ui_DIF_logistic <- tabPanel(
  "Logistic regression",
  tabsetPanel(
    # ** Summary ####
    tabPanel(
      "Summary",
      h3("Logistic regression"),
      p("The logistic regression method allows for detection of uniform and non-uniform DIF (Swaminathan & Rogers, 1990) by including
        a group-membership variable (uniform DIF) and its interaction with a matching criterion (non-uniform DIF) into a model
        for item \\(i\\) and by testing for significance of their effect."),
      h4("Method specification"),
      p(
        "Here you can change ", strong("type"), " of DIF to be tested and ", strong("parametrization"), "- either based on IRT
        models or classical intercept/slope. You can also select a ", strong("correction method"), " for multiple comparison and/or
        ", strong("item purification. "), "You can also select whether apply them in simple (correction applied after purification)
        or iterative (correction applied after each purification iteration)", strong("combination"), "(Hladka, Martinkova, & Magis,
        2023). Finally, you may also change the ", strong("Observed score."), " While matching on the standardized total score is
        typical, the upload of other observed scores is possible in the ", strong("Data. "), "section. Using a pre-test (standardized)
        total score as the observed score allows for testing a differential item functioning in change (DIF-C) to provide proofs of
        instructional sensitivity ",
        a("(Martinkova et al., 2020), ",
          href = "https://doi.org/10.1016/j.learninstruc.2019.101286",
          target = "_blank"
        ), "also see", code("Learning To Learn 9"), " toy dataset."
      ),
      fluidRow(
        column(
          3,
          radioButtons(
            inputId = "DIF_logistic_summary_type",
            label = "Type",
            choices = c(
              "\\(H_{0}\\): No DIF vs. \\(H_{1}\\): Any DIF" = "both",
              "\\(H_{0}\\): No DIF vs. \\(H_{1}\\): Uniform DIF" = "udif",
              "\\(H_{0}\\): Uniform DIF vs. \\(H_{1}\\): Non-uniform DIF" = "nudif"
            ),
            selected = "both"
          )
        ),
        column(
          2,
          selectInput(
            inputId = "DIF_logistic_summary_matching",
            label = "Observed score",
            choices = c(
              "Total score" = "score",
              "Standardized total score" = "zscore"
            ),
            selected = "zscore"
          )
        ),
        column(
          2,
          selectInput(
            inputId = "DIF_logistic_summary_parametrization",
            label = "Parametrization",
            choices = c(
              "Intercept/slope" = "classic",
              "IRT" = "irt"
            ),
            selected = "irt"
          )
        ),
        column(
          2,
          selectInput(
            inputId = "DIF_logistic_summary_correction",
            label = "Correction method",
            choices = c(
              "Benjamini-Hochberg" = "BH",
              "Benjamini-Yekutieli" = "BY",
              "Bonferroni" = "bonferroni",
              "Holm" = "holm",
              "Hochberg" = "hochberg",
              "Hommel" = "hommel",
              "None" = "none"
            ),
            selected = "none"
          ),
          checkboxInput(
            inputId = "DIF_logistic_summary_purification",
            label = "Item purification",
            value = FALSE
          ),
          selectInput(
            inputId = "DIF_logistic_summary_combination",
            label = "Combination",
            choices = c(
              "not applied" = "simple"
            ),
            selected = "not applied"
          )
        )
      ),
      h4("Equation"),
      p(
        "The probability that respondent ", strong("\\(p\\)"), " with the observed score ",
        uiOutput("DIF_logistic_summary_matching_text", inline = TRUE), " and the group membership variable ",
        strong("\\(G_p\\)"), " answers correctly item \\(i\\) is given by the following equation: "
      ),
      fluidRow(column(12, align = "center", uiOutput("DIF_logistic_summary_equation"))),
      h4("Summary table"),
      p("The summary table contains information about DIF test statistics \\(LR(\\chi^2)\\) based on a likelihood ratio test,
        the corresponding \\(p\\)-values considering selected adjustement, and the significance codes. Moreover, it offers the values of
        Nagelkerke's \\(R^2\\) with DIF effect size classifications. This table also provides estimated parameters for the best
        fitted model for each item, and their standard errors. "),
      uiOutput("DIF_logistic_summary_NA_warning"),
      strong(textOutput("DIF_logistic_summary_dif_items")),
      br(),
      fluidRow(column(12, align = "left", tableOutput("DIF_logistic_summary_coef"))),
      fluidRow(column(12, align = "left", uiOutput("DIF_logistic_summary_table_note"))),
      br(),
      fluidRow(column(2, downloadButton(outputId = "DIF_logistic_summary_table_download", label = "Download table"))),
      br(),
      h4("Purification process"),
      textOutput("DIF_logistic_summary_purification_info"),
      br(),
      # tags$head(tags$style("#DIF_logistic_summary_purification_table  {white-space: nowrap;  }")),
      fluidRow(column(12, align = "center", tableOutput("DIF_logistic_summary_purification_table"))),
      conditionalPanel(
        "input.DIF_logistic_summary_purification == 1",
        downloadButton(outputId = "DIF_logistic_summary_purification_table_download", label = "Download table"),
        br(),
        br()
      ),
      h4("Selected R code"),
      code(includeText("sc/dif/log.R"))
    ),
    # ** Items ####
    tabPanel("Items",
      value = "log_it",
      h3("Logistic regression"),
      p("The logistic regression method allows for detection of uniform and non-uniform DIF (Swaminathan & Rogers, 1990) by including
        a group-membership variable (uniform DIF) and its interaction with a matching criterion (non-uniform DIF) into a model
        for item \\(i\\) and by testing for significance of their effect."),
      h4("Method specification"),
      p(
        "Here you can change ", strong("type"), " of DIF to be tested and ", strong("parametrization"), "- either based on IRT
        models or classical intercept/slope. You can also select a ", strong("correction method"), " for multiple comparison and/or
        ", strong("item purification. "), "You can also select whether apply them in simple (correction applied after purification)
        or iterative (correction applied after each purification iteration)", strong("combination."), "Finally, you may also change
        the ", strong("Observed score."), " While matching on the standardized total score is typical, the upload of other observed
        scores is possible in the ", strong("Data "), "section. Using a pre-test (standardized) total score as the observed score
        allows for testing a differential item functioning in change (DIF-C) to provide proofs of instructional sensitivity ",
        a("(Martinkova et al., 2020), ",
          href = "https://doi.org/10.1016/j.learninstruc.2019.101286",
          target = "_blank"
        ), "also see", code("Learning To Learn 9"), " toy dataset. For a selected", strong("item"), "you can display a plot of its
        characteristic curves and a table of its estimated parameters with standard errors. "
      ),
      fluidRow(
        column(
          3,
          radioButtons(
            inputId = "DIF_logistic_items_type",
            label = "Type",
            choices = c(
              "\\(H_{0}\\): No DIF vs. \\(H_{1}\\): Any DIF" = "both",
              "\\(H_{0}\\): No DIF vs. \\(H_{1}\\): Uniform DIF" = "udif",
              "\\(H_{0}\\): Uniform DIF vs. \\(H_{1}\\): Non-uniform DIF" = "nudif"
            ),
            selected = "both"
          )
        ),
        column(
          2,
          selectInput(
            inputId = "DIF_logistic_items_matching",
            label = "Observed score",
            choices = c(
              "Total score" = "score",
              "Standardized total score" = "zscore"
            ),
            selected = "zscore"
          )
        ),
        column(
          2,
          selectInput(
            inputId = "DIF_logistic_items_parametrization",
            label = "Parametrization",
            choices = c(
              "Intercept/slope" = "classic",
              "IRT" = "irt"
            ),
            selected = "irt"
          )
        ),
        column(
          2,
          selectInput(
            inputId = "DIF_logistic_items_correction",
            label = "Correction method",
            choices = c(
              "Benjamini-Hochberg" = "BH",
              "Benjamini-Yekutieli" = "BY",
              "Bonferroni" = "bonferroni",
              "Holm" = "holm",
              "Hochberg" = "hochberg",
              "Hommel" = "hommel",
              "None" = "none"
            ),
            selected = "none"
          ),
          checkboxInput(
            inputId = "DIF_logistic_items_purification",
            label = "Item purification",
            value = FALSE
          ),
          selectInput(
            inputId = "DIF_logistic_items_combination",
            label = "Combination",
            choices = c(
              "not applied" = "simple"
            ),
            selected = "not applied"
          )
        ),
        column(
          2,
          sliderInput(
            inputId = "DIF_logistic_items",
            label = "Item",
            min = 1,
            value = 1,
            max = 10,
            step = 1,
            animate = animationOptions(interval = 1600)
          )
        )
      ),
      uiOutput("DIF_logistic_items_NA_warning"),
      h4("Plot with estimated DIF logistic curve"),
      p("Points represent a proportion of the correct answer (empirical probabilities) with respect to the observed score. Their size is determined
        by the count of respondents who achieved a given level of the observed score and who selected given option with
        respect to the group membership."),
      plotlyOutput("DIF_logistic_items_plot"),
      downloadButton("DIF_logistic_items_plot_download", label = "Download figure"),
      h4("Equation"),
      p(
        "The probability that respondent ", strong("\\(p\\)"), " with the observed score ",
        uiOutput("DIF_logistic_items_matching_text", inline = TRUE), " and the group membership variable ",
        strong("\\(G_p\\)"), " answers correctly item \\(i\\) is given by the following equation: "
      ),
      fluidRow(column(12, align = "center", uiOutput("DIF_logistic_items_equation"))),
      h4("Table of parameters"),
      p("This table summarizes estimated item parameters and their standard errors. "),
      fluidRow(column(12, align = "center", tableOutput("DIF_logistic_items_coef"))),
      br(),
      h4("Selected R code"),
      code(includeText("sc/dif/log_it.R"))
    )
  )
)
