# Copyright 2016-2019 Venelin Mitov
#
# This file is part of PCMBase.
#
# PCMBase is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# PCMBase is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with PCMBase. If not, see <http://www.gnu.org/licenses/>.
library(testthat)
context("PCMLik, DOU")
library(PCMBase)
if(PCMBaseIsADevRelease()) {
library(mvtnorm)
list2env(PCMBaseTestObjects, globalenv())
test_that("Calling PCMGenerateParameterizations()", {
expect_silent(tableParametrizationsDOU <- PCMTableParameterizations(structure(0.0, class="DOU")))
expect_silent(
PCMGenerateParameterizations(
model = structure(0.0, class="DOU"),
# note that I am not using data.table but data.frame syntax for subsetting
# tableParameterizationsOU. This to avoid a problem with devtools::test
# see https://github.com/r-lib/devtools/issues/192
# Another work-around would be to add data.table to Depends:, but I don't
# want this now.
tableParameterizations = tableParametrizationsDOU[
sapply(X0, function(type)
identical(type, c("VectorParameter", "_Global")) ) &
sapply(H1, function(type)
identical(type, c("MatrixParameter", "_Schur", "_WithNonNegativeDiagonal", "_Transformable"))) &
sapply(H2, function(type)
identical(type, c("MatrixParameter", "_Schur", "_WithNonNegativeDiagonal", "_Transformable"))) &
sapply(Theta, function(type)
identical(type, "VectorParameter") ), ])
)
})
set.seed(1, kind = "Mersenne-Twister", normal.kind = "Inversion")
test_that("Equal likelihood with dmvnorm on a random model, single regime (a)", {
expect_silent(model.a.123.DOU <- PCM("DOU__Global_X0__Schur_WithNonNegativeDiagonal_Transformable_H1__Schur_WithNonNegativeDiagonal_Transformable_H2__Theta__UpperTriangularWithDiagonal_WithNonNegativeDiagonal_Sigma_x__UpperTriangularWithDiagonal_WithNonNegativeDiagonal_Global_Sigmae_x", k = 3, regimes = "a"))
expect_silent(PCMParamLoadOrStore(model.a.123.DOU,
PCMParamRandomVecParams(model.a.123.DOU),
offset = 0, k = 3, load = TRUE))
#expect_silent(traits.a.123.DOU <- PCMSim(tree.a, model.a.123.DOU, model.a.123.DOU$X0) )
expect_true(is.finite(PCMLik(traits.a.123, tree.a, model.a.123.DOU)))
expect_true(abs(PCMLik(traits.a.123, tree.a, model.a.123.DOU) -
PCMLikDmvNorm(traits.a.123, tree.a, model.a.123.DOU)) < 5)
})
test_that("Equal likelihood with dmvnorm on a random model, multiple regimes (ab)", {
expect_silent(model.ab.123.DOU <- PCM("DOU__Global_X0__Schur_WithNonNegativeDiagonal_Transformable_H1__Schur_WithNonNegativeDiagonal_Transformable_H2__Theta__UpperTriangularWithDiagonal_WithNonNegativeDiagonal_Sigma_x__UpperTriangularWithDiagonal_WithNonNegativeDiagonal_Global_Sigmae_x", k = 3, regimes = c("a", "b")))
expect_silent(PCMParamLoadOrStore(model.ab.123.DOU,
PCMParamRandomVecParams(model.ab.123.DOU),
offset = 0, k = 3, load = TRUE))
#expect_silent(traits.ab.123.DOU <- PCMSim(tree.ab, model.ab.123.DOU, model.ab.123.DOU$X0) )
expect_true(is.finite(PCMLik(traits.ab.123, tree.ab, model.ab.123.DOU)))
expect_true(abs(PCMLik(traits.ab.123, tree.ab, model.ab.123.DOU) -
PCMLikDmvNorm(traits.ab.123, tree.ab, model.ab.123.DOU)) < 5)
})
}
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