R/metadichotomousmodel.b.R

In kylehamilton/MAJOR: MAJOR: Meta-Analysis for JAMOVI

# This file is a generated template, your changes will not be overwritten

metaDichotomousModelClass <-
  if (requireNamespace('jmvcore'))
    R6::R6Class(
      "metaDichotomousModelClass",
      inherit = metaDichotomousModelBase,
      private = list(
        .run = function() {
          ai <- self$options$ai
          # bi <- self$options$bi
          ci <- self$options$ci
          # di <- self$options$di
          n1i <- self$options$n1i
          n2i <- self$options$n2i
          moderator <- self$options$moderatorcor
          slab <- self$options$slab
          #includemods <- self$options$includemods
          addcred <- self$options$addcred
          addfit <- self$options$addfit
          showweights <- self$options$showweights
          level <- self$options$level
          fsntype <- self$options$fsntype
          method2 <- self$options$methodmetamdms
          mdmseasure <- self$options$mdmsmeasure
          yaxis <- self$options$yaxis
          steps <- self$options$steps
          pchForest <- self$options$pchForest
          table <- self$results$textRICH
          moderatorType <- self$options$moderatorType
          backTransform <- self$options$showBackTransform
          modelBackTransform <- self$results$modelBackTransform
          
          
          ready <- TRUE
          if (is.null(self$options$ai) ||
              is.null(self$options$n1i) ||
              is.null(self$options$ci) || is.null(self$options$n2i) == TRUE) {
            ready <- FALSE
            # I really need to think of a better error message this is a place holder until I figure something out
            jmvcore::reject(
              "Incidents, Sample Size, and Study Label fields must be populated to run analysis",
              code = ''
            )
          }

          #   ready <- FALSE
          #   # I really need to think of a better error message this is a place holder until I figure something out
          #   jmvcore::reject(
          #     "Incidents, Sample Size, and Study Label fields must be populated to run analysis",
          #     code = ''
          #   )
          # }
          if (is.null(self$options$slab) == TRUE) {
            ready <- FALSE
            # I really need to think of a better error message this is a place holder until I figure something out
            jmvcore::reject("Study Label fields must be populated to run analysis", code =
                              '')
          }
          
          if (ready == TRUE) {
              if ((self$options$moderatorType) == "NON")
                if (is.null(self$options$moderatorcor) == FALSE) {
                  ready <- FALSE
                  # I really need to think of a better error message this is a place holder until I figure something out
                  jmvcore::reject("Must Remove Moderator Variable", code =
                                    '')
                }
                data <-
                  data.frame(
                    ai = self$data[[self$options$ai]],
                   # bi = self$data[[self$options$bi]],
                    ci = self$data[[self$options$ci]],
                   # di = self$data[[self$options$di]],
                    n1i = self$data[[self$options$n1i]],
                    n2i = self$data[[self$options$n2i]],
                    slab = self$data[[self$options$slab]]
                  )
              data[[ai]] <- jmvcore::toNumeric(data[[ai]])
             # data[[bi]] <- jmvcore::toNumeric(data[[bi]])
              data[[ci]] <- jmvcore::toNumeric(data[[ci]])
             # data[[di]] <- jmvcore::toNumeric(data[[di]])
              data[[n1i]] <- jmvcore::toNumeric(data[[n1i]])
              data[[n2i]] <- jmvcore::toNumeric(data[[n2i]])
            data$checkG1 <- 0
            data$checkG2 <- 0
            data$checkG1 <- data$n1i - data$ai
            data$checkG2 <- data$n2i - data$ci
              if (data$checkG1 < 0) {
                #ready <- FALSE
                jmvcore::reject(
                  "Number of incidents in the experimental group is higher than the associated sample size group, check your data ",
                  code = ''
                )
              }
              if (data$checkG2 < 0) {
                #ready <- FALSE
                jmvcore::reject(
                  "Number of incidents in the control group is higher than the associated sample size group, check your data ",
                  code = ''
                )
              }
             }
            res <-
              metafor::rma(
                ai = ai,
                # bi = bi,
                ci = ci,
                # di = di,
                n1i = n1i,
                n2i = n2i,
                method = method2,
                measure = mdmseasure,
                data = data,
                slab = slab,
                level = level
              )
            if (is.list(res) == FALSE) {
              jmvcore::reject("Check sample size and incident data", code = '')
            }
          
        if (self$options$moderatorType == "CON") {
          if (is.null(self$options$moderatorcor) == TRUE) {
            ready <- FALSE
            # I really need to think of a better error message this is a place holder until I figure something out
            jmvcore::reject("Must Supply a Moderator Variable", code =
                              '')
          }
            data <-
              data.frame(
                ai = self$data[[self$options$ai]],
                n1i = self$data[[self$options$n1i]],
                ci = self$data[[self$options$ci]],
                n2i = self$data[[self$options$n2i]],
                moderator = self$data[[self$options$moderatorcor]],
                slab = self$data[[self$options$slab]]
              )
            data[[ai]] <- jmvcore::toNumeric(data[[ai]])
            # data[[bi]] <- jmvcore::toNumeric(data[[bi]])
            data[[ci]] <- jmvcore::toNumeric(data[[ci]])
            # data[[di]] <- jmvcore::toNumeric(data[[di]])
            data[[n1i]] <- jmvcore::toNumeric(data[[n1i]])
            data[[n2i]] <- jmvcore::toNumeric(data[[n2i]])
            data[[moderator]] <- jmvcore::toNumeric(data[[moderator]])
            data$checkG1 <- 0
            data$checkG2 <- 0
            data$checkG1 <- data$n1i - data$ai
            data$checkG2 <- data$n2i - data$ci
            if (data$checkG1 < 0) {
              #ready <- FALSE
              jmvcore::reject(
                "Number of incidents in the experimental group is higher than the associated sample size group, check your data ",
                code = ''
              )
            }
            if (data$checkG2 < 0) {
              #ready <- FALSE
              jmvcore::reject(
                "Number of incidents in the control group is higher than the associated sample size group, check your data ",
                code = ''
              )
            }
            res <-
              metafor::rma(
                ai = ai,
                #bi = bi,
                ci = ci,
                #di = di,
                n1i = n1i,
                n2i = n2i,
                mods = moderator,
                method = method2,
                measure = mdmseasure,
                data = data,
                slab = slab,
                level = level
              )}
        if (self$options$moderatorType == "CAT") {
          if (is.null(self$options$moderatorcor) == TRUE) {
            ready <- FALSE
            # I really need to think of a better error message this is a place holder until I figure something out
            jmvcore::reject("Must Supply a Moderator Variable", code =
                              '')
          }
            data <-
              data.frame(
                ai = self$data[[self$options$ai]],
                n1i = self$data[[self$options$n1i]],
                ci = self$data[[self$options$ci]],
                n2i = self$data[[self$options$n2i]],
                moderator = self$data[[self$options$moderatorcor]],
                slab = self$data[[self$options$slab]]
              )
            data[[ai]] <- jmvcore::toNumeric(data[[ai]])
           # data[[bi]] <- jmvcore::toNumeric(data[[bi]])
            data[[ci]] <- jmvcore::toNumeric(data[[ci]])
           # data[[di]] <- jmvcore::toNumeric(data[[di]])
            data[[n1i]] <- jmvcore::toNumeric(data[[n1i]])
            data[[n2i]] <- jmvcore::toNumeric(data[[n2i]])
            data[[moderator]] <- jmvcore::toNumeric(data[[moderator]])
            data$checkG1 <- 0
            data$checkG2 <- 0
            data$checkG1 <- data$n1i - data$ai
            data$checkG2 <- data$n2i - data$ci
            if (data$checkG1 < 0) {
              #ready <- FALSE
              jmvcore::reject(
                "Number of incidents in the experimental group is higher than the associated sample size group, check your data ",
                code = ''
              )
            }
            if (data$checkG2 < 0) {
              #ready <- FALSE
              jmvcore::reject(
                "Number of incidents in the control group is higher than the associated sample size group, check your data ",
                code = ''
              )
            }
            res <-
              metafor::rma(
                ai = ai,
               # bi = bi,
                ci = ci,
               # di = di,
                n1i = n1i,
                n2i = n2i,
                mods = ~ factor(moderator),
                method = method2,
                measure = mdmseasure,
                data = data,
                slab = slab,
                level = level
              )
          }
          if (is.list(res) == FALSE) {
            jmvcore::reject("Check sample size and incident data", code = '')
          }
          
         
        
            # else if (message == 'missing value where TRUE/FALSE needed')
            #   message <- 'One or both variables contain infinite values'
            # if (is.character(res)== TRUE)
            #   jmvcore::reject("Check sample size and incident data", code='')
            
            summaryOutputText <- self$results$summaryOutputText
            if (self$options$moderatorType == "NON") {
              
              textReport <- reporterMAJOR(res)
              outputTextSummary <- textReport[[1]]
              
            }
            
            if (self$options$moderatorType == "CAT") {
              
              outputTextSummary <- "Text reporting does not currently work with moderators"
              
            }
            
            if (self$options$moderatorType == "CON") {
              
              outputTextSummary <- "Text reporting does not currently work with moderators"
              
            }
            
            
            summaryOutputText$setContent(outputTextSummary)
            
            ### Second part
            summaryOutputText2 <- self$results$summaryOutputText2
            
            if (self$options$moderatorType == "NON") {
              
              outputTextSummary2 <- textReport[[2]]
              
            }
            if (self$options$moderatorType == "CAT" ||
                self$options$moderatorType == "CON") {
              
              outputTextSummary2 <- " "
            }
            
            
            summaryOutputText2$setContent(outputTextSummary2)       
            
            #Pub Bias
            failsafePB <-
              metafor::fsn(yi = res$yi,
                           vi = res$vi,
                           type = fsntype)
            ranktestPB <- metafor::ranktest(res)
            regtestPB <- metafor::regtest(res)
            
            
            fsnRICH <- self$results$pubBias$fsnRICH
            
            fsnRICH$setRow(
              rowNo = 1,
              values = list(failSafeNumber = failsafePB$fsnum[1],
                            p = failsafePB$pval[1])
            )
            fsnTitle <-
              paste("Fail-Safe N Analysis (File Drawer Analysis)")
            fsnNote <-
              paste("Fail-safe N Calculation Using the ",
                    fsntype,
                    " Approach",
                    sep = "")
            fsnRICH$setTitle(title = fsnTitle)
            fsnRICH$setNote("fsnNoteTable", fsnNote)
            
            rankRICH <- self$results$pubBias$rankRICH
            rankRICH$setRow(
              rowNo = 1,
              values = list(rankTau = ranktestPB$tau[1],
                            p = ranktestPB$pval[1])
            )
            
            regRICH <- self$results$pubBias$regRICH
            regRICH$setRow(rowNo = 1,
                           values = list(Z = regtestPB$zval[1],
                                         p = regtestPB$pval[1]))
            
            # # Extracting the effect sizes and sampling variances:
            # effect <- res$yi
            # v <- res$vi
            #
            # # The weight-function model with no mean model:
            # wfRES <- weightr::weightfunct(effect, v)
            #
            #
            # self$results$weightFunctionModel$setContent(wfRES)
            
            #Model Fit
            modelFitRICH <- self$results$modelFitRICH
            modelFitRICH$setRow(
              rowNo = 1,
              values = list(
                label = "Maximum-Likelihood",
                loglikelihood = res$fit.stats[1, 1],
                deviance = res$fit.stats[2, 1],
                AIC = res$fit.stats[3, 1],
                BIC = res$fit.stats[4, 1],
                AICc = res$fit.stats[5, 1]
              )
            )
            
            
            modelFitRICH$setRow(
              rowNo = 2,
              values = list(
                label = "Restricted Maximum-Likelihood",
                loglikelihood = res$fit.stats[1, 2],
                deviance = res$fit.stats[2, 2],
                AIC = res$fit.stats[3, 2],
                BIC = res$fit.stats[4, 2],
                AICc = res$fit.stats[5, 2]
              )
            )
            
            #fit statistics and information criteria
            #Show if checked, hide if unchecked
            if (self$options$showModelFit == TRUE) {
              modelFitRICH$setVisible(visible = TRUE)
            } else {
              modelFitRICH$setVisible(visible = FALSE)
            }
            
            #Pub Bias Connections
            #self$results$pubBias$fsn$setContent(failsafePB)
            #self$results$pubBias$rank$setContent(ranktestPB)
            #self$results$pubBias$reg$setContent(regtestPB)
            
            #Data Prep: Results Table
            CILB <- round(res$ci.lb[1], 3)
            CIUB <- round(res$ci.ub[1], 3)
            ciLBUB <- paste(CILB, "-", CIUB)
            
            
            table$setRow(
              rowNo = 1,
              values = list(
                Intercept = "Intercept",
                Estimate = as.numeric(res$b[1]),
                se = res$se[1],
                CILow = res$ci.lb[1],
                CIHigh = res$ci.ub[1],
                p = res$pval[1],
                Z = res$zval[1],
                k = res$k
              )
            )
            
            if (self$options$methodmetamdms == "DL") {
              tau2EstimatorName = "DerSimonian-Laird"
            } else if (self$options$methodmetamdms == "HE") {
              tau2EstimatorName = "Hedges"
            } else if (self$options$methodmetamdms == "HS") {
              tau2EstimatorName = "Hunter-Schmidt"
            } else if (self$options$methodmetamdms == "SJ") {
              tau2EstimatorName = "Sidik-Jonkman"
            } else if (self$options$methodmetamdms == "ML") {
              tau2EstimatorName = "Maximum-Likelihood"
            } else if (self$options$methodmetamdms == "REML") {
              tau2EstimatorName = "Restricted Maximum-Likelihood"
            } else if (self$options$methodmetamdms == "EB") {
              tau2EstimatorName = "Empirical Bayes"
            } else if (self$options$methodmetamdms == "PM") {
              tau2EstimatorName = "Paule-Mandel"
            }
            
            if (is.null(self$options$moderatorcor) == FALSE) {
              titleMix <- paste("Mixed-Effects Model (k = ", res$k, ")", sep = "")
              titleMixNote <-
                paste("Tau\u00B2 Estimator: ", tau2EstimatorName, sep = "")
              table$setTitle(title = titleMix)
              table$setNote("mixnote", titleMixNote)
            } else if (self$options$methodmetamdms == "FE") {
              titleFix <- paste("Fixed-Effects Model (k = ", res$k, ")", sep = "")
              table$setTitle(title = titleFix)
              
            } else {
              titleRan <- paste("Random-Effects Model (k = ", res$k, ")", sep = "")
              titleRanNote <-
                paste("Tau\u00B2 Estimator: ", tau2EstimatorName, sep = "")
              table$setTitle(title = titleRan)
              table$setNote("rannote", titleRanNote)
            }
            
            if (is.null(self$options$moderatorcor) == FALSE) {
              modCILB <- round(res$ci.lb[2], 3)
              modCIUB <- round(res$ci.ub[2], 3)
              
              table$setRow(
                rowNo = 2,
                values = list(
                  Intercept = "Moderator",
                  Estimate = as.numeric(res$b[2]),
                  se = res$se[2],
                  CILow = res$ci.lb[2],
                  CIHigh = res$ci.ub[2],
                  p = res$pval[2],
                  Z = res$zval[2],
                  k = res$k
                )
              )
              
            } else {
              table$setRow(
                rowNo = 2,
                values = list(
                  Intercept = " ",
                  Estimate = NULL,
                  se = NULL,
                  CILow = NULL,
                  CIHigh = NULL,
                  p = NULL,
                  Z = NULL,
                  k = NULL
                )
              )
            }
            
            #Data Prep: Heterogeneity Stats
            tauSquared <- round(res$tau2, 4)
            tauSquaredSE <- round(res$se.tau2, 4)
            tauSqCombind <- paste(tauSquared, "(SE=", tauSquaredSE, ")")
            tauOnly <- round(sqrt(res$tau2), 4)
            ISquStat <- paste(round(res$I2, 2), "%", sep = "")
            HSquStat <- round(res$H2, 4)
            
            if (is.null(self$options$moderatorcor) == FALSE) {
              RSquStat <- paste(round(res$R2, 2), "%", sep = "")
            } else {
              RSquStat <- NULL
            }
            
            #Data Prep: Heterogeneity Test
            QTestStatDF <- round(res$k - 1, 4)
            
            #Heterogeneity Stats annd Test Table
            tableTauSqaured <- self$results$tableTauSqaured
            tableTauSqaured$setRow(
              rowNo = 1,
              values = list(
                tauSqComb = tauSqCombind,
                tauSQRT = tauOnly,
                ISqu = ISquStat,
                HSqu = HSquStat,
                RSqu = RSquStat,
                QallDF = QTestStatDF,
                Qall = res$QE,
                QallPval = res$QEp
              )
            )
            
            
            #Back Tranform from log odds ratio to odds ratio
            #Show if checked, hide if unchecked
            if (self$options$showBackTransform == TRUE &&
                self$options$mdmsmeasure == "OR" &&
                self$options$moderatorType == "NON") {
              modelBackTransform$setVisible(visible = TRUE)
            } else {
              modelBackTransform$setVisible(visible = FALSE)
            }
            
            
            
            if (self$options$moderatorType == "NON") {
              #Data Prep: Backtransform Stats
              resBackTransForm <- predict(res, transf = exp, digits = 2)
              resPred <- round(resBackTransForm$pred, 4)
              resCILB <- round(resBackTransForm$ci.lb, 4)
              resCIUB <- round(resBackTransForm$ci.ub, 4)
              
              #Heterogeneity Stats annd Test Table
              modelBackTransform <- self$results$modelBackTransform
              modelBackTransform$setRow(
                rowNo = 1,
                values = list(
                  backTransformOddsRatio = resPred,
                  backTransformCILow = resCILB,
                  backTransformCIHigh = resCIUB
                )
              )
            }
            # `self$data` contains the data
            # `self$options` contains the options
            # `self$results` contains the results object (to populate)
            
            # image <- self$results$plot
            # imageFUN <- self$results$funplot
            # 
            # image$setState(res)
            # imageFUN$setState(res)
            
            #Forest Plots
            image <- self$results$plot
            
            forestSmall <- self$results$plot
            forestMedium <- self$results$plotMed
            forestLarge <- self$results$plotLarge
            forestSmallWide <- self$results$plotSmallWide
            forestMediumWide <- self$results$plotMedWide
            forestLargeWide <- self$results$plotLargeWide
            
            forestSmall$setState(res)
            forestMedium$setState(res)
            forestLarge$setState(res)
            forestSmallWide$setState(res)
            forestMediumWide$setState(res)
            forestLargeWide$setState(res)
            
            ## Funnel
            imageFUN <- self$results$funplot
            
            funPlot <- self$results$funplot
            funPlotMed <- self$results$funplotMed
            funPlotLarge <- self$results$funplotLarge
            
            funPlot$setState(res)
            funPlotMed$setState(res)
            funPlotLarge$setState(res)
            
            #Forest Plot Size
            if (self$options$forestPlotSize == "SMALL") {
              forestSmall$setVisible(visible = TRUE)
              forestMedium$setVisible(visible = FALSE)
              forestLarge$setVisible(visible = FALSE)
              forestSmallWide$setVisible(visible = FALSE)
              forestMediumWide$setVisible(visible = FALSE)
              forestLargeWide$setVisible(visible = FALSE)
            }
            if (self$options$forestPlotSize == "MEDIUM") {
              forestSmall$setVisible(visible = FALSE)
              forestMedium$setVisible(visible = TRUE)
              forestLarge$setVisible(visible = FALSE)
              forestSmallWide$setVisible(visible = FALSE)
              forestMediumWide$setVisible(visible = FALSE)
              forestLargeWide$setVisible(visible = FALSE)
            }
            if (self$options$forestPlotSize == "LARGE") {
              forestSmall$setVisible(visible = FALSE)
              forestMedium$setVisible(visible = FALSE)
              forestLarge$setVisible(visible = TRUE)
              forestSmallWide$setVisible(visible = FALSE)
              forestMediumWide$setVisible(visible = FALSE)
              forestLargeWide$setVisible(visible = FALSE)
            }
            if (self$options$forestPlotSize == "SMALLWIDE") {
              forestSmall$setVisible(visible = FALSE)
              forestMedium$setVisible(visible = FALSE)
              forestLarge$setVisible(visible = FALSE)
              forestSmallWide$setVisible(visible = TRUE)
              forestMediumWide$setVisible(visible = FALSE)
              forestLargeWide$setVisible(visible = FALSE)
            }
            if (self$options$forestPlotSize == "MEDIUMWIDE") {
              forestSmall$setVisible(visible = FALSE)
              forestMedium$setVisible(visible = FALSE)
              forestLarge$setVisible(visible = FALSE)
              forestSmallWide$setVisible(visible = FALSE)
              forestMediumWide$setVisible(visible = TRUE)
              forestLargeWide$setVisible(visible = FALSE)
            }
            if (self$options$forestPlotSize == "LARGEWIDE") {
              forestSmall$setVisible(visible = FALSE)
              forestMedium$setVisible(visible = FALSE)
              forestLarge$setVisible(visible = FALSE)
              forestSmallWide$setVisible(visible = FALSE)
              forestMediumWide$setVisible(visible = FALSE)
              forestLargeWide$setVisible(visible = TRUE)
            }
            
            #Funnel Plot Size
            if (self$options$funnelPlotSize == "SMALL") {
              funPlot$setVisible(visible = TRUE)
              funPlotMed$setVisible(visible = FALSE)
              funPlotLarge$setVisible(visible = FALSE)
            }
            if (self$options$funnelPlotSize == "MEDIUM") {
              funPlot$setVisible(visible = FALSE)
              funPlotMed$setVisible(visible = TRUE)
              funPlotLarge$setVisible(visible = FALSE)
            }
            if (self$options$funnelPlotSize == "LARGE") {
              funPlot$setVisible(visible = FALSE)
              funPlotMed$setVisible(visible = FALSE)
              funPlotLarge$setVisible(visible = TRUE)
            }
            
            # }}))
          #}
        },
        #Forest Plot Function
        .plot = function(image, ...) {
          # <-- the plot function
          plotData <- image$state
          #StudyID <- self$options$studylabels
          #yi <- self$options$yi
          #vi <- self$options$vi
          #res <- metafor::rma(yi=yi, vi=vi, data=self$data)
          addcred <- self$options$addcred
          addfit <- self$options$addfit
          level <- self$options$level
          showweights <- self$options$showweights
          xlab <- self$options$xAxisTitle
          order <- self$options$forestOrder
          steps <- self$options$steps
          pchForest <- self$options$pchForest
          pch <- as.numeric(pchForest)
          ready <- TRUE
          if (is.null(self$options$ai) ||
              is.null(self$options$n1i) ||
              is.null(self$options$ci) || is.null(self$options$n2i) == TRUE) {
            ready <- FALSE
          }
          if (is.null(image$state$yi) ||
              is.null(image$state$vi) == TRUE) {
            ready <- FALSE
          }
          if (ready == TRUE) {
            #plot <- metafor::forest(plotData$yi, plotData$vi, addcred=addcred, addfit=addfit)
            plot <-
              metafor::forest(
                plotData,
                addcred = addcred,
                addfit = addfit,
                level = level,
                showweights = showweights,
                xlab = xlab,
                order = order,
                steps = steps,
                pch = pch
              )
            plotMed <- plot
            plotLarge <- plot
            plotSmallWide <- plot
            plotMedWide <- plot
            plotLargeWide <- plot
            
            print(plot)
            print(plotMed)
            print(plotLarge)
            print(plotSmallWide)
            print(plotMedWide)
            print(plotLargeWide)
            TRUE
          }
        },
        # Funnel Plot Function
        .funplot = function(imageFUN, ...) {
          # <-- the plot function
          plotDataFUN <- imageFUN$state
          yaxis <- self$options$yaxis
          yaxisInv <- self$options$yaxisInv
          enhancePlot <- self$options$enhanceFunnel
          ready <- TRUE
          if (is.null(self$options$ai) ||
              is.null(self$options$n1i) ||
              is.null(self$options$ci) || is.null(self$options$n2i) == TRUE) {
            ready <- FALSE
          }
          if (is.null(imageFUN$state$yi) ||
              is.null(imageFUN$state$vi) == TRUE) {
            ready <- FALSE
          }
          if (ready == TRUE) {
            if (self$options$yaxisInv == TRUE) {
              if (self$options$enhanceFunnel == TRUE) {
                yaxisTrans <- paste(yaxis, "nv", sep = "")
                plotFUN <-
                  metafor::funnel(
                    plotDataFUN,
                    yaxis = yaxisTrans,
                    level = c(90, 95, 99),
                    shade = c("white", "gray", "darkgray")
                  )
              } else {
                yaxisTrans <- paste(yaxis, "nv", sep = "")
                plotFUN <-
                  metafor::funnel(plotDataFUN, yaxis = yaxisTrans)
              }
              
            } else {
              if (self$options$enhanceFunnel == TRUE) {
                plotFUN <-
                  metafor::funnel(
                    plotDataFUN,
                    yaxis = yaxis,
                    level = c(90, 95, 99),
                    shade = c("white", "gray", "darkgray")
                  )
              } else {
                plotFUN <- metafor::funnel(plotDataFUN, yaxis = yaxis)
                
              }
            }
            funplot <- plotFUN
            funplotMed <- plotFUN
            funplotLarge <- plotFUN
            
            print(funplot)
            print(funplotMed)
            print(funplotLarge)
            TRUE
          }
        }
      )
    )