remove new function code

R/doeAnalysis.R

@@ -833,114 +833,6 @@ get_levels <- function(var, num_levels, dataset) {
   plot$plotObject <- p
 }
 
-# .doeAnalysisPlotEffectNormalDistribution <- function(jaspResults, options, blocks, covariates, ready) {
-#   if (!is.null(jaspResults[["normalEffectsPlot"]]) || !options[["normalEffectsPlot"]]) {
-#     return()
-#   }
-#   plot <- createJaspPlot(title = gettext("Normal Plot of Standardized Effects"), width = 600, height = 400)
-#   plot$dependOn(options = c("normalEffectsPlot", "tableAlias", .doeAnalysisBaseDependencies()))
-#   plot$position <- 11
-#   jaspResults[["normalEffectsPlot"]] <- plot
-#   if (!ready || is.null(jaspResults[["doeResult"]]) || jaspResults$getError()) {
-#     return()
-#   }
-#   result <- if (options[["codeFactors"]]) jaspResults[["doeResultCoded"]]$object[["regression"]] else jaspResults[["doeResult"]]$object[["regression"]]
-#   fac <- if (options[["tableAlias"]]) result[["coefficients"]][["termsAliased"]][-1] else result[["coefficients"]][["terms"]][-1]
-#   coefDf <- data.frame(result[["objectSummary"]]$coefficients)
-#   tDf <- data.frame("tValue" = coefDf[["t.value"]],
-#                     terms = result[["coefficients"]][["terms"]])
-#
-#   # Do not include intercept, covariates and blocks in normal effects plot
-#   tDf <- tDf[-1, ] # remove intercept
-#   if (length(blocks) > 0 && !identical(blocks, "")) {
-#     tDf <- tDf[!grepl(blocks, tDf$terms),]
-#     fac <- if (options[["tableAlias"]]) fac[!grepl("BLK", fac)] else fac[!grepl(blocks, fac)]
-#   }
-#   if (length(covariates) > 0 && !identical(covariates, "")) {
-#     tDf <- tDf[!tDf$terms %in% unlist(covariates), ] # remove the covariate(s)
-#     fac <- if (options[["tableAlias"]]) fac[!grepl("COV", fac)] else fac[!fac %in% unlist(covariates)]
-#   }
-#
-#
-#   #library(ggplot2)
-#   data <- c(1.79, -0.52, 3.08, 0.9, -0.79, -1.58)
-#   x <- data
-#
-#
-#   ticks <- ticks
-#
-#   x <- x[order(x)]
-#   n <- length(x)
-#   i <- rank(x)
-#   p <- (i - 0.3) / (n + 0.4)
-#
-#
-#   df <- data.frame(x = x, p = p)
-#   # Create the ggplot with probit transformation
-#   ggplot(df, aes(x = x, y = p)) +
-#     stat_function(fun = pnorm, colour = "blue", size = 1) +  # Reference line using the pnorm function
-#     geom_point(color = "red", size = 3) +
-#     scale_y_continuous(trans = 'probit', labels = scales::percent_format(accuracy = 1), breaks = ticks) +
-#     labs(x = "Value", y = "Probit of Percentile",
-#          title = "Comparison of Data to Standard Normal Distribution Percentiles") +
-#     theme_minimal() +
-#     xlim(-3, 3.5)  # Optional: adjust the x-limits to show more of the tails if needed
-#
-#
-#
-#
-#   c(0.1, 1, 5, seq(10, 90, 10), 95, 99, 99.9)
-#   tDf
-#
-#   theoretical_fit <- qnorm((1:nrow(tDf))/(nrow(tDf)+1))
-#   tDf <- dplyr::arrange(tDf, tValue)
-#
-#   # Plotting using ggplot2
-#   ggplot2::ggplot(tDf, ggplot2::aes(sample = tValue)) +
-#     #ggplot2::geom_point(ggplot2::aes(y = prob), color = "blue") +  # plot actual data points with their percentiles
-#     ggplot2::geom_line(ggplot2::aes(x = theoretical_fit, y = sort(tValue)), color = "red") +  # add theoretical fit line
-#     ggplot2::labs(title = "Normal Probability Plot of the Effects",
-#          x = "Theoretical Quantiles",
-#          y = "Percentiles") +
-#     ggplot2::theme_minimal() +
-#     ggplot2::scale_y_continuous(labels = scales::percent)  # format y-axis as percent
-#
-#   tDf$percentile <- pnorm(tDf$tValue) * 100
-#   # Plotting
-#   ggplot(tDf, aes(x = tValue)) +
-#     geom_point(aes(y = percentile), color = "blue") +  # Actual data points
-#     geom_line(data = data.frame(t = sort(tDf$tValue)), aes(x = t, y = pnorm(t) * 100), color = "red") +  # Theoretical fit line
-#     labs(title = "Normal Probability Plot of the Effects",
-#          x = "T-Values",
-#          y = "Percentiles") +
-#     theme_minimal() +
-#     scale_y_continuous(limits = c(0, 100)) +
-#     scale_x_continuous(limits = c(-3, 3))# y-axis as percent
-#
-#
-#
-#
-#
-#
-#   df <- result[["objectSummary"]]$df[2]
-#   crit <- abs(qt(0.025, df))
-#   fac_t <- cbind.data.frame(fac, t)
-#   fac_t <- cbind(fac_t[order(fac_t$t), ], y = seq_len(length(t)))
-#   xBreaks <- jaspGraphs::getPrettyAxisBreaks(c(0, t, crit))
-#   critLabelDf <- data.frame(x = 0, y = crit, label = sprintf("t = %.2f", crit))
-#   p <- ggplot2::ggplot(data = fac_t, mapping = ggplot2::aes(y = t, x = y)) +
-#     ggplot2::geom_bar(stat = "identity") +
-#     ggplot2::geom_hline(yintercept = crit, linetype = "dashed", color = "red") +
-#     ggplot2::geom_label(data = critLabelDf, mapping = ggplot2::aes(x = x, y = y, label = label), col = "red", size = 5) +
-#     ggplot2::scale_x_continuous(name = gettext("Term"), breaks = fac_t$y, labels = fac_t$fac) +
-#     ggplot2::scale_y_continuous(name =
-#                                   gettext("Standardized Effect"), breaks = xBreaks, limits = range(xBreaks)) +
-#     ggplot2::coord_flip() +
-#     jaspGraphs::geom_rangeframe() +
-#     jaspGraphs::themeJaspRaw()
-#   plot$plotObject <- p
-# }
-
 .doeAnalysisPlotQQResiduals <- function(jaspResults, options, ready) {
   if (!is.null(jaspResults[["plotNorm"]]) || !options[["plotNorm"]]) {
     return()