tests/testthat/test-shinyInputToClass.R
In Pandora-IsoMemo/resources: Food Reconstruction Using Isotopic Transferred Signals
test_that("test shinyInputToClass - blackBearData with default inputs", {
testData <-
readRDS(testthat::test_path("blackBearData_default.rds"))
# new column added: values[["optimalPrior"]], default value == TRUE
testData$optimalPrior <- TRUE
resObject <- shinyInputToClass(testData,
as.list(NULL),
as.list(NULL))
expect_identical(resObject$data[["obsvn"]][1:3], c(-20.6, -20.9, -22.4))
expect_identical(resObject$data[["obsvnError"]][1:3], c(1, 1, 1))
expect_identical(resObject$data[["weights"]][1:3], c(100, 0, 0))
expect_identical(resObject$data[["weightsUncert"]][1:3], c(0, 0, 0))
expect_identical(resObject$data[["concentration"]][1:3], c(47, 53, 4))
expect_identical(resObject$data[["concentrationUncert"]][1:3], c(4.2, 2.6, 4.4))
expect_identical(resObject$data[["source"]][1:3], c(-21.939, -16.943, -21.939))
expect_identical(resObject$data[["sourceUncert"]][1:3], c(0.94608, 0.78144, 0.94608))
expect_identical(resObject$data[["covariates"]],
structure(
"",
dim = c(1L, 1L),
dimnames = list("Individual_1",
"Covariate_1")
))
expect_identical(resObject$data[["sourceDirichPrior"]], c(0.4, 0.4)) # if optimalPrior == TRUE
expect_identical(resObject$priors, list())
expect_identical(resObject$modelOptions[["modelType"]], "2")
expect_identical(resObject$modelOptions[["modelWeights"]], FALSE)
expect_identical(resObject$modelOptions[["categoricalVars"]], NULL)
expect_identical(resObject$modelOptions[["numericVars"]], NULL)
expect_identical(resObject$modelOptions[["modelWeightsContrained"]], TRUE)
expect_identical(resObject$modelOptions[["modelConcentrations"]], TRUE)
expect_identical(resObject$modelOptions[["modelConcentrationsContrained"]], TRUE)
expect_identical(resObject$modelOptions[["minUnc"]], 0.005)
expect_identical(resObject$modelOptions[["targetOffset"]], FALSE)
expect_identical(resObject$modelOptions[["includeSourceOffset"]], FALSE)
expect_identical(resObject$modelOptions[["burnin"]], 10000L)
expect_identical(resObject$modelOptions[["iterations"]], 10000L)
expect_identical(resObject$modelOptions[["thinning"]], 10L)
expect_identical(resObject$modelOptions[["nchains"]], 1L)
expect_identical(resObject$modelOptions[["hierarchical"]], FALSE)
expect_identical(resObject$modelOptions[["weightsDist"]], "normal")
expect_identical(resObject$modelOptions[["sourceDist"]], list(default = "normal"))
expect_identical(resObject$modelOptions[["sourceDistCovRep"]], list(default = FALSE))
expect_identical(resObject$modelOptions[["concentrationDist"]], "normal")
expect_identical(resObject$modelOptions[["concentrationDistCovRep"]], FALSE)
expect_identical(resObject$modelOptions[["obsvnDist"]], list(default = "normal"))
expect_identical(resObject$modelOptions[["inflatedBeta"]], "0")
expect_identical(resObject$modelOptions[["alphaHyper"]], 1L)
expect_identical(resObject$modelOptions[["covariateType"]], "0")
expect_identical(resObject$constants[["nTargets"]], 55L)
expect_identical(resObject$constants[["nSources"]], 2L)
expect_identical(resObject$constants[["nFractions"]], 2L)
expect_identical(resObject$constants[["nProxies"]], 2L)
expect_identical(resObject$constants[["nHierLevels"]], 0)
expect_identical(resObject$userEstimates[[1]], list())
expect_identical(resObject$userEstimates[[2]], list())
expect_identical(resObject$valueNames[["targets"]], c("d13C", "d15N"))
expect_identical(resObject$valueNames[["fractions"]], c("d13C", "d15N"))
expect_identical(resObject$valueNames[["sources"]], c("Natural", "Human"))
expect_identical(
as.character(resObject$modelCode),
c(
"{",
"for (h_ in 1:nTargets) {\n for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n component.contrib_[h_, j_, k_] <- W_[k_, j_] * sum(alpha_[h_, 1:nSources] * C_[1:nSources, j_] * (I_[1:nSources, j_, k_]))/sum(alpha_[h_, 1:nSources] * C_[1:nSources, j_])\n }\n mu[h_, k_] <- sum(component.contrib_[h_, 1:nFractions, k_])/sum(W_[k_, 1:nFractions])\n obsvn[h_, k_] ~ dnorm(mu[h_, k_], pow(obsvnError[h_, k_], -2))\n }\n}",
"for (h_ in 1:nTargets) {\n for (j_ in 1:nFractions) {\n beta_[h_, j_] <- sum(alpha_[h_, 1:nSources] * C_[1:nSources, j_])/sum(alpha_[h_, 1:nSources] %*% C_[1:nSources, 1:nFractions])\n }\n}",
"for (h_ in 1:nTargets) {\n for (i_ in 1:nSources) {\n for (j_ in 1:nFractions) {\n aux6_[h_, i_, j_] <- alpha_[h_, i_] * C_[i_, j_]/sum(alpha_[h_, 1:nSources] * C_[1:nSources, j_])\n }\n }\n for (k_ in 1:nProxies) {\n for (i_ in 1:nSources) {\n theta_[h_, k_, i_] <- sum(aux6_[h_, i_, 1:nFractions] * W_[k_, 1:nFractions])/sum(aux6_[h_, 1:nSources, 1:nFractions] %*% W_[k_, 1:nFractions])\n }\n }\n}",
"for (h_ in 1:nTargets) {\n alpha_[h_, 1:nSources] ~ ddirch(sourceDirichPrior[1:nSources])\n}",
"for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n for (i_ in 1:nSources) {\n I_[i_, j_, k_] <- I0_[i_, j_, k_]\n }\n }\n}",
"for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n for (i_ in 1:nSources) {\n I0_[i_, j_, k_] ~ dnorm(source[i_, j_, k_], pow(sourceUncert[i_, j_, k_], -2))\n }\n }\n}",
"for (j_ in 1:nFractions) {\n for (i_ in 1:nSources) {\n C_[i_, j_] ~ T(dnorm(concentration[i_, j_], pow(concentrationUncert[i_, j_], -2)), 0, 100)\n }\n}",
"for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n W_[k_, j_] ~ T(dnorm(weights[k_, j_], pow(weightsUncert[k_, j_], -2)), -1e-04, 100)\n }\n}"
)
)
})
test_that("test shinyInputToClass - brownBearData with default inputs", {
testData <-
readRDS(testthat::test_path("brownBearData_default.rds"))
# new column added: values[["optimalPrior"]], default value == TRUE
testData$optimalPrior <- TRUE
resObject <- shinyInputToClass(testData,
as.list(NULL),
as.list(NULL))
expect_identical(resObject$data[["obsvn"]][1:3], c(-22.3, -23.1, -21.5))
expect_identical(resObject$data[["obsvnError"]][1:3], c(1, 1, 1))
expect_identical(resObject$data[["weights"]],
structure(
100,
dim = c(1L, 1L),
dimnames = list("d13C", "d13C")
))
expect_identical(resObject$data[["weightsUncert"]],
structure(
0,
dim = c(1L, 1L),
dimnames = list("d13C", "d13C")
))
expect_identical(resObject$data[["concentration"]], NULL)
expect_identical(resObject$data[["concentrationUncert"]], NULL)
expect_identical(resObject$data[["source"]],
structure(
c(-25.798, -10.714),
dim = c(2L, 1L, 1L),
dimnames = list(c("C3", "Corn"), "d13C", "d13C")
))
expect_identical(resObject$data[["sourceUncert"]],
structure(
c(2.2247, 1.0367),
dim = c(2L, 1L, 1L),
dimnames = list(c("C3", "Corn"), "d13C", "d13C")
))
expect_identical(resObject$data[["covariates"]][1:3], c("f", "f", "f"))
expect_identical(resObject$data[["sourceDirichPrior"]], c(0.4, 0.4)) # if optimalPrior == TRUE
expect_identical(resObject$priors, list())
expect_identical(resObject$modelOptions[["modelType"]], "2")
expect_identical(resObject$modelOptions[["modelWeights"]], FALSE)
expect_identical(resObject$modelOptions[["categoricalVars"]], c("group", "year"))
expect_identical(resObject$modelOptions[["numericVars"]], character(0))
expect_identical(resObject$modelOptions[["modelWeightsContrained"]], TRUE)
expect_identical(resObject$modelOptions[["modelConcentrations"]], FALSE)
expect_identical(resObject$modelOptions[["modelConcentrationsContrained"]], TRUE)
expect_identical(resObject$modelOptions[["minUnc"]], 0.005)
expect_identical(resObject$modelOptions[["targetOffset"]], FALSE)
expect_identical(resObject$modelOptions[["includeSourceOffset"]], FALSE)
expect_identical(resObject$modelOptions[["burnin"]], 10000)
expect_identical(resObject$modelOptions[["iterations"]], 10000)
expect_identical(resObject$modelOptions[["thinning"]], 10)
expect_identical(resObject$modelOptions[["nchains"]], 1)
expect_identical(resObject$modelOptions[["hierarchical"]], TRUE)
expect_identical(resObject$modelOptions[["weightsDist"]], list(default = "normal"))
expect_identical(resObject$modelOptions[["sourceDist"]], list(default = "normal"))
expect_identical(resObject$modelOptions[["sourceDistCovRep"]], list(default = FALSE))
expect_identical(resObject$modelOptions[["concentrationDist"]], "normal")
expect_identical(resObject$modelOptions[["concentrationDistCovRep"]], FALSE)
expect_identical(resObject$modelOptions[["obsvnDist"]], list(default = "normal"))
expect_identical(resObject$modelOptions[["inflatedBeta"]], "0")
expect_identical(resObject$modelOptions[["alphaHyper"]], 1)
expect_identical(resObject$modelOptions[["covariateType"]], "2")
expect_identical(resObject$constants[["nTargets"]], 40L)
expect_identical(resObject$constants[["nSources"]], 2L)
expect_identical(resObject$constants[["nFractions"]], 1L)
expect_identical(resObject$constants[["nProxies"]], 1L)
expect_identical(resObject$constants[["nHierLevels"]], 4L)
expect_identical(resObject$userEstimates[[1]], list())
expect_identical(resObject$userEstimates[[2]], list())
expect_identical(resObject$valueNames[["targets"]], c("d13C"))
expect_identical(resObject$valueNames[["fractions"]], c("d13C"))
expect_identical(resObject$valueNames[["sources"]], c("C3", "Corn"))
expect_identical(
as.character(resObject$modelCode),
c(
"{",
"for (h_ in 1:nTargets) {\n for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n component.contrib_[h_, j_, k_] <- W_[k_, j_] * sum(alpha_[h_, 1:nSources] * (I_[1:nSources, j_, k_]))/sum(alpha_[h_, 1:nSources])\n }\n mu[h_, k_] <- sum(component.contrib_[h_, 1:nFractions, k_])/sum(W_[k_, 1:nFractions])\n obsvn[h_, k_] ~ dnorm(mu[h_, k_], pow(obsvnError[h_, k_], -2))\n }\n}",
"for (h_ in 1:nTargets) {\n for (j_ in 1:nFractions) {\n beta_[h_, j_] <- sum(alpha_[h_, 1:nSources])/sum(alpha_[h_, 1:nSources])\n }\n}",
"for (h_ in 1:nTargets) {\n for (i_ in 1:nSources) {\n for (j_ in 1:nFractions) {\n aux6_[h_, i_, j_] <- alpha_[h_, i_]/sum(alpha_[h_, 1:nSources])\n }\n }\n for (k_ in 1:nProxies) {\n for (i_ in 1:nSources) {\n theta_[h_, k_, i_] <- sum(aux6_[h_, i_, 1:nFractions] * W_[k_, 1:nFractions])/sum(aux6_[h_, 1:nSources, 1:nFractions] %*% W_[k_, 1:nFractions])\n }\n }\n}",
"for (h_ in 1:nTargets) {\n dirParams[h_, 1:nSources] <- q[hierMatch[h_], 1:nSources] * sourceDirichPrior[1:nSources]\n alpha_[h_, 1:nSources] ~ ddirch(dirParams[h_, 1:nSources])\n}",
"for (i_ in 1:nSources) {\n for (g_ in 1:nHierLevels) {\n q[g_, i_] ~ dlnorm(muQ, sdQ)\n }\n}",
"muQ ~ dnorm(0, 5)",
"sdQ ~ dunif(0, 5)",
"for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n for (i_ in 1:nSources) {\n I_[i_, j_, k_] <- I0_[i_, j_, k_]\n }\n }\n}",
"for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n for (i_ in 1:nSources) {\n I0_[i_, j_, k_] ~ dnorm(source[i_, j_, k_], pow(sourceUncert[i_, j_, k_], -2))\n }\n }\n}",
"for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n W_[k_, j_] ~ T(dnorm(weights[k_, j_], pow(weightsUncert[k_, j_], -2)), -1e-04, 100)\n }\n}"
)
)
})
Pandora-IsoMemo/resources documentation built on Nov. 21, 2024, 3:56 a.m.
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test_that("test shinyInputToClass - blackBearData with default inputs", {
testData <-
readRDS(testthat::test_path("blackBearData_default.rds"))
# new column added: values[["optimalPrior"]], default value == TRUE
testData$optimalPrior <- TRUE
resObject <- shinyInputToClass(testData,
as.list(NULL),
as.list(NULL))
expect_identical(resObject$data[["obsvn"]][1:3], c(-20.6, -20.9, -22.4))
expect_identical(resObject$data[["obsvnError"]][1:3], c(1, 1, 1))
expect_identical(resObject$data[["weights"]][1:3], c(100, 0, 0))
expect_identical(resObject$data[["weightsUncert"]][1:3], c(0, 0, 0))
expect_identical(resObject$data[["concentration"]][1:3], c(47, 53, 4))
expect_identical(resObject$data[["concentrationUncert"]][1:3], c(4.2, 2.6, 4.4))
expect_identical(resObject$data[["source"]][1:3], c(-21.939, -16.943, -21.939))
expect_identical(resObject$data[["sourceUncert"]][1:3], c(0.94608, 0.78144, 0.94608))
expect_identical(resObject$data[["covariates"]],
structure(
"",
dim = c(1L, 1L),
dimnames = list("Individual_1",
"Covariate_1")
))
expect_identical(resObject$data[["sourceDirichPrior"]], c(0.4, 0.4)) # if optimalPrior == TRUE
expect_identical(resObject$priors, list())
expect_identical(resObject$modelOptions[["modelType"]], "2")
expect_identical(resObject$modelOptions[["modelWeights"]], FALSE)
expect_identical(resObject$modelOptions[["categoricalVars"]], NULL)
expect_identical(resObject$modelOptions[["numericVars"]], NULL)
expect_identical(resObject$modelOptions[["modelWeightsContrained"]], TRUE)
expect_identical(resObject$modelOptions[["modelConcentrations"]], TRUE)
expect_identical(resObject$modelOptions[["modelConcentrationsContrained"]], TRUE)
expect_identical(resObject$modelOptions[["minUnc"]], 0.005)
expect_identical(resObject$modelOptions[["targetOffset"]], FALSE)
expect_identical(resObject$modelOptions[["includeSourceOffset"]], FALSE)
expect_identical(resObject$modelOptions[["burnin"]], 10000L)
expect_identical(resObject$modelOptions[["iterations"]], 10000L)
expect_identical(resObject$modelOptions[["thinning"]], 10L)
expect_identical(resObject$modelOptions[["nchains"]], 1L)
expect_identical(resObject$modelOptions[["hierarchical"]], FALSE)
expect_identical(resObject$modelOptions[["weightsDist"]], "normal")
expect_identical(resObject$modelOptions[["sourceDist"]], list(default = "normal"))
expect_identical(resObject$modelOptions[["sourceDistCovRep"]], list(default = FALSE))
expect_identical(resObject$modelOptions[["concentrationDist"]], "normal")
expect_identical(resObject$modelOptions[["concentrationDistCovRep"]], FALSE)
expect_identical(resObject$modelOptions[["obsvnDist"]], list(default = "normal"))
expect_identical(resObject$modelOptions[["inflatedBeta"]], "0")
expect_identical(resObject$modelOptions[["alphaHyper"]], 1L)
expect_identical(resObject$modelOptions[["covariateType"]], "0")
expect_identical(resObject$constants[["nTargets"]], 55L)
expect_identical(resObject$constants[["nSources"]], 2L)
expect_identical(resObject$constants[["nFractions"]], 2L)
expect_identical(resObject$constants[["nProxies"]], 2L)
expect_identical(resObject$constants[["nHierLevels"]], 0)
expect_identical(resObject$userEstimates[[1]], list())
expect_identical(resObject$userEstimates[[2]], list())
expect_identical(resObject$valueNames[["targets"]], c("d13C", "d15N"))
expect_identical(resObject$valueNames[["fractions"]], c("d13C", "d15N"))
expect_identical(resObject$valueNames[["sources"]], c("Natural", "Human"))
expect_identical(
as.character(resObject$modelCode),
c(
"{",
"for (h_ in 1:nTargets) {\n for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n component.contrib_[h_, j_, k_] <- W_[k_, j_] * sum(alpha_[h_, 1:nSources] * C_[1:nSources, j_] * (I_[1:nSources, j_, k_]))/sum(alpha_[h_, 1:nSources] * C_[1:nSources, j_])\n }\n mu[h_, k_] <- sum(component.contrib_[h_, 1:nFractions, k_])/sum(W_[k_, 1:nFractions])\n obsvn[h_, k_] ~ dnorm(mu[h_, k_], pow(obsvnError[h_, k_], -2))\n }\n}",
"for (h_ in 1:nTargets) {\n for (j_ in 1:nFractions) {\n beta_[h_, j_] <- sum(alpha_[h_, 1:nSources] * C_[1:nSources, j_])/sum(alpha_[h_, 1:nSources] %*% C_[1:nSources, 1:nFractions])\n }\n}",
"for (h_ in 1:nTargets) {\n for (i_ in 1:nSources) {\n for (j_ in 1:nFractions) {\n aux6_[h_, i_, j_] <- alpha_[h_, i_] * C_[i_, j_]/sum(alpha_[h_, 1:nSources] * C_[1:nSources, j_])\n }\n }\n for (k_ in 1:nProxies) {\n for (i_ in 1:nSources) {\n theta_[h_, k_, i_] <- sum(aux6_[h_, i_, 1:nFractions] * W_[k_, 1:nFractions])/sum(aux6_[h_, 1:nSources, 1:nFractions] %*% W_[k_, 1:nFractions])\n }\n }\n}",
"for (h_ in 1:nTargets) {\n alpha_[h_, 1:nSources] ~ ddirch(sourceDirichPrior[1:nSources])\n}",
"for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n for (i_ in 1:nSources) {\n I_[i_, j_, k_] <- I0_[i_, j_, k_]\n }\n }\n}",
"for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n for (i_ in 1:nSources) {\n I0_[i_, j_, k_] ~ dnorm(source[i_, j_, k_], pow(sourceUncert[i_, j_, k_], -2))\n }\n }\n}",
"for (j_ in 1:nFractions) {\n for (i_ in 1:nSources) {\n C_[i_, j_] ~ T(dnorm(concentration[i_, j_], pow(concentrationUncert[i_, j_], -2)), 0, 100)\n }\n}",
"for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n W_[k_, j_] ~ T(dnorm(weights[k_, j_], pow(weightsUncert[k_, j_], -2)), -1e-04, 100)\n }\n}"
)
)
})
test_that("test shinyInputToClass - brownBearData with default inputs", {
testData <-
readRDS(testthat::test_path("brownBearData_default.rds"))
# new column added: values[["optimalPrior"]], default value == TRUE
testData$optimalPrior <- TRUE
resObject <- shinyInputToClass(testData,
as.list(NULL),
as.list(NULL))
expect_identical(resObject$data[["obsvn"]][1:3], c(-22.3, -23.1, -21.5))
expect_identical(resObject$data[["obsvnError"]][1:3], c(1, 1, 1))
expect_identical(resObject$data[["weights"]],
structure(
100,
dim = c(1L, 1L),
dimnames = list("d13C", "d13C")
))
expect_identical(resObject$data[["weightsUncert"]],
structure(
0,
dim = c(1L, 1L),
dimnames = list("d13C", "d13C")
))
expect_identical(resObject$data[["concentration"]], NULL)
expect_identical(resObject$data[["concentrationUncert"]], NULL)
expect_identical(resObject$data[["source"]],
structure(
c(-25.798, -10.714),
dim = c(2L, 1L, 1L),
dimnames = list(c("C3", "Corn"), "d13C", "d13C")
))
expect_identical(resObject$data[["sourceUncert"]],
structure(
c(2.2247, 1.0367),
dim = c(2L, 1L, 1L),
dimnames = list(c("C3", "Corn"), "d13C", "d13C")
))
expect_identical(resObject$data[["covariates"]][1:3], c("f", "f", "f"))
expect_identical(resObject$data[["sourceDirichPrior"]], c(0.4, 0.4)) # if optimalPrior == TRUE
expect_identical(resObject$priors, list())
expect_identical(resObject$modelOptions[["modelType"]], "2")
expect_identical(resObject$modelOptions[["modelWeights"]], FALSE)
expect_identical(resObject$modelOptions[["categoricalVars"]], c("group", "year"))
expect_identical(resObject$modelOptions[["numericVars"]], character(0))
expect_identical(resObject$modelOptions[["modelWeightsContrained"]], TRUE)
expect_identical(resObject$modelOptions[["modelConcentrations"]], FALSE)
expect_identical(resObject$modelOptions[["modelConcentrationsContrained"]], TRUE)
expect_identical(resObject$modelOptions[["minUnc"]], 0.005)
expect_identical(resObject$modelOptions[["targetOffset"]], FALSE)
expect_identical(resObject$modelOptions[["includeSourceOffset"]], FALSE)
expect_identical(resObject$modelOptions[["burnin"]], 10000)
expect_identical(resObject$modelOptions[["iterations"]], 10000)
expect_identical(resObject$modelOptions[["thinning"]], 10)
expect_identical(resObject$modelOptions[["nchains"]], 1)
expect_identical(resObject$modelOptions[["hierarchical"]], TRUE)
expect_identical(resObject$modelOptions[["weightsDist"]], list(default = "normal"))
expect_identical(resObject$modelOptions[["sourceDist"]], list(default = "normal"))
expect_identical(resObject$modelOptions[["sourceDistCovRep"]], list(default = FALSE))
expect_identical(resObject$modelOptions[["concentrationDist"]], "normal")
expect_identical(resObject$modelOptions[["concentrationDistCovRep"]], FALSE)
expect_identical(resObject$modelOptions[["obsvnDist"]], list(default = "normal"))
expect_identical(resObject$modelOptions[["inflatedBeta"]], "0")
expect_identical(resObject$modelOptions[["alphaHyper"]], 1)
expect_identical(resObject$modelOptions[["covariateType"]], "2")
expect_identical(resObject$constants[["nTargets"]], 40L)
expect_identical(resObject$constants[["nSources"]], 2L)
expect_identical(resObject$constants[["nFractions"]], 1L)
expect_identical(resObject$constants[["nProxies"]], 1L)
expect_identical(resObject$constants[["nHierLevels"]], 4L)
expect_identical(resObject$userEstimates[[1]], list())
expect_identical(resObject$userEstimates[[2]], list())
expect_identical(resObject$valueNames[["targets"]], c("d13C"))
expect_identical(resObject$valueNames[["fractions"]], c("d13C"))
expect_identical(resObject$valueNames[["sources"]], c("C3", "Corn"))
expect_identical(
as.character(resObject$modelCode),
c(
"{",
"for (h_ in 1:nTargets) {\n for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n component.contrib_[h_, j_, k_] <- W_[k_, j_] * sum(alpha_[h_, 1:nSources] * (I_[1:nSources, j_, k_]))/sum(alpha_[h_, 1:nSources])\n }\n mu[h_, k_] <- sum(component.contrib_[h_, 1:nFractions, k_])/sum(W_[k_, 1:nFractions])\n obsvn[h_, k_] ~ dnorm(mu[h_, k_], pow(obsvnError[h_, k_], -2))\n }\n}",
"for (h_ in 1:nTargets) {\n for (j_ in 1:nFractions) {\n beta_[h_, j_] <- sum(alpha_[h_, 1:nSources])/sum(alpha_[h_, 1:nSources])\n }\n}",
"for (h_ in 1:nTargets) {\n for (i_ in 1:nSources) {\n for (j_ in 1:nFractions) {\n aux6_[h_, i_, j_] <- alpha_[h_, i_]/sum(alpha_[h_, 1:nSources])\n }\n }\n for (k_ in 1:nProxies) {\n for (i_ in 1:nSources) {\n theta_[h_, k_, i_] <- sum(aux6_[h_, i_, 1:nFractions] * W_[k_, 1:nFractions])/sum(aux6_[h_, 1:nSources, 1:nFractions] %*% W_[k_, 1:nFractions])\n }\n }\n}",
"for (h_ in 1:nTargets) {\n dirParams[h_, 1:nSources] <- q[hierMatch[h_], 1:nSources] * sourceDirichPrior[1:nSources]\n alpha_[h_, 1:nSources] ~ ddirch(dirParams[h_, 1:nSources])\n}",
"for (i_ in 1:nSources) {\n for (g_ in 1:nHierLevels) {\n q[g_, i_] ~ dlnorm(muQ, sdQ)\n }\n}",
"muQ ~ dnorm(0, 5)",
"sdQ ~ dunif(0, 5)",
"for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n for (i_ in 1:nSources) {\n I_[i_, j_, k_] <- I0_[i_, j_, k_]\n }\n }\n}",
"for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n for (i_ in 1:nSources) {\n I0_[i_, j_, k_] ~ dnorm(source[i_, j_, k_], pow(sourceUncert[i_, j_, k_], -2))\n }\n }\n}",
"for (k_ in 1:nProxies) {\n for (j_ in 1:nFractions) {\n W_[k_, j_] ~ T(dnorm(weights[k_, j_], pow(weightsUncert[k_, j_], -2)), -1e-04, 100)\n }\n}"
)
)
})
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