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# Copyright 2018 Google LLC. All Rights Reserved.
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# This library 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
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA
library(BoomSpikeSlab)
library(testthat)
seed <- 8675309
set.seed(seed)
cat("test-nestedregression\n")
SimulateNestedRegressionData <- function() {
beta.hyperprior.mean <- c(8, 6, 7, 5)
xdim <- length(beta.hyperprior.mean)
beta.hyperprior.variance <- rWishart(2 * xdim, diag(rep(1, xdim)),
inverse = TRUE)
number.of.groups <- 27
nobs.per.group = 23
beta <- rmvn(number.of.groups,
beta.hyperprior.mean,
beta.hyperprior.variance)
residual.sd <- 2.4
X <- cbind(1, matrix(rnorm(number.of.groups * (xdim - 1) * nobs.per.group),
ncol = xdim - 1))
group.id <- rep(1:number.of.groups, len = nrow(X))
y.hat <- numeric(nrow(X))
for (i in 1:nrow(X)) {
y.hat[i] = sum(X[i, ] * beta[group.id[i], ])
}
y <- rnorm(length(y.hat), y.hat, residual.sd)
suf <- BoomSpikeSlab:::.RegressionSufList(X, y, group.id)
return(list(beta.hyperprior.mean = beta.hyperprior.mean,
beta.hyperprior.variance = beta.hyperprior.variance,
beta = beta,
residual.sd = residual.sd,
X = X,
y = y,
group.id = group.id,
suf = suf))
}
test_that("NestedRegression works", {
## Check that things work with default priors.
d <- SimulateNestedRegressionData()
xdim <- length(d$beta.hyperprior.mean)
model <- NestedRegression(
suf = d$suf,
niter = 5000,
seed = seed)
expect_true(CheckMcmcMatrix(model$prior.mean, truth = d$beta.hyperprior.mean),
info = McmcMatrixReport(model$prior.mean, truth = d$beta.hyperprior.mean))
CheckMcmcVector(model$residual.sd, truth = d$residual.sd)
xdim <- length(d$beta.hyperprior.mean)
number.of.groups <- nrow(d$beta)
for (v in 1:xdim) {
expect_true(
CheckMcmcMatrix(model$prior.variance[, v, ],
truth = d$beta.hyperprior.variance[v, ]),
info = McmcMatrixReport(model$prior.variance[, v, ],
truth = d$beta.hyperprior.variance[v, ]))
}
for (g in 1:number.of.groups) {
expect_true(
CheckMcmcMatrix(model$coefficients[, g, ], truth = d$beta[g, ]),
info = McmcMatrixReport(model$coefficients[, g, ], truth = d$beta[g, ]))
}
expect_true(is.list(model$priors))
expect_equal(length(model$priors), 4)
expect_equal(names(model$priors), c("coefficient.prior",
"coefficient.mean.hyperprior", "coefficient.variance.hyperprior",
"residual.precision.prior"))
})
test_that("NestedRegression works with a fixed prior", {
d <- SimulateNestedRegressionData()
model <- NestedRegression(
suf = d$suf,
coefficient.prior = MvnPrior(mean = c(1, 2, 3, 4),
variance = diag(c(16, 4, 9, 1))),
coefficient.mean.hyperprior = FALSE,
coefficient.variance.hyperprior = FALSE,
niter = 100,
seed = seed)
expect_true(all(model$prior.mean[, 1] == 1))
expect_true(all(model$prior.mean[, 2] == 2))
expect_true(all(model$prior.mean[, 3] == 3))
expect_true(all(model$prior.mean[, 4] == 4))
expect_true(all(model$prior.variance[, 1, 1] == 16))
expect_true(all(model$prior.variance[, 2, 2] == 4))
expect_true(all(model$prior.variance[, 3, 3] == 9))
expect_true(all(model$prior.variance[, 4, 4] == 1))
})
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