tests/testthat/test_simulateGSMAR.R

In uGMAR: Estimate Univariate Gaussian and Student's t Mixture Autoregressive Models

library(uGMAR)
context("simulate GSMAR")

params12 <- c(0.8, 0.5, 0.5, 0.3, 0.7, 0.1, 0.6, 10, 12) # StMAR
params23 <- c(1, 0.1, 0.1, 0.1, 2, -0.2, -0.2, 0.2, 3, 0.3, 0.3, 0.3, 0.6, 0.3)
params13gs <- c(0, 0.9, 1, -1, 0.5, 0.8, 1, -0.5, 0.5, 0.3, 0.4, 5, 7) # M1=1, M2=2
stmar12 <- GSMAR(p=1, M=2, params=params12, model="StMAR", parametrization="mean")
gmar23 <- GSMAR(p=2, M=3, params=params23, model="GMAR")
gstmar13 <- GSMAR(p=1, M=c(1, 2), params=params13gs, model="G-StMAR")
set.seed(1); data12 <- simulate(stmar12, nsim=3)
set.seed(2); data23 <- simulate(gmar23, nsim=1)
set.seed(3); data23init <- simulate(gmar23, nsim=2, init_values=c(1, 1.2))
set.seed(1); data13gs <- simulate(gstmar13, nsim=3)


test_that("simulate simulates correctly from non-restricted process", {
  expect_equal(data12$sample[3], 1.859932, tolerance=1e-6)
  expect_equal(data12$componen, c(2, 1, 1))
  expect_equal(data23$sample, 1.017869, tolerance=1e-6)
  expect_equal(data23$component, 2)
  expect_equal(data23$mixing_weights[,2], 0.2067762, tolerance=1e-6)
  expect_equal(data23init$sample[2], 1.351309, tolerance=1e-6)
  expect_equal(data13gs$sample[1], -0.3833485, tolerance=1e-6)
  expect_equal(data13gs$mixing_weights[3, 3], 0.00969763, tolerance=1e-6)
})

params12r <- c(1, 2, -0.9, 1, 2, 0.7)
params23r <- c(-0.1, -0.2, -0.3, -0.2, 0.5, 0.1, 0.2, 0.3, 0.5, 0.3, 3, 14, 50) # StMAR
params22gsr <- c(1, 1.2, 0.3, 0.3, 1, 1.2, 0.5, 6)
gmar12r <- GSMAR(p=1, M=2, params=params12r, model="GMAR", restricted=TRUE)
stmar23r <- GSMAR(p=2, M=3, params=params23r, model="StMAR", restricted=TRUE)
gstmar22r <- GSMAR(p=2, M=c(1, 1), params=params22gsr, model="G-StMAR", restricted=TRUE)
set.seed(1); data12r <- simulate(gmar12r, nsim=1)
set.seed(2); data12rinit <- simulate(gmar12r, nsim=2, init_values=1)
set.seed(3); data23r <- simulate(stmar23r, nsim=2)
set.seed(1); data22gsr <- simulate(gstmar22r, nsim=2)


test_that("simulate simulates correctly from restricted process", {
  expect_equal(data12r$sample, 1.018146, tolerance=1e-6)
  expect_equal(data12r$component, 2)
  expect_equal(data12r$mixing_weights[,1],  0.7716839, tolerance=1e-6)
  expect_equal(data12rinit$sample[1], 0.6312408, tolerance=1e-6)
  expect_equal(data23r$sample[2], 0.004890296, tolerance=1e-6)
  expect_equal(data23r$component[2], 1)
  expect_equal(data22gsr$sample[2], 2.160116, tolerance=1e-6)
  expect_equal(data22gsr$component[1], 1)
})

R1 <- matrix(c(1, 0, 0, 0, 0, 1), ncol=2)
R3 <- matrix(c(0.5, 0.5), ncol=1)
R4 <- diag(1, ncol=2, nrow=2)
params22c <- c(1, 0.1, -0.1, 1, 2, 0.2, 2, 0.8, 11, 12) # R4, R3, StMAR
params32cr <- c(1, 2, 0.3, -0.3, 1, 2, 0.6) # R1
params22gsrc <- c(0, 0, -0.99, 1, 1.2, 0.3, 10) # M1=1, M2=1, R3
stmar22c <- GSMAR(p=2, M=2, params=params22c, model="StMAR", constraints=list(R4, R3))
gmar32cr <- GSMAR(p=3, M=2, params=params32cr, model="GMAR", restricted=TRUE, constraints=R1)
gstmar22cr <- GSMAR(p=2, M=c(1, 1), params=params22gsrc, model="G-StMAR", restricted=TRUE, constraints=R3)
set.seed(1); data22c <- simulate(stmar22c, nsim=1)
set.seed(2); data32cr <- simulate(gmar32cr, nsim=2)
set.seed(3); data22gsrc <- simulate(gstmar22cr, nsim=2)


test_that("simulate simulates correctly from constrained process", {
  expect_equal(data22c$sample, 1.240905, tolerance=1e-6)
  expect_equal(data22c$mixing_weights[,1], 0.9172016, tolerance=1e-6)
  expect_equal(data32cr$sample[2], 0.7406432, tolerance=1e-6)
  expect_equal(data32cr$component[2], 1)
  expect_equal(data22gsrc$sample[2], -0.4407819, tolerance=1e-6)
  expect_equal(data22gsrc$component[1], 2)
  expect_equal(data22gsrc$mixing_weights[,1], c(0.3099519, 0.3092720), tolerance=1e-6)
})