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# 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/>.
# This file was contributed by Krzysztof Bartoszek
library(testthat)
context("PCMLik, BM_drift")
library(PCMBase)
if(PCMBaseIsADevRelease()) {
library(mvtnorm)
list2env(PCMBaseTestObjects, globalenv())
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.BM_drift <- PCM("BM_drift", k = 3, regimes = "a"))
expect_silent(PCMParamLoadOrStore(model.a.123.BM_drift,
PCMParamRandomVecParams(model.a.123.BM_drift),
offset = 0, k = 3, load = TRUE))
expect_equivalent(
PCMLik(traits.a.123, tree.a, model.a.123.BM_drift),
dmvnorm(as.vector(traits.a.123[, 1:PCMTreeNumTips(tree.a)]),
as.vector(PCMMean(tree.a, model.a.123.BM_drift, model.a.123.BM_drift$X0)),
PCMVar(tree.a, model.a.123.BM_drift), log = TRUE))
})
test_that("Equal likelihood with with BM for 0 drift, single regime (a)", {
expect_silent(model.a.123.BM_drift <- PCM("BM_drift", k = 3, regimes = "a"))
expect_silent(model.a.123.BM <- PCM("BM", k = 3, regimes = "a"))
expect_silent(PCMParamLoadOrStore(model.a.123.BM,
PCMParamRandomVecParams(model.a.123.BM),
offset = 0, k = 3, load = TRUE))
expect_silent(params_for_BM_drift<-NA)
expect_silent(PCMParamLoadOrStore(model.a.123.BM,params_for_BM_drift, offset = 0, k = 3, load = FALSE))
expect_silent(params_for_BM_drift<-c(params_for_BM_drift[1:3],0.0,0.0,0.0,params_for_BM_drift[4:length(params_for_BM_drift)]))
expect_silent(PCMParamLoadOrStore(model.a.123.BM_drift, params_for_BM_drift, offset = 0, k = 3, load = TRUE))
expect_equivalent(
PCMLik(traits.a.123, tree.a, model.a.123.BM_drift),
PCMLik(traits.a.123, tree.a, model.a.123.BM))
})
test_that("Equal likelihood with dmvnorm on a random model, with single regime (a) and SE >0", {
expect_silent(model.a.123.BM_drift <- PCM("BM_drift", k = 3, regimes = "a"))
expect_silent(PCMParamLoadOrStore(model.a.123.BM_drift,
PCMParamRandomVecParams(model.a.123.BM_drift),
offset = 0, k = 3, load = TRUE))
expect_equivalent(
PCMLik(traits.a.123, tree.a, model.a.123.BM_drift, SE = abs(0.01*traits.a.123[, seq_len(PCMTreeNumTips(tree.a))])),
PCMLikDmvNorm(traits.a.123, tree.a, model.a.123.BM_drift, SE = abs(0.01*traits.a.123[, seq_len(PCMTreeNumTips(tree.a))])))
expect_equivalent(
PCMLik(traits.a.123, tree.a, model.a.123.BM_drift, SE = abs(0.01*traits.a.123[, seq_len(PCMTreeNumTips(tree.a))])),
{
dmvnorm(
as.vector(traits.a.123[, 1:PCMTreeNumTips(tree.a)]),
as.vector(PCMMean(tree.a, model.a.123.BM_drift, model.a.123.BM_drift$X0)),
PCMVar(tree.a, model.a.123.BM_drift) + diag(abs(0.01*as.vector(traits.a.123[, 1:PCMTreeNumTips(tree.a)]))^2),
log = TRUE
)
}
)
})
test_that("Equal likelihood with BM for 0 drift, with single regime (a) and SE >0", {
expect_silent(model.a.123.BM_drift <- PCM("BM_drift", k = 3, regimes = "a"))
expect_silent(model.a.123.BM <- PCM("BM", k = 3, regimes = "a"))
expect_silent(PCMParamLoadOrStore(model.a.123.BM_drift,
PCMParamRandomVecParams(model.a.123.BM_drift),
offset = 0, k = 3, load = TRUE))
expect_silent(params_for_BM_drift<-NA)
expect_silent(PCMParamLoadOrStore(model.a.123.BM_drift,params_for_BM_drift, offset = 0, k = 3, load = FALSE))
expect_silent(params_for_BM_drift<-c(params_for_BM_drift[1:3],0.0,0.0,0.0,params_for_BM_drift[4:length(params_for_BM_drift)]))
expect_silent(PCMParamLoadOrStore(model.a.123.BM_drift, params_for_BM_drift, offset = 0, k = 3, load = TRUE))
expect_equivalent(
PCMLik(traits.a.123, tree.a, model.a.123.BM_drift, SE = abs(0.01*traits.a.123[, seq_len(PCMTreeNumTips(tree.a))])),
PCMLik(traits.a.123, tree.a, model.a.123.BM_drift, SE = abs(0.01*traits.a.123[, seq_len(PCMTreeNumTips(tree.a))])))
})
test_that("Equal likelihood with dmvnorm on a random model, multiple regimes (ab)", {
expect_silent(model.ab.123.BM_drift <- PCM("BM_drift", k = 3, regimes = c("a", "b")))
expect_silent(PCMParamLoadOrStore(model.ab.123.BM_drift,
PCMParamRandomVecParams(model.ab.123.BM_drift),
offset = 0, k = 3, load = TRUE))
expect_equivalent(
PCMLik(traits.ab.123, tree.ab, model.ab.123.BM_drift),
dmvnorm(as.vector(traits.ab.123[, 1:PCMTreeNumTips(tree.ab)]),
as.vector(PCMMean(tree.ab, model.ab.123.BM_drift, model.ab.123.BM_drift$X0)),
PCMVar(tree.ab, model.ab.123.BM_drift), log = TRUE))
})
test_that("Equal likelihood with BM for 0 drift, multiple regimes (ab)", {
expect_silent(model.ab.123.BM_drift <- PCM("BM_drift", k = 3, regimes = c("a", "b")))
expect_silent(model.ab.123.BM <- PCM("BM", k = 3, regimes = c("a", "b")))
expect_silent(PCMParamLoadOrStore(model.ab.123.BM,
PCMParamRandomVecParams(model.ab.123.BM),
offset = 0, k = 3, load = TRUE))
expect_silent(params_for_BM_drift<-NA)
expect_silent(PCMParamLoadOrStore(model.ab.123.BM,params_for_BM_drift, offset = 0, k = 3, load = FALSE))
expect_silent(params_for_BM_drift<-c(params_for_BM_drift[1:3],0.0,0.0,0.0,0.0,0.0,0.0,params_for_BM_drift[4:length(params_for_BM_drift)]))
expect_silent(PCMParamLoadOrStore(model.ab.123.BM_drift, params_for_BM_drift, offset = 0, k = 3, load = TRUE))
expect_equivalent(
PCMLik(traits.ab.123, tree.ab, model.ab.123.BM_drift),
PCMLik(traits.ab.123, tree.ab, model.ab.123.BM))
})
}
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