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Apply suggestions from code review
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Co-authored-by: Don van den Bergh <[email protected]>
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@FBartos @vandenman
FBartos and vandenman authored Sep 29, 2023
1 parent 8edf6e3 commit 9113031
Showing 1 changed file with 0 additions and 185 deletions.
185 changes: 0 additions & 185 deletions tests/testthat/test-robustbayesianmetaanalysis.R
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Original file line number Diff line number Diff line change
Expand Up @@ -156,52 +156,6 @@ pathToFittedModel <- file.path("robmaFit.RDS")
}
{
options <- analysisOptions("RobustBayesianMetaAnalysis")
options$.meta <- list(effectSize = list(shouldEncode = TRUE), effectSizeCi = list(
shouldEncode = TRUE), effectSizeSe = list(shouldEncode = TRUE),
modelsEffect = list(a = list(isRCode = TRUE), alpha = list(
isRCode = TRUE), b = list(isRCode = TRUE), beta = list(
isRCode = TRUE), k = list(isRCode = TRUE), mu = list(
isRCode = TRUE), nu = list(isRCode = TRUE), priorWeight = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), theta = list(
isRCode = TRUE), truncationLower = list(isRCode = TRUE),
truncationUpper = list(isRCode = TRUE), x0 = list(isRCode = TRUE)),
modelsEffectNull = list(a = list(isRCode = TRUE), alpha = list(
isRCode = TRUE), b = list(isRCode = TRUE), beta = list(
isRCode = TRUE), k = list(isRCode = TRUE), mu = list(
isRCode = TRUE), nu = list(isRCode = TRUE), priorWeight = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), theta = list(
isRCode = TRUE), truncationLower = list(isRCode = TRUE),
truncationUpper = list(isRCode = TRUE), x0 = list(isRCode = TRUE)),
modelsHeterogeneity = list(a = list(isRCode = TRUE), alpha = list(
isRCode = TRUE), b = list(isRCode = TRUE), beta = list(
isRCode = TRUE), k = list(isRCode = TRUE), mu = list(
isRCode = TRUE), nu = list(isRCode = TRUE), priorWeight = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), theta = list(
isRCode = TRUE), truncationLower = list(isRCode = TRUE),
truncationUpper = list(isRCode = TRUE), x0 = list(isRCode = TRUE)),
modelsHeterogeneityNull = list(a = list(isRCode = TRUE),
alpha = list(isRCode = TRUE), b = list(isRCode = TRUE),
beta = list(isRCode = TRUE), k = list(isRCode = TRUE),
mu = list(isRCode = TRUE), nu = list(isRCode = TRUE),
priorWeight = list(isRCode = TRUE), sigma = list(isRCode = TRUE),
theta = list(isRCode = TRUE), truncationLower = list(
isRCode = TRUE), truncationUpper = list(isRCode = TRUE),
x0 = list(isRCode = TRUE)), modelsPeese = list(a = list(
isRCode = TRUE), alpha = list(isRCode = TRUE), b = list(
isRCode = TRUE), beta = list(isRCode = TRUE), k = list(
isRCode = TRUE), mu = list(isRCode = TRUE), nu = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), truncationLower = list(
isRCode = TRUE), truncationUpper = list(isRCode = TRUE),
x0 = list(isRCode = TRUE)), modelsPet = list(a = list(
isRCode = TRUE), alpha = list(isRCode = TRUE), b = list(
isRCode = TRUE), beta = list(isRCode = TRUE), k = list(
isRCode = TRUE), mu = list(isRCode = TRUE), nu = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), theta = list(
isRCode = TRUE), truncationLower = list(isRCode = TRUE),
truncationUpper = list(isRCode = TRUE), x0 = list(isRCode = TRUE)),
modelsSelectionModelsNull = list(priorWeight = list(isRCode = TRUE)),
sampleSize = list(shouldEncode = TRUE), studyLabel = list(
shouldEncode = TRUE))
options$advancedAutofitMaximumFittingTime <- FALSE
options$advancedAutofitMcmcError <- FALSE
options$advancedAutofitMcmcErrorSd <- FALSE
Expand Down Expand Up @@ -288,52 +242,6 @@ pathToFittedModel <- file.path("robmaFit.RDS")
### custom model settings (testing different distributions and prior plots)
{
options <- analysisOptions("RobustBayesianMetaAnalysis")
options$.meta <- list(a = list(isRCode = TRUE), effectSize = list(shouldEncode = TRUE),
effectSizeCi = list(shouldEncode = TRUE), effectSizeSe = list(
shouldEncode = TRUE), modelsEffect = list(a = list(isRCode = TRUE),
alpha = list(isRCode = TRUE), b = list(isRCode = TRUE),
beta = list(isRCode = TRUE), k = list(isRCode = TRUE),
mu = list(isRCode = TRUE), nu = list(isRCode = TRUE),
priorWeight = list(isRCode = TRUE), sigma = list(isRCode = TRUE),
theta = list(isRCode = TRUE), truncationLower = list(
isRCode = TRUE), truncationUpper = list(isRCode = TRUE),
x0 = list(isRCode = TRUE)), modelsEffectNull = list(a = list(
isRCode = TRUE), alpha = list(isRCode = TRUE), b = list(
isRCode = TRUE), beta = list(isRCode = TRUE), k = list(
isRCode = TRUE), mu = list(isRCode = TRUE), nu = list(
isRCode = TRUE), priorWeight = list(isRCode = TRUE),
sigma = list(isRCode = TRUE), theta = list(isRCode = TRUE),
truncationLower = list(isRCode = TRUE), truncationUpper = list(
isRCode = TRUE), x0 = list(isRCode = TRUE)), modelsHeterogeneity = list(
a = list(isRCode = TRUE), alpha = list(isRCode = TRUE),
b = list(isRCode = TRUE), beta = list(isRCode = TRUE),
k = list(isRCode = TRUE), mu = list(isRCode = TRUE),
nu = list(isRCode = TRUE), priorWeight = list(isRCode = TRUE),
sigma = list(isRCode = TRUE), theta = list(isRCode = TRUE),
truncationLower = list(isRCode = TRUE), truncationUpper = list(
isRCode = TRUE), x0 = list(isRCode = TRUE)), modelsHeterogeneityNull = list(
a = list(isRCode = TRUE), alpha = list(isRCode = TRUE),
b = list(isRCode = TRUE), beta = list(isRCode = TRUE),
k = list(isRCode = TRUE), mu = list(isRCode = TRUE),
nu = list(isRCode = TRUE), priorWeight = list(isRCode = TRUE),
sigma = list(isRCode = TRUE), theta = list(isRCode = TRUE),
truncationLower = list(isRCode = TRUE), truncationUpper = list(
isRCode = TRUE), x0 = list(isRCode = TRUE)), modelsPeese = list(
a = list(isRCode = TRUE), alpha = list(isRCode = TRUE),
b = list(isRCode = TRUE), beta = list(isRCode = TRUE),
k = list(isRCode = TRUE), mu = list(isRCode = TRUE),
nu = list(isRCode = TRUE), sigma = list(isRCode = TRUE),
truncationLower = list(isRCode = TRUE), truncationUpper = list(
isRCode = TRUE), x0 = list(isRCode = TRUE)), modelsPet = list(
a = list(isRCode = TRUE), alpha = list(isRCode = TRUE),
b = list(isRCode = TRUE), beta = list(isRCode = TRUE),
k = list(isRCode = TRUE), mu = list(isRCode = TRUE),
nu = list(isRCode = TRUE), sigma = list(isRCode = TRUE),
theta = list(isRCode = TRUE), truncationLower = list(
isRCode = TRUE), truncationUpper = list(isRCode = TRUE),
x0 = list(isRCode = TRUE)), modelsSelectionModelsNull = list(
priorWeight = list(isRCode = TRUE)), sampleSize = list(
shouldEncode = TRUE), studyLabel = list(shouldEncode = TRUE))
options$advancedAutofitMaximumFittingTime <- FALSE
options$advancedAutofitMcmcError <- FALSE
options$advancedAutofitMcmcErrorSd <- FALSE
Expand Down Expand Up @@ -599,53 +507,6 @@ pathToFittedModel <- file.path("robmaFit.RDS")
### fit a small model using d + se, with minimum samples, no autofit, & and the complete output
{
options <- analysisOptions("RobustBayesianMetaAnalysis")
options$.meta <- list(effectSize = list(shouldEncode = FALSE), effectSizeCi = list(
shouldEncode = TRUE), effectSizeSe = list(shouldEncode = FALSE),
modelsEffect = list(a = list(isRCode = TRUE), alpha = list(
isRCode = TRUE), b = list(isRCode = TRUE), beta = list(
isRCode = TRUE), k = list(isRCode = TRUE), mu = list(
isRCode = TRUE), nu = list(isRCode = TRUE), priorWeight = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), theta = list(
isRCode = TRUE), truncationLower = list(isRCode = TRUE),
truncationUpper = list(isRCode = TRUE), x0 = list(isRCode = TRUE)),
modelsEffectNull = list(a = list(isRCode = TRUE), alpha = list(
isRCode = TRUE), b = list(isRCode = TRUE), beta = list(
isRCode = TRUE), k = list(isRCode = TRUE), mu = list(
isRCode = TRUE), nu = list(isRCode = TRUE), priorWeight = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), theta = list(
isRCode = TRUE), truncationLower = list(isRCode = TRUE),
truncationUpper = list(isRCode = TRUE), x0 = list(isRCode = TRUE)),
modelsHeterogeneity = list(a = list(isRCode = TRUE), alpha = list(
isRCode = TRUE), b = list(isRCode = TRUE), beta = list(
isRCode = TRUE), k = list(isRCode = TRUE), mu = list(
isRCode = TRUE), nu = list(isRCode = TRUE), priorWeight = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), theta = list(
isRCode = TRUE), truncationLower = list(isRCode = TRUE),
truncationUpper = list(isRCode = TRUE), x0 = list(isRCode = TRUE)),
modelsHeterogeneityNull = list(a = list(isRCode = TRUE),
alpha = list(isRCode = TRUE), b = list(isRCode = TRUE),
beta = list(isRCode = TRUE), k = list(isRCode = TRUE),
mu = list(isRCode = TRUE), nu = list(isRCode = TRUE),
priorWeight = list(isRCode = TRUE), sigma = list(isRCode = TRUE),
theta = list(isRCode = TRUE), truncationLower = list(
isRCode = TRUE), truncationUpper = list(isRCode = TRUE),
x0 = list(isRCode = TRUE)), modelsPeese = list(a = list(
isRCode = TRUE), alpha = list(isRCode = TRUE), b = list(
isRCode = TRUE), beta = list(isRCode = TRUE), k = list(
isRCode = TRUE), mu = list(isRCode = TRUE), nu = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), truncationLower = list(
isRCode = TRUE), truncationUpper = list(isRCode = TRUE),
x0 = list(isRCode = TRUE)), modelsPet = list(a = list(
isRCode = TRUE), alpha = list(isRCode = TRUE), b = list(
isRCode = TRUE), beta = list(isRCode = TRUE), k = list(
isRCode = TRUE), mu = list(isRCode = TRUE), nu = list(
isRCode = TRUE), priorWeight = list(isRCode = TRUE),
sigma = list(isRCode = TRUE), theta = list(isRCode = TRUE),
truncationLower = list(isRCode = TRUE), truncationUpper = list(
isRCode = TRUE), x0 = list(isRCode = TRUE)), modelsSelectionModels = list(
priorWeight = list(isRCode = TRUE)), modelsSelectionModelsNull = list(
priorWeight = list(isRCode = TRUE)), sampleSize = list(
shouldEncode = TRUE), studyLabel = list(shouldEncode = FALSE))
options$advancedAutofitMaximumFittingTime <- FALSE
options$advancedAutofitMcmcError <- FALSE
options$advancedAutofitMcmcErrorSd <- FALSE
Expand Down Expand Up @@ -953,52 +814,6 @@ pathToFittedModel <- file.path("robmaFit.RDS")
### more options tested using a pre-loaded model: modify BF type, CI width, model ordering, output scale
{
options <- analysisOptions("RobustBayesianMetaAnalysis")
options$.meta <- list(effectSize = list(shouldEncode = TRUE), effectSizeCi = list(
shouldEncode = TRUE), effectSizeSe = list(shouldEncode = TRUE),
modelsEffect = list(a = list(isRCode = TRUE), alpha = list(
isRCode = TRUE), b = list(isRCode = TRUE), beta = list(
isRCode = TRUE), k = list(isRCode = TRUE), mu = list(
isRCode = TRUE), nu = list(isRCode = TRUE), priorWeight = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), theta = list(
isRCode = TRUE), truncationLower = list(isRCode = TRUE),
truncationUpper = list(isRCode = TRUE), x0 = list(isRCode = TRUE)),
modelsEffectNull = list(a = list(isRCode = TRUE), alpha = list(
isRCode = TRUE), b = list(isRCode = TRUE), beta = list(
isRCode = TRUE), k = list(isRCode = TRUE), mu = list(
isRCode = TRUE), nu = list(isRCode = TRUE), priorWeight = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), theta = list(
isRCode = TRUE), truncationLower = list(isRCode = TRUE),
truncationUpper = list(isRCode = TRUE), x0 = list(isRCode = TRUE)),
modelsHeterogeneity = list(a = list(isRCode = TRUE), alpha = list(
isRCode = TRUE), b = list(isRCode = TRUE), beta = list(
isRCode = TRUE), k = list(isRCode = TRUE), mu = list(
isRCode = TRUE), nu = list(isRCode = TRUE), priorWeight = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), theta = list(
isRCode = TRUE), truncationLower = list(isRCode = TRUE),
truncationUpper = list(isRCode = TRUE), x0 = list(isRCode = TRUE)),
modelsHeterogeneityNull = list(a = list(isRCode = TRUE),
alpha = list(isRCode = TRUE), b = list(isRCode = TRUE),
beta = list(isRCode = TRUE), k = list(isRCode = TRUE),
mu = list(isRCode = TRUE), nu = list(isRCode = TRUE),
priorWeight = list(isRCode = TRUE), sigma = list(isRCode = TRUE),
theta = list(isRCode = TRUE), truncationLower = list(
isRCode = TRUE), truncationUpper = list(isRCode = TRUE),
x0 = list(isRCode = TRUE)), modelsPeese = list(a = list(
isRCode = TRUE), alpha = list(isRCode = TRUE), b = list(
isRCode = TRUE), beta = list(isRCode = TRUE), k = list(
isRCode = TRUE), mu = list(isRCode = TRUE), nu = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), truncationLower = list(
isRCode = TRUE), truncationUpper = list(isRCode = TRUE),
x0 = list(isRCode = TRUE)), modelsPet = list(a = list(
isRCode = TRUE), alpha = list(isRCode = TRUE), b = list(
isRCode = TRUE), beta = list(isRCode = TRUE), k = list(
isRCode = TRUE), mu = list(isRCode = TRUE), nu = list(
isRCode = TRUE), sigma = list(isRCode = TRUE), theta = list(
isRCode = TRUE), truncationLower = list(isRCode = TRUE),
truncationUpper = list(isRCode = TRUE), x0 = list(isRCode = TRUE)),
modelsSelectionModelsNull = list(priorWeight = list(isRCode = TRUE)),
sampleSize = list(shouldEncode = TRUE), studyLabel = list(
shouldEncode = TRUE))
options$advancedAutofitMaximumFittingTime <- FALSE
options$advancedAutofitMcmcError <- FALSE
options$advancedAutofitMcmcErrorSd <- FALSE
Expand Down

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