#
# Copyright (C) 2013-2022 University of Amsterdam
#
# This program 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 2 of the License, or
# (at your option) any later version.
#
# This program 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 this program. If not, see <http://www.gnu.org/licenses/>.
#
# This is a generated file. Don't change it
RegressionLogisticBayesian <- function(
data = NULL,
version = "0.18.2",
bayesFactorOrder = "bestModelTop",
bayesFactorType = "BF10",
bernoulliParam = 0.5,
betaBinomialParamA = 1,
betaBinomialParamB = 1,
castilloParamU = 1,
cchPriorAlpha = 0.5,
cchPriorBeta = 2,
cchPriorS = 0,
covariates = list(),
dependent = "",
descriptives = FALSE,
effectsType = "allModels",
factors = list(),
gPriorAlpha = 3,
inclusionProbabilitiesPlot = FALSE,
logPosteriorOddsPlot = FALSE,
marginalPosteriorPlot = FALSE,
modelComplexityPlot = FALSE,
modelPrior = "betaBinomial",
modelProbabilitiesPlot = FALSE,
modelTerms = list(),
modelsShown = "limited",
numModelsShown = 10,
numberOfModels = 0,
numericalAccuracy = 1000,
plotHeight = 320,
plotWidth = 480,
posteriorSummaryPlot = FALSE,
posteriorSummaryPlotCiLevel = 0.95,
posteriorSummaryPlotWithoutIntercept = FALSE,
posteriorSummaryTable = FALSE,
priorRegressionCoefficients = "cch",
qqPlot = FALSE,
residualSdsSavedToData = FALSE,
residualSdsSavedToDataColumn = "",
residualsSavedToData = FALSE,
residualsSavedToDataColumn = "",
residualsVsFittedPlot = FALSE,
samples = 0,
samplingMethod = "bas",
seed = 1,
setSeed = FALSE,
summaryType = "averaged",
weights = "",
wilsonParamLambda = 1) {
defaultArgCalls <- formals(jaspRegression::RegressionLogisticBayesian)
defaultArgs <- lapply(defaultArgCalls, eval)
options <- as.list(match.call())[-1L]
options <- lapply(options, eval)
defaults <- setdiff(names(defaultArgs), names(options))
options[defaults] <- defaultArgs[defaults]
options[["data"]] <- NULL
options[["version"]] <- NULL
optionsWithFormula <- c("covariates", "dependent", "factors", "modelTerms", "summaryType", "weights")
for (name in optionsWithFormula) {
if ((name %in% optionsWithFormula) && inherits(options[[name]], "formula")) options[[name]] = jaspBase::jaspFormula(options[[name]], data) }
return(jaspBase::runWrappedAnalysis("jaspRegression::RegressionLogisticBayesian", data, options, version))
}
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