
0, 1.53908699828218, 0, 1.59744074560367, 0, 0.723386677289053,
0, 0.124017651192734, 0, 0.00389639710499402, 0, 5.05673652316932e-10,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.53250421932245e-05,
0, 0.015308561941703, 0, 0.168170211647942, 0, 0.549050844946754,
0, 0.723386677289053, 0, 0.375690548876713, 0, 0.0661544886375023,
0, 0.00157010899258621, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128465e-05, 0, 0.0120248586792744,
0, 0.0759808782995633, 0, 0.124017651192734, 0, 0.0661544886375021,
0, 0.00872963127077592, 0, 3.4496711175134e-05, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290909e-05,
0, 0.00157213610851391, 0, 0.00389639710499397, 0, 0.00157010899258619,
0, 3.44967111751334e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316797e-10,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0), .Dim = c(2L, 520L)), a_constraint = c(0, 0)))
expect_equal(make_constraint(MRA, constraint = "unconstrained", joint = FALSE),
list(A_constraint = NULL, a_constraint = NULL))
expect_equal(make_constraint(MRA, constraint = "overall", joint = FALSE),
list(A_constraint = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1.08601623125006e-05, 0.000112496007290721, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000625061236957296, 0.0225489224875791,
0.0633262258769764, 0.0312738163856595, 0.00121730932738073,
0, 0, 0, 0, 0, 0, 0, 0, 1.08601623125006e-05, 0.0225489224875791,
0.321435192757127, 0.857954760387478, 0.622740078658578, 0.113913040814712,
0.00180999093529261, 0, 0, 0, 0, 0, 0, 0, 0.000112496007290721,
0.0633262258769764, 0.857954760387478, 2.69320117577294, 2.6738302846565,
0.995571956296118, 0.1245002417436, 0.00180999093529261, 0, 0,
0, 0, 0, 0, 1.08654289635091e-05, 0.0312738163856595, 0.622740078658578,
2.6738302846565, 4.30156261189159, 3.04319377260997, 0.995571956296118,
0.113913040814712, 0.00121730932738073, 0, 0, 0, 0, 0, 0, 0.00121730932738073,
0.113913040814712, 0.995571956296118, 3.04319377260997, 4.30156261189159,
2.67383028465651, 0.622740078658578, 0.0312738163856595, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0.00180999093529261, 0.1245002417436, 0.995571956296118,
2.67383028465651, 2.69320117577294, 0.857954760387479, 0.0633262258769764,
0.000112496007290721, 0, 0, 0, 0, 0, 0, 0, 0.00180999093529261,
0.113913040814712, 0.622740078658578, 0.857954760387479, 0.321435192757127,
0.0225489224875791, 1.08601623125006e-05, 0, 0, 0, 0, 0, 0, 0,
0, 0.00121730932738073, 0.0312738163856595, 0.0633262258769764,
0.0225489224875791, 0.000625061236957296, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1.08654289635091e-05, 0.000112496007290721, 1.08601623125006e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316797e-10,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.4496711175134e-05,
0.0015701089925862, 0.003896397104994, 0.00157213610851392, 3.45007275290915e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.44967111751343e-05, 0.00872963127077592,
0.0661544886375021, 0.124017651192734, 0.0759808782995632, 0.0120248586792744,
9.61902739128458e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157010899258622,
0.0661544886375023, 0.375690548876713, 0.723386677289052, 0.549050844946753,
0.168170211647942, 0.0153085619417029, 9.53250421932223e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316932e-10, 0.00389639710499403,
0.124017651192734, 0.723386677289053, 1.59744074560367, 1.53908699828218,
0.744223319422889, 0.178596350346924, 0.0153052083946846, 9.57044116187268e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157213610851394, 0.0759808782995634,
0.549050844946754, 1.53908699828218, 2.16411927874826, 1.67691161703102,
0.75100032952569, 0.178611592076099, 0.015317207123465, 9.6417509205864e-05,
0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290925e-05, 0.0120248586792744,
0.168170211647942, 0.744223319422889, 1.67691161703102, 2.19167865690685,
1.67725643503247, 0.751057520686108, 0.17863784828745, 0.0153172071234649,
9.57044116187268e-05, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128473e-05,
0.0153085619417029, 0.178596350346924, 0.751000329525689, 1.67725643503247,
2.19421288675405, 1.67673692684187, 0.751057520686108, 0.178611592076099,
0.0153052083946846, 9.53250421932245e-05, 0, 0, 0, 0, 0, 0, 0,
0, 9.53250421932241e-05, 0.0153052083946846, 0.178611592076099,
0.751057520686108, 1.67673692684187, 2.19421288675405, 1.67725643503247,
0.751000329525689, 0.178596350346924, 0.0153085619417029, 9.61902739128473e-05,
0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187261e-05, 0.0153172071234649,
0.178637848287449, 0.751057520686107, 1.67725643503247, 2.19167865690685,
1.67691161703102, 0.74422331942289, 0.168170211647943, 0.0120248586792745,
3.45007275290925e-05, 0, 0, 0, 0, 0, 0, 0, 0, 9.64175092058634e-05,
0.015317207123465, 0.178611592076099, 0.751000329525689, 1.67691161703101,
2.16411927874826, 1.53908699828218, 0.549050844946754, 0.0759808782995635,
0.00157213610851394, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187275e-05,
0.0153052083946846, 0.178596350346925, 0.744223319422889, 1.53908699828218,
1.59744074560367, 0.723386677289053, 0.124017651192734, 0.00389639710499402,
5.05673652316932e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.53250421932245e-05,
0.015308561941703, 0.168170211647942, 0.549050844946754, 0.723386677289053,
0.375690548876713, 0.0661544886375023, 0.00157010899258621, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128465e-05, 0.0120248586792744,
0.0759808782995633, 0.124017651192734, 0.0661544886375021, 0.00872963127077592,
3.4496711175134e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290909e-05,
0.00157213610851391, 0.00389639710499397, 0.00157010899258619,
3.44967111751334e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 5.05673652316797e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(1L, 520L)), a_constraint = 0))
expect_equal(make_constraint(MRA, constraint = "resolution", joint = FALSE),
list(A_constraint = list(structure(c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1.08601623125006e-05, 0.000112496007290721, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000625061236957296, 0.0225489224875791,
0.0633262258769764, 0.0312738163856595, 0.00121730932738073,
0, 0, 0, 0, 0, 0, 0, 0, 1.08601623125006e-05, 0.0225489224875791,
0.321435192757127, 0.857954760387478, 0.622740078658578, 0.113913040814712,
0.00180999093529261, 0, 0, 0, 0, 0, 0, 0, 0.000112496007290721,
0.0633262258769764, 0.857954760387478, 2.69320117577294, 2.6738302846565,
0.995571956296118, 0.1245002417436, 0.00180999093529261, 0, 0,
0, 0, 0, 0, 1.08654289635091e-05, 0.0312738163856595, 0.622740078658578,
2.6738302846565, 4.30156261189159, 3.04319377260997, 0.995571956296118,
0.113913040814712, 0.00121730932738073, 0, 0, 0, 0, 0, 0, 0.00121730932738073,
0.113913040814712, 0.995571956296118, 3.04319377260997, 4.30156261189159,
2.67383028465651, 0.622740078658578, 0.0312738163856595, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0.00180999093529261, 0.1245002417436, 0.995571956296118,
2.67383028465651, 2.69320117577294, 0.857954760387479, 0.0633262258769764,
0.000112496007290721, 0, 0, 0, 0, 0, 0, 0, 0.00180999093529261,
0.113913040814712, 0.622740078658578, 0.857954760387479, 0.321435192757127,
0.0225489224875791, 1.08601623125006e-05, 0, 0, 0, 0, 0, 0, 0,
0, 0.00121730932738073, 0.0312738163856595, 0.0633262258769764,
0.0225489224875791, 0.000625061236957296, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1.08654289635091e-05, 0.000112496007290721, 1.08601623125006e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(1L, 196L)),
structure(c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 5.05673652316797e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 3.4496711175134e-05, 0.0015701089925862, 0.003896397104994,
0.00157213610851392, 3.45007275290915e-05, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 3.44967111751343e-05, 0.00872963127077592,
0.0661544886375021, 0.124017651192734, 0.0759808782995632, 0.0120248586792744,
9.61902739128458e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157010899258622,
0.0661544886375023, 0.375690548876713, 0.723386677289052, 0.549050844946753,
0.168170211647942, 0.0153085619417029, 9.53250421932223e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316932e-10, 0.00389639710499403,
0.124017651192734, 0.723386677289053, 1.59744074560367, 1.53908699828218,
0.744223319422889, 0.178596350346924, 0.0153052083946846, 9.57044116187268e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157213610851394, 0.0759808782995634,
0.549050844946754, 1.53908699828218, 2.16411927874826, 1.67691161703102,
0.75100032952569, 0.178611592076099, 0.015317207123465, 9.6417509205864e-05,
0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290925e-05, 0.0120248586792744,
0.168170211647942, 0.744223319422889, 1.67691161703102, 2.19167865690685,
1.67725643503247, 0.751057520686108, 0.17863784828745, 0.0153172071234649,
9.57044116187268e-05, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128473e-05,
0.0153085619417029, 0.178596350346924, 0.751000329525689, 1.67725643503247,
2.19421288675405, 1.67673692684187, 0.751057520686108, 0.178611592076099,
0.0153052083946846, 9.53250421932245e-05, 0, 0, 0, 0, 0, 0, 0,
0, 9.53250421932241e-05, 0.0153052083946846, 0.178611592076099,
0.751057520686108, 1.67673692684187, 2.19421288675405, 1.67725643503247,
0.751000329525689, 0.178596350346924, 0.0153085619417029, 9.61902739128473e-05,
0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187261e-05, 0.0153172071234649,
0.178637848287449, 0.751057520686107, 1.67725643503247, 2.19167865690685,
1.67691161703102, 0.74422331942289, 0.168170211647943, 0.0120248586792745,
3.45007275290925e-05, 0, 0, 0, 0, 0, 0, 0, 0, 9.64175092058634e-05,
0.015317207123465, 0.178611592076099, 0.751000329525689, 1.67691161703101,
2.16411927874826, 1.53908699828218, 0.549050844946754, 0.0759808782995635,
0.00157213610851394, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187275e-05,
0.0153052083946846, 0.178596350346925, 0.744223319422889, 1.53908699828218,
1.59744074560367, 0.723386677289053, 0.124017651192734, 0.00389639710499402,
5.05673652316932e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.53250421932245e-05,
0.015308561941703, 0.168170211647942, 0.549050844946754, 0.723386677289053,
0.375690548876713, 0.0661544886375023, 0.00157010899258621, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128465e-05, 0.0120248586792744,
0.0759808782995633, 0.124017651192734, 0.0661544886375021, 0.00872963127077592,
3.4496711175134e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290909e-05,
0.00157213610851391, 0.00389639710499397, 0.00157010899258619,
3.44967111751334e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 5.05673652316797e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(1L, 324L))), a_constraint = list(
0, 0)))
MRA <- mra_wendland_2d(locs, M = 2, n_coarse_grid = 4)
class(MRA) <- NULL
expect_error(make_constraint(MRA), 'MRA must be of class "mra_wendland_2d" which is the output of mra_wendland_1d()')
class(MRA) <- "aaa"
expect_error(make_constraint(MRA), 'MRA must be of class "mra_wendland_2d" which is the output of mra_wendland_1d()')
MRA <- mra_wendland_2d(locs, M = 2, n_coarse_grid = 4)
expect_error(make_constraint(MRA, constraint = "predicted"), 'constraint = \"predicted\" is not currently supported -- developer note: add W_pred to function call to enable this in future results')
expect_error(make_constraint(MRA, constraint = "adsfs"), 'constraint must be either \"unconstrained\", \"overall\", \"resolution\", or \"predicted\"')
expect_error(make_constraint(MRA, constraint = TRUE), 'constraint must be either \"unconstrained\", \"overall\", \"resolution\", or \"predicted\"')
expect_error(make_constraint(MRA, constraint = 33), 'constraint must be either \"unconstrained\", \"overall\", \"resolution\", or \"predicted\"')
expect_error(make_constraint(MRA, constraint = NA), 'constraint must be either \"unconstrained\", \"overall\", \"resolution\", or \"predicted\"')
expect_error(make_constraint(MRA, joint = "TRUE"), 'joint must be either TRUE or FALSE.')
expect_error(make_constraint(MRA, joint = 35), 'joint must be either TRUE or FALSE.')
expect_error(make_constraint(MRA, joint = NA), 'joint must be either TRUE or FALSE.')
})
jtipton25/BayesMRA documentation built on Feb. 28, 2024, 1:27 p.m.
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context("testing precision matrices")
# test_that("make_Q", {
#
# expect_error(make_Q(4, "aaa"), "phi must be between -1 and 1")
# expect_error(make_Q(4, NA), "phi must be between -1 and 1")
# expect_error(make_Q(4, -2), "phi must be between -1 and 1")
# expect_error(make_Q(4, 2), "phi must be between -1 and 1")
# make_Q(4, 0.9)
# # expect_error(wendland_basis(1:10, 1:10), "radius must be a single positive numeric value")
# # expect_error(wendland_basis(-10:10, 5), "d must be nonnegative")
# # expect_error(wendland_basis(c(1:10, NA), 2), "d must not contain missing values")
# # expect_equal(wendland_basis(1:5, 3), c(0.37717827566936, 0.013971447441955, 0, 0, 0))
# })
test_that("make_Q_alpha_2d", {
# expect_identical(
# make_Q_alpha_2d(4, 0.5),
# structure(list(new("spam",
# entries = c(2, -0.5, -0.5, -0.5, 3, -0.5, -0.5,
# -0.5, 3, -0.5, -0.5, -0.5, 2, -0.5, -0.5, 3, -0.5, -0.5, -0.5,
# -0.5, 4, -0.5, -0.5, -0.5, -0.5, 4, -0.5, -0.5, -0.5, -0.5, 3,
# -0.5, -0.5, 3, -0.5, -0.5, -0.5, -0.5, 4, -0.5, -0.5, -0.5, -0.5,
# 4, -0.5, -0.5, -0.5, -0.5, 3, -0.5, -0.5, 2, -0.5, -0.5, -0.5,
# 3, -0.5, -0.5, -0.5, 3, -0.5, -0.5, -0.5, 2),
# colindices = c(1L,
# 2L, 5L, 1L, 2L, 3L, 6L, 2L, 3L, 4L, 7L, 3L, 4L, 8L, 1L, 5L, 6L,
# 9L, 2L, 5L, 6L, 7L, 10L, 3L, 6L, 7L, 8L, 11L, 4L, 7L, 8L, 12L,
# 5L, 9L, 10L, 13L, 6L, 9L, 10L, 11L, 14L, 7L, 10L, 11L, 12L, 15L,
# 8L, 11L, 12L, 16L, 9L, 13L, 14L, 10L, 13L, 14L, 15L, 11L, 14L,
# 15L, 16L, 12L, 15L, 16L),
# rowpointers = c(1L, 4L, 8L, 12L, 15L,
# 19L, 24L, 29L, 33L, 37L, 42L, 47L, 51L, 54L, 58L, 62L, 65L),
# dimension = c(16L, 16L))),
# class = "Q_alpha"
# )
# )
n_dims <- c(4, 16)
phi <- rep(1, 3)
expect_error(make_Q_alpha_2d(n_dims, phi), "n_dims and phi must both be vectors of length M.")
n_dims <- c(4, 16, 32)
phi <- rep(1, 2)
expect_error(make_Q_alpha_2d(n_dims, phi), "n_dims and phi must both be vectors of length M.")
n_dims <- c(4, 16)
phi <- rep(NA, 2)
expect_error(make_Q_alpha_2d(n_dims, phi), "phi must be a numeric vector of length M with values between -1 and 1.")
phi <- rep(2, 2)
expect_error(make_Q_alpha_2d(n_dims, phi), "phi must be a numeric vector of length M with values between -1 and 1.")
phi <- rep(-2, 2)
expect_error(make_Q_alpha_2d(n_dims, phi), "phi must be a numeric vector of length M with values between -1 and 1.")
expect_error(make_Q_alpha_2d(4, 1.5), "phi must be a numeric vector of length M with values between -1 and 1.")
expect_error(make_Q_alpha_2d(4, -1.5), "phi must be a numeric vector of length M with values between -1 and 1.")
expect_error(make_Q_alpha_2d(c(2, 4), c(0.5, 1.5)), "phi must be a numeric vector of length M with values between -1 and 1.")
expect_error(make_Q_alpha_2d(c(2, 4), c(0.5, NA)), "phi must be a numeric vector of length M with values between -1 and 1.")
expect_error(make_Q_alpha_2d(c(2, 4), c(0.5, "aaa")), "phi must be a numeric vector of length M with values between -1 and 1.")
expect_error(make_Q_alpha_2d(c(2, NA), c(0.5, 0.5)), "n_dims must be a vector of integers of length M.")
expect_error(make_Q_alpha_2d(c(2, "aaa"), c(0.5, 0.5)), "n_dims must be a vector of integers of length M.")
expect_error(make_Q_alpha_2d(c(2, 3.5), c(0.5, 0.5)), "n_dims must be a vector of integers of length M.")
n_dims <- c(4, 16)
phi <- rep(1, 2)
expect_error(make_Q_alpha_2d(n_dims, phi, use_spam = "TRUE"), "use_spam must be either TRUE or FALSE.")
expect_error(make_Q_alpha_2d(n_dims, phi, use_spam = 3.5), "use_spam must be either TRUE or FALSE.")
expect_error(make_Q_alpha_2d(n_dims, phi, use_spam = NA), "use_spam must be either TRUE or FALSE.")
expect_error(make_Q_alpha_2d(n_dims, phi, prec_model = "AAA"), 'The only valid options for prec_model are \"CAR\" and \"SAR\".')
expect_error(make_Q_alpha_2d(n_dims, phi, prec_model = NA), 'The only valid options for prec_model are \"CAR\" and \"SAR\".')
expect_error(make_Q_alpha_2d(n_dims, phi, prec_model = 32), 'The only valid options for prec_model are \"CAR\" and \"SAR\".')
})
test_that("make_Q_alpha_tau2", {
n_dims <- c(4, 16)
phi <- c(1, 1)
tau2 <- c(2, 2)
Q_alpha <- make_Q_alpha_2d(n_dims, phi)
class(Q_alpha) <- "aaa"
expect_error(make_Q_alpha_tau2(Q_alpha, tau2), 'Q_alpha must by of class "Q_alpha" which is the output of make_Q_alpha_2d\\(\\)')
class(Q_alpha) <- "Q_alpha"
tau2 <- 1:3
expect_error(make_Q_alpha_tau2(Q_alpha, tau2), "Q_alpha must be a list of length M and tau2 must be a positive numeric vector of length M.")
tau2 <- c(1, NA)
expect_error(make_Q_alpha_tau2(Q_alpha, tau2), "tau2 must be a positive numeric vector of length M.")
tau2 <- c(1, "aaa")
expect_error(make_Q_alpha_tau2(Q_alpha, tau2), "tau2 must be a positive numeric vector of length M.")
tau2 <- c(1, -1)
expect_error(make_Q_alpha_tau2(Q_alpha, tau2), "tau2 must be a positive numeric vector of length M.")
tau2 <- 1:2
expect_error(make_Q_alpha_tau2(Q_alpha, tau2, use_spam = "TRUE"), "use_spam must be either TRUE or FALSE.")
expect_error(make_Q_alpha_tau2(Q_alpha, tau2, use_spam = 3.5), "use_spam must be either TRUE or FALSE.")
expect_error(make_Q_alpha_tau2(Q_alpha, tau2, use_spam = NA), "use_spam must be either TRUE or FALSE.")
expect_equal({
n_dims <- c(2, 4)
phi <- c(1, 1)
Q_alpha <- make_Q_alpha_2d(n_dims, phi, prec_model = "SAR")
tau2 <- c(3, 5)
make_Q_alpha_tau2(Q_alpha, tau2)
},
{
new("spam", entries = c(4.5, -3, -3, 1.5, -3, 4.5, 1.5, -3, -3,
1.5, 4.5, -3, 1.5, -3, -3, 4.5, 7.5, -4.16666666666667, 0.833333333333333,
-4.16666666666667, 1.25, 0.833333333333333, -4.16666666666667,
6.66666666666667, -3.33333333333333, 0.833333333333333, 1.11111111111111,
-2.91666666666667, 0.833333333333333, 0.416666666666667, 0.833333333333333,
-3.33333333333333, 6.66666666666667, -4.16666666666667, 0.833333333333333,
-2.91666666666667, 1.11111111111111, 0.416666666666667, 0.833333333333333,
-4.16666666666667, 7.5, 1.25, -4.16666666666667, 0.833333333333333,
-4.16666666666667, 1.11111111111111, 6.66666666666667, -2.91666666666667,
0.416666666666667, -3.33333333333333, 0.833333333333333, 0.833333333333333,
1.25, -2.91666666666667, 0.833333333333333, -2.91666666666667,
6.25, -2.5, 0.416666666666667, 0.833333333333333, -2.5, 0.625,
0.416666666666667, 0.833333333333333, -2.91666666666667, 1.25,
0.416666666666667, -2.5, 6.25, -2.91666666666667, 0.625, -2.5,
0.833333333333333, 0.416666666666667, 1.11111111111111, -4.16666666666667,
0.416666666666667, -2.91666666666667, 6.66666666666667, 0.833333333333333,
-3.33333333333333, 0.833333333333333, 0.833333333333333, -3.33333333333333,
0.833333333333333, 6.66666666666667, -2.91666666666667, 0.416666666666667,
-4.16666666666667, 1.11111111111111, 0.416666666666667, 0.833333333333333,
-2.5, 0.625, -2.91666666666667, 6.25, -2.5, 0.416666666666667,
1.25, -2.91666666666667, 0.833333333333333, 0.416666666666667,
0.625, -2.5, 0.833333333333333, 0.416666666666667, -2.5, 6.25,
-2.91666666666667, 0.833333333333333, -2.91666666666667, 1.25,
0.833333333333333, 0.833333333333333, -3.33333333333333, 0.416666666666667,
-2.91666666666667, 6.66666666666667, 1.11111111111111, -4.16666666666667,
0.833333333333333, -4.16666666666667, 1.25, 7.5, -4.16666666666667,
0.833333333333333, 0.416666666666667, 1.11111111111111, -2.91666666666667,
0.833333333333333, -4.16666666666667, 6.66666666666667, -3.33333333333333,
0.833333333333333, 0.416666666666667, 0.833333333333333, -2.91666666666667,
1.11111111111111, 0.833333333333333, -3.33333333333333, 6.66666666666667,
-4.16666666666667, 0.833333333333333, 1.25, -4.16666666666667,
0.833333333333333, -4.16666666666667, 7.5),
colindices = c(1L,
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 9L, 10L, 13L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 14L, 5L,
6L, 7L, 8L, 10L, 11L, 12L, 15L, 6L, 7L, 8L, 11L, 12L, 16L, 5L,
6L, 9L, 10L, 11L, 13L, 14L, 17L, 5L, 6L, 7L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 18L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 14L, 15L,
16L, 19L, 7L, 8L, 10L, 11L, 12L, 15L, 16L, 20L, 5L, 9L, 10L,
13L, 14L, 15L, 17L, 18L, 6L, 9L, 10L, 11L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 7L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 18L, 19L,
20L, 8L, 11L, 12L, 14L, 15L, 16L, 19L, 20L, 9L, 13L, 14L, 17L,
18L, 19L, 10L, 13L, 14L, 15L, 17L, 18L, 19L, 20L, 11L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 12L, 15L, 16L, 18L, 19L, 20L),
rowpointers = c(1L,
5L, 9L, 13L, 17L, 23L, 31L, 39L, 45L, 53L, 64L, 75L, 83L, 91L,
102L, 113L, 121L, 127L, 135L, 143L, 149L),
dimension = c(20L,
20L)
)
})
expect_identical({
n_dims <- c(2, 4)
phi <- c(1, 1)
Q_alpha <- make_Q_alpha_2d(n_dims, phi)
tau2 <- c(3, 5)
make_Q_alpha_tau2(Q_alpha, tau2)
},
{
new("spam",
entries = c(6, -3, -3, -3, 6, -3, -3, 6, -3, -3,
-3, 6, 10, -5, -5, -5, 15, -5, -5, -5, 15, -5, -5, -5, 10, -5,
-5, 15, -5, -5, -5, -5, 20, -5, -5, -5, -5, 20, -5, -5, -5, -5,
15, -5, -5, 15, -5, -5, -5, -5, 20, -5, -5, -5, -5, 20, -5, -5,
-5, -5, 15, -5, -5, 10, -5, -5, -5, 15, -5, -5, -5, 15, -5, -5,
-5, 10),
colindices = c(1L, 2L, 3L, 1L, 2L, 4L, 1L, 3L, 4L, 2L,
3L, 4L, 5L, 6L, 9L, 5L, 6L, 7L, 10L, 6L, 7L, 8L, 11L, 7L, 8L,
12L, 5L, 9L, 10L, 13L, 6L, 9L, 10L, 11L, 14L, 7L, 10L, 11L, 12L,
15L, 8L, 11L, 12L, 16L, 9L, 13L, 14L, 17L, 10L, 13L, 14L, 15L,
18L, 11L, 14L, 15L, 16L, 19L, 12L, 15L, 16L, 20L, 13L, 17L, 18L,
14L, 17L, 18L, 19L, 15L, 18L, 19L, 20L, 16L, 19L, 20L),
rowpointers = c(1L,
4L, 7L, 10L, 13L, 16L, 20L, 24L, 27L, 31L, 36L, 41L, 45L, 49L,
54L, 59L, 63L, 66L, 70L, 74L, 77L),
dimension = c(20L, 20L))
})
expect_equal({
n_dims <- c(2, 4)
phi <- c(1, 1)
Q_alpha <- make_Q_alpha_2d(n_dims, phi, prec_model = "SAR", use_spam = FALSE)
tau2 <- c(3, 5)
make_Q_alpha_tau2(Q_alpha, tau2, use_spam = FALSE)
},
{
new("dgCMatrix",
i = c(0L, 1L, 2L, 3L, 0L, 1L, 2L, 3L, 0L, 1L,
2L, 3L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 8L, 9L, 12L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 13L, 4L, 5L, 6L, 7L, 9L, 10L, 11L, 14L, 5L,
6L, 7L, 10L, 11L, 15L, 4L, 5L, 8L, 9L, 10L, 12L, 13L, 16L, 4L,
5L, 6L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 17L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 13L, 14L, 15L, 18L, 6L, 7L, 9L, 10L, 11L, 14L,
15L, 19L, 4L, 8L, 9L, 12L, 13L, 14L, 16L, 17L, 5L, 8L, 9L, 10L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 6L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 17L, 18L, 19L, 7L, 10L, 11L, 13L, 14L, 15L, 18L, 19L,
8L, 12L, 13L, 16L, 17L, 18L, 9L, 12L, 13L, 14L, 16L, 17L, 18L,
19L, 10L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 11L, 14L, 15L, 17L,
18L, 19L),
p = c(0L, 4L, 8L, 12L, 16L, 22L, 30L, 38L, 44L, 52L,
63L, 74L, 82L, 90L, 101L, 112L, 120L, 126L, 134L, 142L, 148L),
Dim = c(20L, 20L),
Dimnames = list(NULL, NULL),
x = c(4.5,
-3, -3, 1.5, -3, 4.5, 1.5, -3, -3, 1.5, 4.5, -3, 1.5, -3,
-3, 4.5, 7.5, -4.16666666666667, 0.833333333333333, -4.16666666666667,
1.25, 0.833333333333333, -4.16666666666667, 6.66666666666667,
-3.33333333333333, 0.833333333333333, 1.11111111111111, -2.91666666666667,
0.833333333333333, 0.416666666666667, 0.833333333333333,
-3.33333333333333, 6.66666666666667, -4.16666666666667, 0.833333333333333,
-2.91666666666667, 1.11111111111111, 0.416666666666667, 0.833333333333333,
-4.16666666666667, 7.5, 1.25, -4.16666666666667, 0.833333333333333,
-4.16666666666667, 1.11111111111111, 6.66666666666667, -2.91666666666667,
0.416666666666667, -3.33333333333333, 0.833333333333333,
0.833333333333333, 1.25, -2.91666666666667, 0.833333333333333,
-2.91666666666667, 6.25, -2.5, 0.416666666666667, 0.833333333333333,
-2.5, 0.625, 0.416666666666667, 0.833333333333333, -2.91666666666667,
1.25, 0.416666666666667, -2.5, 6.25, -2.91666666666667, 0.625,
-2.5, 0.833333333333333, 0.416666666666667, 1.11111111111111,
-4.16666666666667, 0.416666666666667, -2.91666666666667,
6.66666666666667, 0.833333333333333, -3.33333333333333, 0.833333333333333,
0.833333333333333, -3.33333333333333, 0.833333333333333,
6.66666666666667, -2.91666666666667, 0.416666666666667, -4.16666666666667,
1.11111111111111, 0.416666666666667, 0.833333333333333, -2.5,
0.625, -2.91666666666667, 6.25, -2.5, 0.416666666666667,
1.25, -2.91666666666667, 0.833333333333333, 0.416666666666667,
0.625, -2.5, 0.833333333333333, 0.416666666666667, -2.5,
6.25, -2.91666666666667, 0.833333333333333, -2.91666666666667,
1.25, 0.833333333333333, 0.833333333333333, -3.33333333333333,
0.416666666666667, -2.91666666666667, 6.66666666666667, 1.11111111111111,
-4.16666666666667, 0.833333333333333, -4.16666666666667,
1.25, 7.5, -4.16666666666667, 0.833333333333333, 0.416666666666667,
1.11111111111111, -2.91666666666667, 0.833333333333333, -4.16666666666667,
6.66666666666667, -3.33333333333333, 0.833333333333333, 0.416666666666667,
0.833333333333333, -2.91666666666667, 1.11111111111111, 0.833333333333333,
-3.33333333333333, 6.66666666666667, -4.16666666666667, 0.833333333333333,
1.25, -4.16666666666667, 0.833333333333333, -4.16666666666667,
7.5),
factors = list())
})
expect_identical({
n_dims <- c(2, 4)
phi <- c(1, 1)
Q_alpha <- make_Q_alpha_2d(n_dims, phi, use_spam = FALSE)
tau2 <- c(3, 5)
make_Q_alpha_tau2(Q_alpha, tau2, use_spam = FALSE)
},
{
new("dgCMatrix",
i = c(0L, 1L, 2L, 0L, 1L, 3L, 0L, 2L, 3L, 1L,
2L, 3L, 4L, 5L, 8L, 4L, 5L, 6L, 9L, 5L, 6L, 7L, 10L, 6L, 7L,
11L, 4L, 8L, 9L, 12L, 5L, 8L, 9L, 10L, 13L, 6L, 9L, 10L, 11L,
14L, 7L, 10L, 11L, 15L, 8L, 12L, 13L, 16L, 9L, 12L, 13L, 14L,
17L, 10L, 13L, 14L, 15L, 18L, 11L, 14L, 15L, 19L, 12L, 16L, 17L,
13L, 16L, 17L, 18L, 14L, 17L, 18L, 19L, 15L, 18L, 19L),
p = c(0L,
3L, 6L, 9L, 12L, 15L, 19L, 23L, 26L, 30L, 35L, 40L, 44L, 48L,
53L, 58L, 62L, 65L, 69L, 73L, 76L),
Dim = c(20L, 20L),
Dimnames = list(NULL, NULL),
x = c(6, -3, -3, -3, 6, -3, -3, 6, -3, -3, -3,
6, 10, -5, -5, -5, 15, -5, -5, -5, 15, -5, -5, -5, 10, -5, -5,
15, -5, -5, -5, -5, 20, -5, -5, -5, -5, 20, -5, -5, -5, -5, 15,
-5, -5, 15, -5, -5, -5, -5, 20, -5, -5, -5, -5, 20, -5, -5, -5,
-5, 15, -5, -5, 10, -5, -5, -5, 15, -5, -5, -5, 15, -5, -5, -5,
10),
factors = list()
)
})
# locs <- matrix(1:20, 10, 2)
# MRA <- mra_wendland_2d(locs)
# locs_pred <- matrix(NA, 20, 2)
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), "locs_pred must be a numeric matrix with N rows and 2 columns")
#
# locs_pred <- matrix(1:30, 10, 3)
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), "locs_pred must be a numeric matrix with N rows and 2 columns")
#
# locs_pred <- matrix("11", 10, 2)
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), "locs_pred must be a numeric matrix with N rows and 2 columns")
#
# locs_pred <- 1:10
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), "locs_pred must be a numeric matrix with N rows and 2 columns")
#
# locs <- matrix(1:30, 10, 3)
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), "locs must be a numeric matrix with N rows and 2 columns")
#
# locs <- matrix(1:20, 10, 2)
# locs_pred <- matrix(1:20, 10, 2)
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA, use_spam = 3.5), "use_spam must be either TRUE or FALSE")
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA, use_spam = NA), "use_spam must be either TRUE or FALSE")
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA, use_spam = "aaa"), "use_spam must be either TRUE or FALSE")
#
# class(MRA) <- NULL
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), 'MRA must be of class "mra_wendland_2d"')
#
# class(MRA) <- "XXX"
# expect_error(mra_wendland_2d_pred(locs, locs_pred, MRA), 'MRA must be of class "mra_wendland_2d"')
})
test_that("test_constraint", {
locs <- matrix(1:20, 10, 2)
MRA <- mra_wendland_2d(locs, M = 2, n_coarse_grid = 4)
expect_equal(make_constraint(MRA, constraint = "unconstrained"),
list(A_constraint = NULL, a_constraint = NULL))
expect_equal(make_constraint(MRA, constraint = "overall"),
list(A_constraint = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1.08601623125006e-05, 0.000112496007290721, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000625061236957296, 0.0225489224875791,
0.0633262258769764, 0.0312738163856595, 0.00121730932738073,
0, 0, 0, 0, 0, 0, 0, 0, 1.08601623125006e-05, 0.0225489224875791,
0.321435192757127, 0.857954760387478, 0.622740078658578, 0.113913040814712,
0.00180999093529261, 0, 0, 0, 0, 0, 0, 0, 0.000112496007290721,
0.0633262258769764, 0.857954760387478, 2.69320117577294, 2.6738302846565,
0.995571956296118, 0.1245002417436, 0.00180999093529261, 0, 0,
0, 0, 0, 0, 1.08654289635091e-05, 0.0312738163856595, 0.622740078658578,
2.6738302846565, 4.30156261189159, 3.04319377260997, 0.995571956296118,
0.113913040814712, 0.00121730932738073, 0, 0, 0, 0, 0, 0, 0.00121730932738073,
0.113913040814712, 0.995571956296118, 3.04319377260997, 4.30156261189159,
2.67383028465651, 0.622740078658578, 0.0312738163856595, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0.00180999093529261, 0.1245002417436, 0.995571956296118,
2.67383028465651, 2.69320117577294, 0.857954760387479, 0.0633262258769764,
0.000112496007290721, 0, 0, 0, 0, 0, 0, 0, 0.00180999093529261,
0.113913040814712, 0.622740078658578, 0.857954760387479, 0.321435192757127,
0.0225489224875791, 1.08601623125006e-05, 0, 0, 0, 0, 0, 0, 0,
0, 0.00121730932738073, 0.0312738163856595, 0.0633262258769764,
0.0225489224875791, 0.000625061236957296, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1.08654289635091e-05, 0.000112496007290721, 1.08601623125006e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316797e-10,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.4496711175134e-05,
0.0015701089925862, 0.003896397104994, 0.00157213610851392, 3.45007275290915e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.44967111751343e-05, 0.00872963127077592,
0.0661544886375021, 0.124017651192734, 0.0759808782995632, 0.0120248586792744,
9.61902739128458e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157010899258622,
0.0661544886375023, 0.375690548876713, 0.723386677289052, 0.549050844946753,
0.168170211647942, 0.0153085619417029, 9.53250421932223e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316932e-10, 0.00389639710499403,
0.124017651192734, 0.723386677289053, 1.59744074560367, 1.53908699828218,
0.744223319422889, 0.178596350346924, 0.0153052083946846, 9.57044116187268e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157213610851394, 0.0759808782995634,
0.549050844946754, 1.53908699828218, 2.16411927874826, 1.67691161703102,
0.75100032952569, 0.178611592076099, 0.015317207123465, 9.6417509205864e-05,
0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290925e-05, 0.0120248586792744,
0.168170211647942, 0.744223319422889, 1.67691161703102, 2.19167865690685,
1.67725643503247, 0.751057520686108, 0.17863784828745, 0.0153172071234649,
9.57044116187268e-05, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128473e-05,
0.0153085619417029, 0.178596350346924, 0.751000329525689, 1.67725643503247,
2.19421288675405, 1.67673692684187, 0.751057520686108, 0.178611592076099,
0.0153052083946846, 9.53250421932245e-05, 0, 0, 0, 0, 0, 0, 0,
0, 9.53250421932241e-05, 0.0153052083946846, 0.178611592076099,
0.751057520686108, 1.67673692684187, 2.19421288675405, 1.67725643503247,
0.751000329525689, 0.178596350346924, 0.0153085619417029, 9.61902739128473e-05,
0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187261e-05, 0.0153172071234649,
0.178637848287449, 0.751057520686107, 1.67725643503247, 2.19167865690685,
1.67691161703102, 0.74422331942289, 0.168170211647943, 0.0120248586792745,
3.45007275290925e-05, 0, 0, 0, 0, 0, 0, 0, 0, 9.64175092058634e-05,
0.015317207123465, 0.178611592076099, 0.751000329525689, 1.67691161703101,
2.16411927874826, 1.53908699828218, 0.549050844946754, 0.0759808782995635,
0.00157213610851394, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187275e-05,
0.0153052083946846, 0.178596350346925, 0.744223319422889, 1.53908699828218,
1.59744074560367, 0.723386677289053, 0.124017651192734, 0.00389639710499402,
5.05673652316932e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.53250421932245e-05,
0.015308561941703, 0.168170211647942, 0.549050844946754, 0.723386677289053,
0.375690548876713, 0.0661544886375023, 0.00157010899258621, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128465e-05, 0.0120248586792744,
0.0759808782995633, 0.124017651192734, 0.0661544886375021, 0.00872963127077592,
3.4496711175134e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290909e-05,
0.00157213610851391, 0.00389639710499397, 0.00157010899258619,
3.44967111751334e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 5.05673652316797e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(1L, 520L)), a_constraint = 0))
expect_equal(make_constraint(MRA, constraint = "resolution"),
list(A_constraint = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.08601623125006e-05, 0,
0.000112496007290721, 0, 1.08654289635091e-05, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000625061236957296,
0, 0.0225489224875791, 0, 0.0633262258769764, 0, 0.0312738163856595,
0, 0.00121730932738073, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1.08601623125006e-05, 0, 0.0225489224875791, 0, 0.321435192757127,
0, 0.857954760387478, 0, 0.622740078658578, 0, 0.113913040814712,
0, 0.00180999093529261, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.000112496007290721, 0, 0.0633262258769764, 0, 0.857954760387478,
0, 2.69320117577294, 0, 2.6738302846565, 0, 0.995571956296118,
0, 0.1245002417436, 0, 0.00180999093529261, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1.08654289635091e-05, 0, 0.0312738163856595,
0, 0.622740078658578, 0, 2.6738302846565, 0, 4.30156261189159,
0, 3.04319377260997, 0, 0.995571956296118, 0, 0.113913040814712,
0, 0.00121730932738073, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.00121730932738073, 0, 0.113913040814712, 0, 0.995571956296118,
0, 3.04319377260997, 0, 4.30156261189159, 0, 2.67383028465651,
0, 0.622740078658578, 0, 0.0312738163856595, 0, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00180999093529261, 0,
0.1245002417436, 0, 0.995571956296118, 0, 2.67383028465651, 0,
2.69320117577294, 0, 0.857954760387479, 0, 0.0633262258769764,
0, 0.000112496007290721, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.00180999093529261, 0, 0.113913040814712, 0, 0.622740078658578,
0, 0.857954760387479, 0, 0.321435192757127, 0, 0.0225489224875791,
0, 1.08601623125006e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.00121730932738073, 0, 0.0312738163856595, 0,
0.0633262258769764, 0, 0.0225489224875791, 0, 0.000625061236957296,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1.08654289635091e-05, 0, 0.000112496007290721, 0, 1.08601623125006e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 5.05673652316797e-10, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 3.4496711175134e-05, 0, 0.0015701089925862, 0, 0.003896397104994,
0, 0.00157213610851392, 0, 3.45007275290915e-05, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3.44967111751343e-05, 0, 0.00872963127077592, 0, 0.0661544886375021,
0, 0.124017651192734, 0, 0.0759808782995632, 0, 0.0120248586792744,
0, 9.61902739128458e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157010899258622, 0, 0.0661544886375023,
0, 0.375690548876713, 0, 0.723386677289052, 0, 0.549050844946753,
0, 0.168170211647942, 0, 0.0153085619417029, 0, 9.53250421932223e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316932e-10,
0, 0.00389639710499403, 0, 0.124017651192734, 0, 0.723386677289053,
0, 1.59744074560367, 0, 1.53908699828218, 0, 0.744223319422889,
0, 0.178596350346924, 0, 0.0153052083946846, 0, 9.57044116187268e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157213610851394,
0, 0.0759808782995634, 0, 0.549050844946754, 0, 1.53908699828218,
0, 2.16411927874826, 0, 1.67691161703102, 0, 0.75100032952569,
0, 0.178611592076099, 0, 0.015317207123465, 0, 9.6417509205864e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290925e-05,
0, 0.0120248586792744, 0, 0.168170211647942, 0, 0.744223319422889,
0, 1.67691161703102, 0, 2.19167865690685, 0, 1.67725643503247,
0, 0.751057520686108, 0, 0.17863784828745, 0, 0.0153172071234649,
0, 9.57044116187268e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 9.61902739128473e-05, 0, 0.0153085619417029, 0,
0.178596350346924, 0, 0.751000329525689, 0, 1.67725643503247,
0, 2.19421288675405, 0, 1.67673692684187, 0, 0.751057520686108,
0, 0.178611592076099, 0, 0.0153052083946846, 0, 9.53250421932245e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.53250421932241e-05,
0, 0.0153052083946846, 0, 0.178611592076099, 0, 0.751057520686108,
0, 1.67673692684187, 0, 2.19421288675405, 0, 1.67725643503247,
0, 0.751000329525689, 0, 0.178596350346924, 0, 0.0153085619417029,
0, 9.61902739128473e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 9.57044116187261e-05, 0, 0.0153172071234649, 0,
0.178637848287449, 0, 0.751057520686107, 0, 1.67725643503247,
0, 2.19167865690685, 0, 1.67691161703102, 0, 0.74422331942289,
0, 0.168170211647943, 0, 0.0120248586792745, 0, 3.45007275290925e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.64175092058634e-05,
0, 0.015317207123465, 0, 0.178611592076099, 0, 0.751000329525689,
0, 1.67691161703101, 0, 2.16411927874826, 0, 1.53908699828218,
0, 0.549050844946754, 0, 0.0759808782995635, 0, 0.00157213610851394,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187275e-05,
0, 0.0153052083946846, 0, 0.178596350346925, 0, 0.744223319422889,
0, 1.53908699828218, 0, 1.59744074560367, 0, 0.723386677289053,
0, 0.124017651192734, 0, 0.00389639710499402, 0, 5.05673652316932e-10,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.53250421932245e-05,
0, 0.015308561941703, 0, 0.168170211647942, 0, 0.549050844946754,
0, 0.723386677289053, 0, 0.375690548876713, 0, 0.0661544886375023,
0, 0.00157010899258621, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128465e-05, 0, 0.0120248586792744,
0, 0.0759808782995633, 0, 0.124017651192734, 0, 0.0661544886375021,
0, 0.00872963127077592, 0, 3.4496711175134e-05, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290909e-05,
0, 0.00157213610851391, 0, 0.00389639710499397, 0, 0.00157010899258619,
0, 3.44967111751334e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316797e-10,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0), .Dim = c(2L, 520L)), a_constraint = c(0, 0)))
expect_equal(make_constraint(MRA, constraint = "unconstrained", joint = FALSE),
list(A_constraint = NULL, a_constraint = NULL))
expect_equal(make_constraint(MRA, constraint = "overall", joint = FALSE),
list(A_constraint = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1.08601623125006e-05, 0.000112496007290721, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000625061236957296, 0.0225489224875791,
0.0633262258769764, 0.0312738163856595, 0.00121730932738073,
0, 0, 0, 0, 0, 0, 0, 0, 1.08601623125006e-05, 0.0225489224875791,
0.321435192757127, 0.857954760387478, 0.622740078658578, 0.113913040814712,
0.00180999093529261, 0, 0, 0, 0, 0, 0, 0, 0.000112496007290721,
0.0633262258769764, 0.857954760387478, 2.69320117577294, 2.6738302846565,
0.995571956296118, 0.1245002417436, 0.00180999093529261, 0, 0,
0, 0, 0, 0, 1.08654289635091e-05, 0.0312738163856595, 0.622740078658578,
2.6738302846565, 4.30156261189159, 3.04319377260997, 0.995571956296118,
0.113913040814712, 0.00121730932738073, 0, 0, 0, 0, 0, 0, 0.00121730932738073,
0.113913040814712, 0.995571956296118, 3.04319377260997, 4.30156261189159,
2.67383028465651, 0.622740078658578, 0.0312738163856595, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0.00180999093529261, 0.1245002417436, 0.995571956296118,
2.67383028465651, 2.69320117577294, 0.857954760387479, 0.0633262258769764,
0.000112496007290721, 0, 0, 0, 0, 0, 0, 0, 0.00180999093529261,
0.113913040814712, 0.622740078658578, 0.857954760387479, 0.321435192757127,
0.0225489224875791, 1.08601623125006e-05, 0, 0, 0, 0, 0, 0, 0,
0, 0.00121730932738073, 0.0312738163856595, 0.0633262258769764,
0.0225489224875791, 0.000625061236957296, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1.08654289635091e-05, 0.000112496007290721, 1.08601623125006e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316797e-10,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.4496711175134e-05,
0.0015701089925862, 0.003896397104994, 0.00157213610851392, 3.45007275290915e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.44967111751343e-05, 0.00872963127077592,
0.0661544886375021, 0.124017651192734, 0.0759808782995632, 0.0120248586792744,
9.61902739128458e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157010899258622,
0.0661544886375023, 0.375690548876713, 0.723386677289052, 0.549050844946753,
0.168170211647942, 0.0153085619417029, 9.53250421932223e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316932e-10, 0.00389639710499403,
0.124017651192734, 0.723386677289053, 1.59744074560367, 1.53908699828218,
0.744223319422889, 0.178596350346924, 0.0153052083946846, 9.57044116187268e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157213610851394, 0.0759808782995634,
0.549050844946754, 1.53908699828218, 2.16411927874826, 1.67691161703102,
0.75100032952569, 0.178611592076099, 0.015317207123465, 9.6417509205864e-05,
0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290925e-05, 0.0120248586792744,
0.168170211647942, 0.744223319422889, 1.67691161703102, 2.19167865690685,
1.67725643503247, 0.751057520686108, 0.17863784828745, 0.0153172071234649,
9.57044116187268e-05, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128473e-05,
0.0153085619417029, 0.178596350346924, 0.751000329525689, 1.67725643503247,
2.19421288675405, 1.67673692684187, 0.751057520686108, 0.178611592076099,
0.0153052083946846, 9.53250421932245e-05, 0, 0, 0, 0, 0, 0, 0,
0, 9.53250421932241e-05, 0.0153052083946846, 0.178611592076099,
0.751057520686108, 1.67673692684187, 2.19421288675405, 1.67725643503247,
0.751000329525689, 0.178596350346924, 0.0153085619417029, 9.61902739128473e-05,
0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187261e-05, 0.0153172071234649,
0.178637848287449, 0.751057520686107, 1.67725643503247, 2.19167865690685,
1.67691161703102, 0.74422331942289, 0.168170211647943, 0.0120248586792745,
3.45007275290925e-05, 0, 0, 0, 0, 0, 0, 0, 0, 9.64175092058634e-05,
0.015317207123465, 0.178611592076099, 0.751000329525689, 1.67691161703101,
2.16411927874826, 1.53908699828218, 0.549050844946754, 0.0759808782995635,
0.00157213610851394, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187275e-05,
0.0153052083946846, 0.178596350346925, 0.744223319422889, 1.53908699828218,
1.59744074560367, 0.723386677289053, 0.124017651192734, 0.00389639710499402,
5.05673652316932e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.53250421932245e-05,
0.015308561941703, 0.168170211647942, 0.549050844946754, 0.723386677289053,
0.375690548876713, 0.0661544886375023, 0.00157010899258621, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128465e-05, 0.0120248586792744,
0.0759808782995633, 0.124017651192734, 0.0661544886375021, 0.00872963127077592,
3.4496711175134e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290909e-05,
0.00157213610851391, 0.00389639710499397, 0.00157010899258619,
3.44967111751334e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 5.05673652316797e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(1L, 520L)), a_constraint = 0))
expect_equal(make_constraint(MRA, constraint = "resolution", joint = FALSE),
list(A_constraint = list(structure(c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1.08601623125006e-05, 0.000112496007290721, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000625061236957296, 0.0225489224875791,
0.0633262258769764, 0.0312738163856595, 0.00121730932738073,
0, 0, 0, 0, 0, 0, 0, 0, 1.08601623125006e-05, 0.0225489224875791,
0.321435192757127, 0.857954760387478, 0.622740078658578, 0.113913040814712,
0.00180999093529261, 0, 0, 0, 0, 0, 0, 0, 0.000112496007290721,
0.0633262258769764, 0.857954760387478, 2.69320117577294, 2.6738302846565,
0.995571956296118, 0.1245002417436, 0.00180999093529261, 0, 0,
0, 0, 0, 0, 1.08654289635091e-05, 0.0312738163856595, 0.622740078658578,
2.6738302846565, 4.30156261189159, 3.04319377260997, 0.995571956296118,
0.113913040814712, 0.00121730932738073, 0, 0, 0, 0, 0, 0, 0.00121730932738073,
0.113913040814712, 0.995571956296118, 3.04319377260997, 4.30156261189159,
2.67383028465651, 0.622740078658578, 0.0312738163856595, 1.08654289635091e-05,
0, 0, 0, 0, 0, 0, 0.00180999093529261, 0.1245002417436, 0.995571956296118,
2.67383028465651, 2.69320117577294, 0.857954760387479, 0.0633262258769764,
0.000112496007290721, 0, 0, 0, 0, 0, 0, 0, 0.00180999093529261,
0.113913040814712, 0.622740078658578, 0.857954760387479, 0.321435192757127,
0.0225489224875791, 1.08601623125006e-05, 0, 0, 0, 0, 0, 0, 0,
0, 0.00121730932738073, 0.0312738163856595, 0.0633262258769764,
0.0225489224875791, 0.000625061236957296, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1.08654289635091e-05, 0.000112496007290721, 1.08601623125006e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(1L, 196L)),
structure(c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 5.05673652316797e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 3.4496711175134e-05, 0.0015701089925862, 0.003896397104994,
0.00157213610851392, 3.45007275290915e-05, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 3.44967111751343e-05, 0.00872963127077592,
0.0661544886375021, 0.124017651192734, 0.0759808782995632, 0.0120248586792744,
9.61902739128458e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157010899258622,
0.0661544886375023, 0.375690548876713, 0.723386677289052, 0.549050844946753,
0.168170211647942, 0.0153085619417029, 9.53250421932223e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 5.05673652316932e-10, 0.00389639710499403,
0.124017651192734, 0.723386677289053, 1.59744074560367, 1.53908699828218,
0.744223319422889, 0.178596350346924, 0.0153052083946846, 9.57044116187268e-05,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00157213610851394, 0.0759808782995634,
0.549050844946754, 1.53908699828218, 2.16411927874826, 1.67691161703102,
0.75100032952569, 0.178611592076099, 0.015317207123465, 9.6417509205864e-05,
0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290925e-05, 0.0120248586792744,
0.168170211647942, 0.744223319422889, 1.67691161703102, 2.19167865690685,
1.67725643503247, 0.751057520686108, 0.17863784828745, 0.0153172071234649,
9.57044116187268e-05, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128473e-05,
0.0153085619417029, 0.178596350346924, 0.751000329525689, 1.67725643503247,
2.19421288675405, 1.67673692684187, 0.751057520686108, 0.178611592076099,
0.0153052083946846, 9.53250421932245e-05, 0, 0, 0, 0, 0, 0, 0,
0, 9.53250421932241e-05, 0.0153052083946846, 0.178611592076099,
0.751057520686108, 1.67673692684187, 2.19421288675405, 1.67725643503247,
0.751000329525689, 0.178596350346924, 0.0153085619417029, 9.61902739128473e-05,
0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187261e-05, 0.0153172071234649,
0.178637848287449, 0.751057520686107, 1.67725643503247, 2.19167865690685,
1.67691161703102, 0.74422331942289, 0.168170211647943, 0.0120248586792745,
3.45007275290925e-05, 0, 0, 0, 0, 0, 0, 0, 0, 9.64175092058634e-05,
0.015317207123465, 0.178611592076099, 0.751000329525689, 1.67691161703101,
2.16411927874826, 1.53908699828218, 0.549050844946754, 0.0759808782995635,
0.00157213610851394, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.57044116187275e-05,
0.0153052083946846, 0.178596350346925, 0.744223319422889, 1.53908699828218,
1.59744074560367, 0.723386677289053, 0.124017651192734, 0.00389639710499402,
5.05673652316932e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.53250421932245e-05,
0.015308561941703, 0.168170211647942, 0.549050844946754, 0.723386677289053,
0.375690548876713, 0.0661544886375023, 0.00157010899258621, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.61902739128465e-05, 0.0120248586792744,
0.0759808782995633, 0.124017651192734, 0.0661544886375021, 0.00872963127077592,
3.4496711175134e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.45007275290909e-05,
0.00157213610851391, 0.00389639710499397, 0.00157010899258619,
3.44967111751334e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 5.05673652316797e-10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(1L, 324L))), a_constraint = list(
0, 0)))
MRA <- mra_wendland_2d(locs, M = 2, n_coarse_grid = 4)
class(MRA) <- NULL
expect_error(make_constraint(MRA), 'MRA must be of class "mra_wendland_2d" which is the output of mra_wendland_1d()')
class(MRA) <- "aaa"
expect_error(make_constraint(MRA), 'MRA must be of class "mra_wendland_2d" which is the output of mra_wendland_1d()')
MRA <- mra_wendland_2d(locs, M = 2, n_coarse_grid = 4)
expect_error(make_constraint(MRA, constraint = "predicted"), 'constraint = \"predicted\" is not currently supported -- developer note: add W_pred to function call to enable this in future results')
expect_error(make_constraint(MRA, constraint = "adsfs"), 'constraint must be either \"unconstrained\", \"overall\", \"resolution\", or \"predicted\"')
expect_error(make_constraint(MRA, constraint = TRUE), 'constraint must be either \"unconstrained\", \"overall\", \"resolution\", or \"predicted\"')
expect_error(make_constraint(MRA, constraint = 33), 'constraint must be either \"unconstrained\", \"overall\", \"resolution\", or \"predicted\"')
expect_error(make_constraint(MRA, constraint = NA), 'constraint must be either \"unconstrained\", \"overall\", \"resolution\", or \"predicted\"')
expect_error(make_constraint(MRA, joint = "TRUE"), 'joint must be either TRUE or FALSE.')
expect_error(make_constraint(MRA, joint = 35), 'joint must be either TRUE or FALSE.')
expect_error(make_constraint(MRA, joint = NA), 'joint must be either TRUE or FALSE.')
})
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