tests/testthat/test-CJS.R
In nimble-dev/nimbleEcology: Distributions for Ecological Models in 'nimble'
# Test the Cormack-Jolly-Seber distribution nimbleFunction.
# -----------------------------------------------------------------------------
# 0. Load
# Set the context for testthat
context("Testing dCJS-related functions.")
# -----------------------------------------------------------------------------
# 1. Test dCJS_ss
# dCJS_ss is used in the case that probSurvive and probCapture are both
# scalar values.
test_that("dCJS_ss works",
{
# Uncompiled calculation
x <- c(1, 0, 1, 0, 0)
probSurvive <- 0.6
probCapture <- 0.4
probX <- dCJS_ss(x, probSurvive, probCapture, len = 5)
# Manually calculate the correct answer
correctProbX <- probSurvive * (1 - probCapture) *
probSurvive * (probCapture) *
(probSurvive^2 * (1 - probCapture)^2 +
probSurvive * (1 - probCapture) * (1 - probSurvive) +
(1 - probSurvive))
expect_equal(probX, correctProbX)
# Uncompiled log probability
lProbX <- dCJS_ss(x, probSurvive, probCapture, log = TRUE, len = 5)
lCorrectProbX <- log(correctProbX)
expect_equal(lProbX, lCorrectProbX)
# Compilation and compiled calculations
CdCJS_ss <- compileNimble(dCJS_ss)
CprobX <- CdCJS_ss(x, probSurvive, probCapture, len = 5)
expect_equal(CprobX, probX)
ClProbX <- CdCJS_ss(x, probSurvive, probCapture, log = TRUE, len = 5)
expect_equal(ClProbX, lProbX)
# Use in Nimble model
nc <- nimbleCode({
x[1:5] ~ dCJS_ss(probSurvive, probCapture, len = 5)
probSurvive ~ dunif(0,1)
probCapture ~ dunif(0,1)
})
m <- nimbleModel(nc, data = list(x = x),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
m$calculate()
MlProbX <- m$getLogProb("x")
expect_equal(MlProbX, lProbX)
# Compiled model
cm <- compileNimble(m)
cm$calculate()
CMlProbX <- cm$getLogProb("x")
expect_equal(CMlProbX, lProbX)
# Test imputing value for all NAs
xNA <- c(NA, NA, NA, NA, NA)
mNA <- nimbleModel(nc, data = list(x = xNA),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
mNAConf <- configureMCMC(mNA)
mNAConf$addMonitors('x')
mNA_MCMC <- buildMCMC(mNAConf)
cmNA <- compileNimble(mNA, mNA_MCMC)
set.seed(0)
cmNA$mNA_MCMC$run(10)
# Did the imputed values come back?
expect_true(all(!is.na(as.matrix(cmNA$mNA_MCMC$mvSamples)[,"x[2]"])))
# Test simulation code
set.seed(1)
nSim <- 10
xSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
xSim[i,] <- rCJS_ss(1, probSurvive, probCapture, len = length(x))
set.seed(1)
CrCJS_ss <- compileNimble(rCJS_ss)
CxSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
CxSim[i,] <- CrCJS_ss(1, probSurvive, probCapture, len = length(x))
expect_identical(xSim, CxSim)
simNodes <- m$getDependencies(c('probSurvive', 'probCapture'), self = FALSE)
mxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for(i in 1:nSim) {
m$simulate(simNodes, includeData = TRUE)
mxSim[i,] <- m$x
}
expect_identical(mxSim, xSim)
CmxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for(i in 1:nSim) {
cm$simulate(simNodes, includeData = TRUE)
CmxSim[i,] <- cm$x
}
expect_identical(CmxSim, mxSim)
})
# -----------------------------------------------------------------------------
# 2. Test dCJS_sv
# dCJS_sv is used in the case where survival probability is a scalar and
# capture probability is a vector.
test_that("dCJS_sv works",
{
# Uncompiled calculation
x <- c(1, 0, 1, 0, 0)
probSurvive <- 0.6
probCapture <- c(1, 0.25, 0.6, 0.4, 0.8)
probX <- dCJS_sv(x, probSurvive, probCapture)
# Manually calculate the correct answer
correctProbX <- probSurvive * (1 - probCapture[2]) *
probSurvive * (probCapture[3]) *
(probSurvive^2 * (1 - probCapture[4]) * (1 - probCapture[5]) +
probSurvive * (1 - probCapture[4]) * (1 - probSurvive) +
(1 - probSurvive))
expect_equal(probX, correctProbX)
# Uncompiled log probability
lProbX <- dCJS_sv(x, probSurvive, probCapture, log = TRUE)
lCorrectProbX <- log(correctProbX)
expect_equal(lProbX, lCorrectProbX)
# Compilation and compiled calculations
CdCJS_sv <- compileNimble(dCJS_sv)
CprobX <- CdCJS_sv(x, probSurvive, probCapture)
expect_equal(CprobX, probX)
ClProbX <- CdCJS_sv(x, probSurvive, probCapture, log = TRUE)
expect_equal(ClProbX, lProbX)
# Use in Nimble model
nc <- nimbleCode({
x[1:5] ~ dCJS_sv(probSurvive, probCapture[1:5], len = 5)
probSurvive ~ dunif(0,1)
for (i in 1:5) {
probCapture[i] ~ dunif(0,1)
}
})
m <- nimbleModel(nc, data = list(x = x),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
m$calculate()
MlProbX <- m$getLogProb("x")
expect_equal(MlProbX, lProbX)
# Compiled model
cm <- compileNimble(m)
cm$calculate()
CMlProbX <- cm$getLogProb("x")
expect_equal(CMlProbX, lProbX)
# Test imputing value for all NAs
xNA <- c(NA, NA, NA, NA, NA)
mNA <- nimbleModel(nc, data = list(x = xNA),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
mNAConf <- configureMCMC(mNA)
mNAConf$addMonitors('x')
mNA_MCMC <- buildMCMC(mNAConf)
cmNA <- compileNimble(mNA, mNA_MCMC)
set.seed(10)
cmNA$mNA_MCMC$run(5)
# Did the imputed values come back?
expect_true(all(!is.na(as.matrix(cmNA$mNA_MCMC$mvSamples)[,"x[1]"])))
# Test simulation code
set.seed(1)
nSim <- 10
xSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
xSim[i,] <- rCJS_sv(1, probSurvive, probCapture, len = length(x))
set.seed(1)
CrCJS_sv <- compileNimble(rCJS_sv)
CxSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
CxSim[i,] <- CrCJS_sv(1, probSurvive, probCapture, len = length(x))
expect_identical(xSim, CxSim)
simNodes <- m$getDependencies(c('probSurvive', 'probCapture'), self = FALSE)
mxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for (i in 1:nSim) {
m$simulate(simNodes, includeData = TRUE)
mxSim[i,] <- m$x
}
expect_identical(mxSim, xSim)
CmxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for (i in 1:nSim) {
cm$simulate(simNodes, includeData = TRUE)
CmxSim[i,] <- cm$x
}
expect_identical(CmxSim, mxSim)
})
# -----------------------------------------------------------------------------
# 3. Test dCJS_vs
# dCJS_vs is used in the case where survival probability is a vector and
# capture probability is a scalar.
test_that("dCJS_vs works",
{
# Uncompiled calculation
x <- c(1, 0, 1, 0, 0)
probSurvive <- c(0.8, 0.45, 0.4, 0.7)
probCapture <- 0.6
probX <- dCJS_vs(x, probSurvive, probCapture)
# Manually calculate the correct answer
correctProbX <- probSurvive[1] * (1 - probCapture) *
probSurvive[2] * probCapture *
(probSurvive[3] * probSurvive[4] * (1 - probCapture)^2 +
probSurvive[3] * (1 - probCapture) * (1 - probSurvive[4]) +
(1 - probSurvive[3]))
expect_equal(probX, correctProbX)
# Uncompiled log probability
lProbX <- dCJS_vs(x, probSurvive, probCapture, log = TRUE)
lCorrectProbX <- log(correctProbX)
expect_equal(lProbX, lCorrectProbX)
# Compilation and compiled calculations
CdCJS_vs <- compileNimble(dCJS_vs)
CprobX <- CdCJS_vs(x, probSurvive, probCapture)
expect_equal(CprobX, probX)
ClProbX <- CdCJS_vs(x, probSurvive, probCapture, log = TRUE)
expect_equal(ClProbX, lProbX)
# Use in Nimble model
nc <- nimbleCode({
x[1:5] ~ dCJS_vs(probSurvive[1:4], probCapture, len = 5)
probCapture ~ dunif(0,1)
for (i in 1:4) {
probSurvive[i] ~ dunif(0,1)
}
})
m <- nimbleModel(nc, data = list(x = x),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
m$calculate()
MlProbX <- m$getLogProb("x")
expect_equal(MlProbX, lProbX)
# Compiled model
cm <- compileNimble(m)
cm$calculate()
CMlProbX <- cm$getLogProb("x")
expect_equal(CMlProbX, lProbX)
# Test imputing value for all NAs
xNA <- c(NA, NA, NA, NA, NA)
mNA <- nimbleModel(nc, data = list(x = xNA),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
mNAConf <- configureMCMC(mNA)
mNAConf$addMonitors('x')
mNA_MCMC <- buildMCMC(mNAConf)
cmNA <- compileNimble(mNA, mNA_MCMC)
set.seed(5)
cmNA$mNA_MCMC$run(10)
# Did the imputed values come back?
expect_true(all(!is.na(as.matrix(cmNA$mNA_MCMC$mvSamples)[,"x[1]"])))
# Test simulation code
set.seed(1)
nSim <- 10
xSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
xSim[i,] <- rCJS_vs(1, probSurvive, probCapture, len = length(x))
set.seed(1)
CrCJS_vs <- compileNimble(rCJS_vs)
CxSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
CxSim[i,] <- CrCJS_vs(1, probSurvive, probCapture, len = length(x))
expect_identical(xSim, CxSim)
simNodes <- m$getDependencies(c('probSurvive', 'probCapture'), self = FALSE)
mxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for(i in 1:nSim) {
m$simulate(simNodes, includeData = TRUE)
mxSim[i,] <- m$x
}
expect_identical(mxSim, xSim)
CmxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for(i in 1:nSim) {
cm$simulate(simNodes, includeData = TRUE)
CmxSim[i,] <- cm$x
}
expect_identical(CmxSim, mxSim)
})
# -----------------------------------------------------------------------------
# 4. Test dCJS_vv
test_that("dCJS_vv works",
{
## Uncompiled calculation
x <- c(1, 0,1,0,0)
probSurvive <- c(0.6, 0.5, 0.4, 0.55)
probCapture <- c(1, 0.45, 0.5, 0.55, 0.6)
len <- 5
probX <- dCJS_vv(x, probSurvive, probCapture, len)
correctProbX <-
probSurvive[1] * (1 - probCapture[2]) *
probSurvive[2] * (probCapture[3]) *
((probSurvive[3] * (1 - probCapture[4]) *
probSurvive[4] * (1 - probCapture[5])) +
(probSurvive[3] * (1 - probCapture[4]) *
(1 - probSurvive[4])) +
(1 - probSurvive[3]))
expect_equal(probX, correctProbX)
## log Prob
lProbX <- dCJS_vv(x, probSurvive, probCapture, log = TRUE)
lCorrectProbX <- log(correctProbX)
expect_equal(lProbX, lCorrectProbX)
## Compiles
CdCJS_vv <- compileNimble(dCJS_vv)
CprobX <- CdCJS_vv(x, probSurvive, probCapture)
expect_equal(CprobX, probX)
ClProbX <- CdCJS_vv(x, probSurvive, probCapture, len = 5, log = TRUE)
expect_equal(ClProbX, lProbX)
## Use in model
nc <- nimbleCode({
x[1:5] ~ dCJS_vv(probSurvive[1:4], probCapture[1:5], len = 5)
for (i in 1:4) {
probSurvive[i] ~ dunif(0,1)
probCapture[i] ~ dunif(0,1)
}
})
m <- nimbleModel(nc, data = list(x = x),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
m$calculate()
MlProbX <- m$getLogProb("x")
expect_equal(MlProbX, lProbX)
cm <- compileNimble(m)
cm$calculate()
CMlProbX <- cm$getLogProb("x")
expect_equal(CMlProbX, lProbX)
# Test imputing value for all NAs
xNA <- c(NA, NA, NA, NA, NA)
mNA <- nimbleModel(nc, data = list(x = xNA),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
mNAConf <- configureMCMC(mNA)
mNAConf$addMonitors('x')
mNA_MCMC <- buildMCMC(mNAConf)
cmNA <- compileNimble(mNA, mNA_MCMC)
set.seed(5)
cmNA$mNA_MCMC$run(10)
# Did the imputed values come back?
expect_true(all(!is.na(as.matrix(cmNA$mNA_MCMC$mvSamples)[,"x[1]"])))
# Test simulation code
set.seed(1)
nSim <- 10
xSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
xSim[i,] <- rCJS_vv(1, probSurvive, probCapture, len = length(x))
set.seed(1)
CrCJS_vv <- compileNimble(rCJS_vv)
CxSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
CxSim[i,] <- CrCJS_vv(1, probSurvive, probCapture, len = length(x))
expect_identical(xSim, CxSim)
simNodes <- m$getDependencies(c('probSurvive', 'probCapture'), self = FALSE)
mxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for(i in 1:nSim) {
m$simulate(simNodes, includeData = TRUE)
mxSim[i,] <- m$x
}
expect_identical(mxSim, xSim)
CmxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for(i in 1:nSim) {
cm$simulate(simNodes, includeData = TRUE)
CmxSim[i,] <- cm$x
}
expect_identical(CmxSim, mxSim)
})
test_that("dCJS errors", {
### Uncompiled errors
# dCJS_ss error checks
expect_error(
dCJS_ss(x = c(1,0,1,0,0), probCapture = 0.4, probSurvive = 0.5, len = 3)
)
expect_error(
dCJS_ss(x = c(0,0,1,0,0), probCapture = 0.4, probSurvive = 0.5)
)
# dCJS_sv error checks
expect_error(
dCJS_vs(x = c(1,0,1,0,0), probCapture = 0.1, probSurvive = c(0.9, 0.9, 0.4, 0.4), len = 2)
)
expect_error(
dCJS_vs(x = c(1,0,1,0,0), probCapture = 0.1, probSurvive = c(0.9, 0.9, 0.4))
)
expect_error(
dCJS_vs(x = c(0,0,1,0,0), probCapture = 0.1, probSurvive = c(0.9, 0.9, 0.4, 0.4))
)
# dCJS_vs error checks
expect_error(
dCJS_sv(x = c(1,0,1,0,0), probCapture = c(1,0.9, 0.9, 0.4, 0.4), probSurvive = 0.1, len = 6)
)
expect_error(
dCJS_sv(x = c(1,0,1,0,0), probCapture = c(1,0.9, 0.9, 0.4), probSurvive = 0.8)
)
expect_error(
dCJS_sv(x = c(0,0,1,0,0), probCapture = c(1,0.9, 0.9, 0.4, 0.4), probSurvive = 0.1)
)
# dCJS_vv error checks
expect_error(
dCJS_vv(x = c(1,0,1,0,0), probCapture = c(1,0,1,0.3,0.3), probSurvive = c(0.9, 0.9))
)
expect_error(
dCJS_vv(x = c(1,0,1,0,0), probCapture = c(1,0,1), probSurvive = c(0.9, 0.9, 0.1, 0.1))
)
expect_error(
dCJS_vv(x = c(1,0,1,0,0), probCapture = c(1,0,1,0,0),
probSurvive = c(0.9, 0.9, 0.1, 0.1), len = 2)
)
expect_error(
dCJS_vv(x = c(0,0,1,0,0), probCapture = c(1,0,1,0,0),
probSurvive = c(0.9, 0.9, 0.1, 0.1))
)
### Compiled errors
CdCJS_ss <- compileNimble(dCJS_ss)
CdCJS_sv <- compileNimble(dCJS_sv)
CdCJS_vs <- compileNimble(dCJS_vs)
CdCJS_vv <- compileNimble(dCJS_vv)
expect_error(
CdCJS_ss(x = c(1,0,1,0,0), probCapture = 0.4, probSurvive = 0.5, len = 3)
)
expect_error(
CdCJS_ss(x = c(0,0,1,0,0), probCapture = 0.4, probSurvive = 0.5)
)
# dCJS_sv error checks
expect_error(
CdCJS_vs(x = c(1,0,1,0,0), probCapture = 0.1, probSurvive = c(0.9, 0.9, 0.4, 0.4), len = 3)
)
expect_error(
CdCJS_vs(x = c(1,0,1,0,0), probCapture = 0.1, probSurvive = c(0.9, 0.9, 0.4))
)
expect_error(
CdCJS_vs(x = c(0,0,1,0,0), probCapture = 0.1, probSurvive = c(0.9, 0.9, 0.4, 0.4))
)
# dCJS_vs error checks
expect_error(
CdCJS_sv(x = c(1,0,1,0,0), probCapture = c(1,0.9, 0.9, 0.4, 0.4), probSurvive = 0.1, len = 3)
)
expect_error(
CdCJS_sv(x = c(1,0,1,0,0), probCapture = c(1,0.9, 0.9, 0.4), probSurvive = 0.8)
)
expect_error(
CdCJS_sv(x = c(0,0,1,0,0), probCapture = c(1,0.9, 0.9, 0.4, 0.4), probSurvive = 0.1)
)
# dCJS_vv error checks
expect_error(
CdCJS_vv(x = c(1,0,1,0,0), probCapture = c(1,0,1,0.3,0.3), probSurvive = c(0.9, 0.9))
)
expect_error(
CdCJS_vv(x = c(1,0,1,0,0), probCapture = c(0,1), probSurvive = c(0.9, 0.9, 0.1, 0.1))
)
expect_error(
CdCJS_vv(x = c(1,0,1,0,0), probCapture = c(1,0,1,0,0),
probSurvive = c(0.9, 0.9, 0.1, 0.1), len = 2)
)
expect_error(
CdCJS_vv(x = c(0,0,1,0,0), probCapture = c(1,0,1,0,0),
probSurvive = c(0.9, 0.9, 0.1, 0.1))
)
})
nimble-dev/nimbleEcology documentation built on Nov. 5, 2021, 3:39 a.m.
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# Test the Cormack-Jolly-Seber distribution nimbleFunction.
# -----------------------------------------------------------------------------
# 0. Load
# Set the context for testthat
context("Testing dCJS-related functions.")
# -----------------------------------------------------------------------------
# 1. Test dCJS_ss
# dCJS_ss is used in the case that probSurvive and probCapture are both
# scalar values.
test_that("dCJS_ss works",
{
# Uncompiled calculation
x <- c(1, 0, 1, 0, 0)
probSurvive <- 0.6
probCapture <- 0.4
probX <- dCJS_ss(x, probSurvive, probCapture, len = 5)
# Manually calculate the correct answer
correctProbX <- probSurvive * (1 - probCapture) *
probSurvive * (probCapture) *
(probSurvive^2 * (1 - probCapture)^2 +
probSurvive * (1 - probCapture) * (1 - probSurvive) +
(1 - probSurvive))
expect_equal(probX, correctProbX)
# Uncompiled log probability
lProbX <- dCJS_ss(x, probSurvive, probCapture, log = TRUE, len = 5)
lCorrectProbX <- log(correctProbX)
expect_equal(lProbX, lCorrectProbX)
# Compilation and compiled calculations
CdCJS_ss <- compileNimble(dCJS_ss)
CprobX <- CdCJS_ss(x, probSurvive, probCapture, len = 5)
expect_equal(CprobX, probX)
ClProbX <- CdCJS_ss(x, probSurvive, probCapture, log = TRUE, len = 5)
expect_equal(ClProbX, lProbX)
# Use in Nimble model
nc <- nimbleCode({
x[1:5] ~ dCJS_ss(probSurvive, probCapture, len = 5)
probSurvive ~ dunif(0,1)
probCapture ~ dunif(0,1)
})
m <- nimbleModel(nc, data = list(x = x),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
m$calculate()
MlProbX <- m$getLogProb("x")
expect_equal(MlProbX, lProbX)
# Compiled model
cm <- compileNimble(m)
cm$calculate()
CMlProbX <- cm$getLogProb("x")
expect_equal(CMlProbX, lProbX)
# Test imputing value for all NAs
xNA <- c(NA, NA, NA, NA, NA)
mNA <- nimbleModel(nc, data = list(x = xNA),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
mNAConf <- configureMCMC(mNA)
mNAConf$addMonitors('x')
mNA_MCMC <- buildMCMC(mNAConf)
cmNA <- compileNimble(mNA, mNA_MCMC)
set.seed(0)
cmNA$mNA_MCMC$run(10)
# Did the imputed values come back?
expect_true(all(!is.na(as.matrix(cmNA$mNA_MCMC$mvSamples)[,"x[2]"])))
# Test simulation code
set.seed(1)
nSim <- 10
xSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
xSim[i,] <- rCJS_ss(1, probSurvive, probCapture, len = length(x))
set.seed(1)
CrCJS_ss <- compileNimble(rCJS_ss)
CxSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
CxSim[i,] <- CrCJS_ss(1, probSurvive, probCapture, len = length(x))
expect_identical(xSim, CxSim)
simNodes <- m$getDependencies(c('probSurvive', 'probCapture'), self = FALSE)
mxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for(i in 1:nSim) {
m$simulate(simNodes, includeData = TRUE)
mxSim[i,] <- m$x
}
expect_identical(mxSim, xSim)
CmxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for(i in 1:nSim) {
cm$simulate(simNodes, includeData = TRUE)
CmxSim[i,] <- cm$x
}
expect_identical(CmxSim, mxSim)
})
# -----------------------------------------------------------------------------
# 2. Test dCJS_sv
# dCJS_sv is used in the case where survival probability is a scalar and
# capture probability is a vector.
test_that("dCJS_sv works",
{
# Uncompiled calculation
x <- c(1, 0, 1, 0, 0)
probSurvive <- 0.6
probCapture <- c(1, 0.25, 0.6, 0.4, 0.8)
probX <- dCJS_sv(x, probSurvive, probCapture)
# Manually calculate the correct answer
correctProbX <- probSurvive * (1 - probCapture[2]) *
probSurvive * (probCapture[3]) *
(probSurvive^2 * (1 - probCapture[4]) * (1 - probCapture[5]) +
probSurvive * (1 - probCapture[4]) * (1 - probSurvive) +
(1 - probSurvive))
expect_equal(probX, correctProbX)
# Uncompiled log probability
lProbX <- dCJS_sv(x, probSurvive, probCapture, log = TRUE)
lCorrectProbX <- log(correctProbX)
expect_equal(lProbX, lCorrectProbX)
# Compilation and compiled calculations
CdCJS_sv <- compileNimble(dCJS_sv)
CprobX <- CdCJS_sv(x, probSurvive, probCapture)
expect_equal(CprobX, probX)
ClProbX <- CdCJS_sv(x, probSurvive, probCapture, log = TRUE)
expect_equal(ClProbX, lProbX)
# Use in Nimble model
nc <- nimbleCode({
x[1:5] ~ dCJS_sv(probSurvive, probCapture[1:5], len = 5)
probSurvive ~ dunif(0,1)
for (i in 1:5) {
probCapture[i] ~ dunif(0,1)
}
})
m <- nimbleModel(nc, data = list(x = x),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
m$calculate()
MlProbX <- m$getLogProb("x")
expect_equal(MlProbX, lProbX)
# Compiled model
cm <- compileNimble(m)
cm$calculate()
CMlProbX <- cm$getLogProb("x")
expect_equal(CMlProbX, lProbX)
# Test imputing value for all NAs
xNA <- c(NA, NA, NA, NA, NA)
mNA <- nimbleModel(nc, data = list(x = xNA),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
mNAConf <- configureMCMC(mNA)
mNAConf$addMonitors('x')
mNA_MCMC <- buildMCMC(mNAConf)
cmNA <- compileNimble(mNA, mNA_MCMC)
set.seed(10)
cmNA$mNA_MCMC$run(5)
# Did the imputed values come back?
expect_true(all(!is.na(as.matrix(cmNA$mNA_MCMC$mvSamples)[,"x[1]"])))
# Test simulation code
set.seed(1)
nSim <- 10
xSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
xSim[i,] <- rCJS_sv(1, probSurvive, probCapture, len = length(x))
set.seed(1)
CrCJS_sv <- compileNimble(rCJS_sv)
CxSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
CxSim[i,] <- CrCJS_sv(1, probSurvive, probCapture, len = length(x))
expect_identical(xSim, CxSim)
simNodes <- m$getDependencies(c('probSurvive', 'probCapture'), self = FALSE)
mxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for (i in 1:nSim) {
m$simulate(simNodes, includeData = TRUE)
mxSim[i,] <- m$x
}
expect_identical(mxSim, xSim)
CmxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for (i in 1:nSim) {
cm$simulate(simNodes, includeData = TRUE)
CmxSim[i,] <- cm$x
}
expect_identical(CmxSim, mxSim)
})
# -----------------------------------------------------------------------------
# 3. Test dCJS_vs
# dCJS_vs is used in the case where survival probability is a vector and
# capture probability is a scalar.
test_that("dCJS_vs works",
{
# Uncompiled calculation
x <- c(1, 0, 1, 0, 0)
probSurvive <- c(0.8, 0.45, 0.4, 0.7)
probCapture <- 0.6
probX <- dCJS_vs(x, probSurvive, probCapture)
# Manually calculate the correct answer
correctProbX <- probSurvive[1] * (1 - probCapture) *
probSurvive[2] * probCapture *
(probSurvive[3] * probSurvive[4] * (1 - probCapture)^2 +
probSurvive[3] * (1 - probCapture) * (1 - probSurvive[4]) +
(1 - probSurvive[3]))
expect_equal(probX, correctProbX)
# Uncompiled log probability
lProbX <- dCJS_vs(x, probSurvive, probCapture, log = TRUE)
lCorrectProbX <- log(correctProbX)
expect_equal(lProbX, lCorrectProbX)
# Compilation and compiled calculations
CdCJS_vs <- compileNimble(dCJS_vs)
CprobX <- CdCJS_vs(x, probSurvive, probCapture)
expect_equal(CprobX, probX)
ClProbX <- CdCJS_vs(x, probSurvive, probCapture, log = TRUE)
expect_equal(ClProbX, lProbX)
# Use in Nimble model
nc <- nimbleCode({
x[1:5] ~ dCJS_vs(probSurvive[1:4], probCapture, len = 5)
probCapture ~ dunif(0,1)
for (i in 1:4) {
probSurvive[i] ~ dunif(0,1)
}
})
m <- nimbleModel(nc, data = list(x = x),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
m$calculate()
MlProbX <- m$getLogProb("x")
expect_equal(MlProbX, lProbX)
# Compiled model
cm <- compileNimble(m)
cm$calculate()
CMlProbX <- cm$getLogProb("x")
expect_equal(CMlProbX, lProbX)
# Test imputing value for all NAs
xNA <- c(NA, NA, NA, NA, NA)
mNA <- nimbleModel(nc, data = list(x = xNA),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
mNAConf <- configureMCMC(mNA)
mNAConf$addMonitors('x')
mNA_MCMC <- buildMCMC(mNAConf)
cmNA <- compileNimble(mNA, mNA_MCMC)
set.seed(5)
cmNA$mNA_MCMC$run(10)
# Did the imputed values come back?
expect_true(all(!is.na(as.matrix(cmNA$mNA_MCMC$mvSamples)[,"x[1]"])))
# Test simulation code
set.seed(1)
nSim <- 10
xSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
xSim[i,] <- rCJS_vs(1, probSurvive, probCapture, len = length(x))
set.seed(1)
CrCJS_vs <- compileNimble(rCJS_vs)
CxSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
CxSim[i,] <- CrCJS_vs(1, probSurvive, probCapture, len = length(x))
expect_identical(xSim, CxSim)
simNodes <- m$getDependencies(c('probSurvive', 'probCapture'), self = FALSE)
mxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for(i in 1:nSim) {
m$simulate(simNodes, includeData = TRUE)
mxSim[i,] <- m$x
}
expect_identical(mxSim, xSim)
CmxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for(i in 1:nSim) {
cm$simulate(simNodes, includeData = TRUE)
CmxSim[i,] <- cm$x
}
expect_identical(CmxSim, mxSim)
})
# -----------------------------------------------------------------------------
# 4. Test dCJS_vv
test_that("dCJS_vv works",
{
## Uncompiled calculation
x <- c(1, 0,1,0,0)
probSurvive <- c(0.6, 0.5, 0.4, 0.55)
probCapture <- c(1, 0.45, 0.5, 0.55, 0.6)
len <- 5
probX <- dCJS_vv(x, probSurvive, probCapture, len)
correctProbX <-
probSurvive[1] * (1 - probCapture[2]) *
probSurvive[2] * (probCapture[3]) *
((probSurvive[3] * (1 - probCapture[4]) *
probSurvive[4] * (1 - probCapture[5])) +
(probSurvive[3] * (1 - probCapture[4]) *
(1 - probSurvive[4])) +
(1 - probSurvive[3]))
expect_equal(probX, correctProbX)
## log Prob
lProbX <- dCJS_vv(x, probSurvive, probCapture, log = TRUE)
lCorrectProbX <- log(correctProbX)
expect_equal(lProbX, lCorrectProbX)
## Compiles
CdCJS_vv <- compileNimble(dCJS_vv)
CprobX <- CdCJS_vv(x, probSurvive, probCapture)
expect_equal(CprobX, probX)
ClProbX <- CdCJS_vv(x, probSurvive, probCapture, len = 5, log = TRUE)
expect_equal(ClProbX, lProbX)
## Use in model
nc <- nimbleCode({
x[1:5] ~ dCJS_vv(probSurvive[1:4], probCapture[1:5], len = 5)
for (i in 1:4) {
probSurvive[i] ~ dunif(0,1)
probCapture[i] ~ dunif(0,1)
}
})
m <- nimbleModel(nc, data = list(x = x),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
m$calculate()
MlProbX <- m$getLogProb("x")
expect_equal(MlProbX, lProbX)
cm <- compileNimble(m)
cm$calculate()
CMlProbX <- cm$getLogProb("x")
expect_equal(CMlProbX, lProbX)
# Test imputing value for all NAs
xNA <- c(NA, NA, NA, NA, NA)
mNA <- nimbleModel(nc, data = list(x = xNA),
inits = list(probSurvive = probSurvive,
probCapture = probCapture))
mNAConf <- configureMCMC(mNA)
mNAConf$addMonitors('x')
mNA_MCMC <- buildMCMC(mNAConf)
cmNA <- compileNimble(mNA, mNA_MCMC)
set.seed(5)
cmNA$mNA_MCMC$run(10)
# Did the imputed values come back?
expect_true(all(!is.na(as.matrix(cmNA$mNA_MCMC$mvSamples)[,"x[1]"])))
# Test simulation code
set.seed(1)
nSim <- 10
xSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
xSim[i,] <- rCJS_vv(1, probSurvive, probCapture, len = length(x))
set.seed(1)
CrCJS_vv <- compileNimble(rCJS_vv)
CxSim <- array(NA, dim = c(nSim, length(x)))
for(i in 1:nSim)
CxSim[i,] <- CrCJS_vv(1, probSurvive, probCapture, len = length(x))
expect_identical(xSim, CxSim)
simNodes <- m$getDependencies(c('probSurvive', 'probCapture'), self = FALSE)
mxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for(i in 1:nSim) {
m$simulate(simNodes, includeData = TRUE)
mxSim[i,] <- m$x
}
expect_identical(mxSim, xSim)
CmxSim <- array(NA, dim = c(nSim, length(x)))
set.seed(1)
for(i in 1:nSim) {
cm$simulate(simNodes, includeData = TRUE)
CmxSim[i,] <- cm$x
}
expect_identical(CmxSim, mxSim)
})
test_that("dCJS errors", {
### Uncompiled errors
# dCJS_ss error checks
expect_error(
dCJS_ss(x = c(1,0,1,0,0), probCapture = 0.4, probSurvive = 0.5, len = 3)
)
expect_error(
dCJS_ss(x = c(0,0,1,0,0), probCapture = 0.4, probSurvive = 0.5)
)
# dCJS_sv error checks
expect_error(
dCJS_vs(x = c(1,0,1,0,0), probCapture = 0.1, probSurvive = c(0.9, 0.9, 0.4, 0.4), len = 2)
)
expect_error(
dCJS_vs(x = c(1,0,1,0,0), probCapture = 0.1, probSurvive = c(0.9, 0.9, 0.4))
)
expect_error(
dCJS_vs(x = c(0,0,1,0,0), probCapture = 0.1, probSurvive = c(0.9, 0.9, 0.4, 0.4))
)
# dCJS_vs error checks
expect_error(
dCJS_sv(x = c(1,0,1,0,0), probCapture = c(1,0.9, 0.9, 0.4, 0.4), probSurvive = 0.1, len = 6)
)
expect_error(
dCJS_sv(x = c(1,0,1,0,0), probCapture = c(1,0.9, 0.9, 0.4), probSurvive = 0.8)
)
expect_error(
dCJS_sv(x = c(0,0,1,0,0), probCapture = c(1,0.9, 0.9, 0.4, 0.4), probSurvive = 0.1)
)
# dCJS_vv error checks
expect_error(
dCJS_vv(x = c(1,0,1,0,0), probCapture = c(1,0,1,0.3,0.3), probSurvive = c(0.9, 0.9))
)
expect_error(
dCJS_vv(x = c(1,0,1,0,0), probCapture = c(1,0,1), probSurvive = c(0.9, 0.9, 0.1, 0.1))
)
expect_error(
dCJS_vv(x = c(1,0,1,0,0), probCapture = c(1,0,1,0,0),
probSurvive = c(0.9, 0.9, 0.1, 0.1), len = 2)
)
expect_error(
dCJS_vv(x = c(0,0,1,0,0), probCapture = c(1,0,1,0,0),
probSurvive = c(0.9, 0.9, 0.1, 0.1))
)
### Compiled errors
CdCJS_ss <- compileNimble(dCJS_ss)
CdCJS_sv <- compileNimble(dCJS_sv)
CdCJS_vs <- compileNimble(dCJS_vs)
CdCJS_vv <- compileNimble(dCJS_vv)
expect_error(
CdCJS_ss(x = c(1,0,1,0,0), probCapture = 0.4, probSurvive = 0.5, len = 3)
)
expect_error(
CdCJS_ss(x = c(0,0,1,0,0), probCapture = 0.4, probSurvive = 0.5)
)
# dCJS_sv error checks
expect_error(
CdCJS_vs(x = c(1,0,1,0,0), probCapture = 0.1, probSurvive = c(0.9, 0.9, 0.4, 0.4), len = 3)
)
expect_error(
CdCJS_vs(x = c(1,0,1,0,0), probCapture = 0.1, probSurvive = c(0.9, 0.9, 0.4))
)
expect_error(
CdCJS_vs(x = c(0,0,1,0,0), probCapture = 0.1, probSurvive = c(0.9, 0.9, 0.4, 0.4))
)
# dCJS_vs error checks
expect_error(
CdCJS_sv(x = c(1,0,1,0,0), probCapture = c(1,0.9, 0.9, 0.4, 0.4), probSurvive = 0.1, len = 3)
)
expect_error(
CdCJS_sv(x = c(1,0,1,0,0), probCapture = c(1,0.9, 0.9, 0.4), probSurvive = 0.8)
)
expect_error(
CdCJS_sv(x = c(0,0,1,0,0), probCapture = c(1,0.9, 0.9, 0.4, 0.4), probSurvive = 0.1)
)
# dCJS_vv error checks
expect_error(
CdCJS_vv(x = c(1,0,1,0,0), probCapture = c(1,0,1,0.3,0.3), probSurvive = c(0.9, 0.9))
)
expect_error(
CdCJS_vv(x = c(1,0,1,0,0), probCapture = c(0,1), probSurvive = c(0.9, 0.9, 0.1, 0.1))
)
expect_error(
CdCJS_vv(x = c(1,0,1,0,0), probCapture = c(1,0,1,0,0),
probSurvive = c(0.9, 0.9, 0.1, 0.1), len = 2)
)
expect_error(
CdCJS_vv(x = c(0,0,1,0,0), probCapture = c(1,0,1,0,0),
probSurvive = c(0.9, 0.9, 0.1, 0.1))
)
})
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