#' @tool_code ST_LINEAR_REG
#' @name 线性回归
#' @version 1.0.0
#' @description 连续型结局变量的多因素线性回归分析
#' @author SSA-Pro Team

library(glue)
library(ggplot2)
library(base64enc)

run_analysis <- function(input) {
  # ===== 初始化 =====
  logs <- c()
  log_add <- function(msg) { logs <<- c(logs, paste0("[", Sys.time(), "] ", msg)) }

  on.exit({}, add = TRUE)

  # ===== 数据加载 =====
  log_add("开始加载输入数据")
  df <- tryCatch(
    load_input_data(input),
    error = function(e) {
      log_add(paste("数据加载失败:", e$message))
      return(NULL)
    }
  )

  if (is.null(df)) {
    return(make_error(ERROR_CODES$E100_INTERNAL_ERROR, details = "数据加载失败"))
  }
  log_add(glue("数据加载成功: {nrow(df)} 行, {ncol(df)} 列"))

  p <- input$params
  outcome_var <- p$outcome_var
  predictors <- p$predictors
  confounders <- p$confounders

  # ===== 参数校验 =====
  if (!(outcome_var %in% names(df))) {
    return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = outcome_var))
  }

  all_vars <- c(predictors, confounders)
  all_vars <- all_vars[!is.null(all_vars) & all_vars != ""]

  for (v in all_vars) {
    if (!(v %in% names(df))) {
      return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = v))
    }
  }

  if (length(predictors) == 0) {
    return(make_error(ERROR_CODES$E100_INTERNAL_ERROR, details = "至少需要一个预测变量"))
  }

  # ===== 数据清洗 =====
  original_rows <- nrow(df)
  vars_to_check <- c(outcome_var, all_vars)
  for (v in vars_to_check) {
    df <- df[!is.na(df[[v]]), ]
  }

  # 确保结局变量为数值型
  if (!is.numeric(df[[outcome_var]])) {
    df[[outcome_var]] <- as.numeric(as.character(df[[outcome_var]]))
    df <- df[!is.na(df[[outcome_var]]), ]
  }

  removed_rows <- original_rows - nrow(df)
  if (removed_rows > 0) {
    log_add(glue("数据清洗: 移除 {removed_rows} 行缺失值 (剩余 {nrow(df)} 行)"))
  }

  n_predictors <- length(all_vars)

  # ===== 护栏检查 =====
  guardrail_results <- list()
  warnings_list <- c()

  sample_check <- check_sample_size(nrow(df), min_required = n_predictors + 10, action = ACTION_BLOCK)
  guardrail_results <- c(guardrail_results, list(sample_check))
  log_add(glue("样本量: N = {nrow(df)}, 预测变量数 = {n_predictors}, {sample_check$reason}"))

  guardrail_status <- run_guardrail_chain(guardrail_results)

  if (guardrail_status$status == "blocked") {
    return(list(status = "blocked", message = guardrail_status$reason, trace_log = logs))
  }

  # ===== 构建模型公式 =====
  formula_str <- paste(outcome_var, "~", paste(all_vars, collapse = " + "))
  formula_obj <- as.formula(formula_str)
  log_add(glue("模型公式: {formula_str}"))

  # ===== 核心计算 =====
  log_add("拟合线性回归模型")

  model <- tryCatch({
    lm(formula_obj, data = df)
  }, error = function(e) {
    log_add(paste("模型拟合失败:", e$message))
    return(NULL)
  }, warning = function(w) {
    warnings_list <<- c(warnings_list, w$message)
    log_add(paste("模型警告:", w$message))
    invokeRestart("muffleWarning")
  })

  if (is.null(model)) {
    return(map_r_error("线性回归模型拟合失败"))
  }

  model_summary <- summary(model)

  # ===== 提取模型结果 =====
  coef_summary <- model_summary$coefficients
  coef_table <- data.frame(
    variable = rownames(coef_summary),
    estimate = coef_summary[, "Estimate"],
    std_error = coef_summary[, "Std. Error"],
    t_value = coef_summary[, "t value"],
    p_value = coef_summary[, "Pr(>|t|)"],
    stringsAsFactors = FALSE
  )

  # 95% 置信区间
  ci <- confint(model)
  coef_table$ci_lower <- ci[, 1]
  coef_table$ci_upper <- ci[, 2]

  coefficients_list <- lapply(1:nrow(coef_table), function(i) {
    row <- coef_table[i, ]
    list(
      variable = row$variable,
      estimate = round(row$estimate, 4),
      std_error = round(row$std_error, 4),
      t_value = round(row$t_value, 3),
      ci_lower = round(row$ci_lower, 4),
      ci_upper = round(row$ci_upper, 4),
      p_value = round(row$p_value, 4),
      p_value_fmt = format_p_value(row$p_value),
      significant = row$p_value < 0.05
    )
  })

  # ===== 模型拟合度 =====
  r_squared <- model_summary$r.squared
  adj_r_squared <- model_summary$adj.r.squared
  f_stat <- model_summary$fstatistic
  f_p_value <- if (!is.null(f_stat)) {
    pf(f_stat[1], f_stat[2], f_stat[3], lower.tail = FALSE)
  } else {
    NA
  }

  if (!is.null(f_stat)) {
    log_add(glue("R² = {round(r_squared, 4)}, Adj R² = {round(adj_r_squared, 4)}, F = {round(f_stat[1], 2)}, P = {round(f_p_value, 4)}"))
  } else {
    log_add(glue("R² = {round(r_squared, 4)}, Adj R² = {round(adj_r_squared, 4)}, F = NA"))
  }

  # ===== 残差诊断 =====
  residuals_vals <- residuals(model)
  fitted_vals <- fitted(model)

  # 残差正态性
  normality_p <- NA
  if (length(residuals_vals) >= 3 && length(residuals_vals) <= 5000) {
    normality_test <- shapiro.test(residuals_vals)
    normality_p <- normality_test$p.value
    if (normality_p < 0.05) {
      warnings_list <- c(warnings_list, glue("残差不满足正态性 (Shapiro-Wilk p = {round(normality_p, 4)})"))
    }
  }

  # ===== 共线性检测 (VIF) =====
  vif_results <- NULL
  if (length(all_vars) > 1) {
    tryCatch({
      if (requireNamespace("car", quietly = TRUE)) {
        vif_values <- car::vif(model)
        if (is.matrix(vif_values)) {
          vif_values <- vif_values[, "GVIF"]
        }
        vif_results <- lapply(names(vif_values), function(v) {
          list(variable = v, vif = round(vif_values[v], 2))
        })

        high_vif <- names(vif_values)[vif_values > 5]
        if (length(high_vif) > 0) {
          warnings_list <- c(warnings_list, paste("VIF > 5 的变量:", paste(high_vif, collapse = ", ")))
        }
      }
    }, error = function(e) {
      log_add(paste("VIF 计算失败:", e$message))
    })
  }

  # ===== 生成图表 =====
  log_add("生成诊断图")
  plot_base64 <- tryCatch({
    generate_regression_plots(model, outcome_var)
  }, error = function(e) {
    log_add(paste("图表生成失败:", e$message))
    NULL
  })

  # ===== 生成可复现代码 =====
  original_filename <- if (!is.null(input$original_filename) && nchar(input$original_filename) > 0) {
    input$original_filename
  } else {
    "data.csv"
  }

  reproducible_code <- glue('
# SSA-Pro 自动生成代码
# 工具: 线性回归
# 时间: {Sys.time()}
# ================================

# 数据准备
df <- read.csv("{original_filename}")

# 线性回归
model <- lm({formula_str}, data = df)
summary(model)

# 置信区间
confint(model)

# 残差诊断
par(mfrow = c(2, 2))
plot(model)

# VIF（需要 car 包）
# library(car)
# vif(model)
')

  # ===== 构建 report_blocks =====
  blocks <- list()

  # Block 1: 模型概况
  kv_model <- list(
    "模型公式" = formula_str,
    "观测数" = as.character(nrow(df)),
    "预测变量数" = as.character(n_predictors),
    "R²" = as.character(round(r_squared, 4)),
    "调整 R²" = as.character(round(adj_r_squared, 4))
  )
  if (!is.null(f_stat)) {
    kv_model[["F 统计量"]] <- as.character(round(f_stat[1], 2))
    kv_model[["模型 P 值"]] <- format_p_value(f_p_value)
  }
  if (!is.na(normality_p)) {
    kv_model[["残差正态性 (Shapiro P)"]] <- format_p_value(normality_p)
  }
  blocks[[length(blocks) + 1]] <- make_kv_block(kv_model, title = "模型概况")

  # Block 2: 回归系数表
  coef_headers <- c("变量", "系数 (B)", "标准误", "t 值", "95% CI", "P 值", "显著性")
  coef_rows <- lapply(coefficients_list, function(row) {
    ci_str <- sprintf("[%.4f, %.4f]", row$ci_lower, row$ci_upper)
    sig <- if (row$significant) "*" else ""
    c(row$variable, as.character(row$estimate), as.character(row$std_error),
      as.character(row$t_value), ci_str, row$p_value_fmt, sig)
  })
  blocks[[length(blocks) + 1]] <- make_table_block(coef_headers, coef_rows,
    title = "回归系数表", footnote = "* P < 0.05")

  # Block 3: VIF 表
  if (!is.null(vif_results) && length(vif_results) > 0) {
    vif_headers <- c("变量", "VIF")
    vif_rows <- lapply(vif_results, function(row) c(row$variable, as.character(row$vif)))
    blocks[[length(blocks) + 1]] <- make_table_block(vif_headers, vif_rows, title = "方差膨胀因子 (VIF)")
  }

  # Block 4: 诊断图
  if (!is.null(plot_base64)) {
    blocks[[length(blocks) + 1]] <- make_image_block(plot_base64,
      title = "回归诊断图", alt = "残差 vs 拟合值 + Q-Q 图")
  }

  # Block 5: 结论摘要
  sig_vars <- sapply(coefficients_list, function(r) {
    if (r$variable != "(Intercept)" && r$significant) r$variable else NULL
  })
  sig_vars <- unlist(sig_vars[!sapply(sig_vars, is.null)])

  model_sig <- if (!is.na(f_p_value) && f_p_value < 0.05) "整体具有统计学意义" else "整体不具有统计学意义"
  f_display <- if (!is.null(f_stat)) round(f_stat[1], 2) else "NA"
  p_display <- if (!is.na(f_p_value)) format_p_value(f_p_value) else "NA"
  conclusion <- glue("线性回归模型{model_sig}（F = {f_display}，P {p_display}）。模型解释了因变量 {round(r_squared * 100, 1)}% 的变异（R² = {round(r_squared, 4)}，调整 R² = {round(adj_r_squared, 4)}）。")

  if (length(sig_vars) > 0) {
    conclusion <- paste0(conclusion, glue("\n\n在 α = 0.05 水平下，以下预测变量具有统计学意义：**{paste(sig_vars, collapse = '**、**')}**。"))
  } else {
    conclusion <- paste0(conclusion, "\n\n在 α = 0.05 水平下，无预测变量达到统计学意义。")
  }

  if (length(warnings_list) > 0) {
    conclusion <- paste0(conclusion, "\n\n⚠️ 注意：", paste(warnings_list, collapse = "；"), "。")
  }

  blocks[[length(blocks) + 1]] <- make_markdown_block(conclusion, title = "结论摘要")

  # ===== 返回结果 =====
  log_add("分析完成")

  return(list(
    status = "success",
    message = "分析完成",
    warnings = if (length(warnings_list) > 0) warnings_list else NULL,
    results = list(
      method = "Multiple Linear Regression (OLS)",
      formula = formula_str,
      n_observations = nrow(df),
      n_predictors = n_predictors,
      coefficients = coefficients_list,
      model_fit = list(
        r_squared = jsonlite::unbox(round(r_squared, 4)),
        adj_r_squared = jsonlite::unbox(round(adj_r_squared, 4)),
        f_statistic = if (!is.null(f_stat)) jsonlite::unbox(round(f_stat[1], 2)) else NULL,
        f_df = if (!is.null(f_stat)) as.numeric(f_stat[2:3]) else NULL,
        f_p_value = if (!is.na(f_p_value)) jsonlite::unbox(round(f_p_value, 4)) else NULL,
        f_p_value_fmt = if (!is.na(f_p_value)) format_p_value(f_p_value) else NULL
      ),
      diagnostics = list(
        residual_normality_p = if (!is.na(normality_p)) jsonlite::unbox(round(normality_p, 4)) else NULL
      ),
      vif = vif_results
    ),
    report_blocks = blocks,
    plots = if (!is.null(plot_base64)) list(plot_base64) else list(),
    trace_log = logs,
    reproducible_code = as.character(reproducible_code)
  ))
}

# 辅助函数：回归诊断图（残差 vs 拟合值 + Q-Q 图 拼接）
generate_regression_plots <- function(model, outcome_var) {
  diag_df <- data.frame(
    fitted = fitted(model),
    residuals = residuals(model),
    std_residuals = rstandard(model)
  )

  # 残差 vs 拟合值
  p1 <- ggplot(diag_df, aes(x = fitted, y = residuals)) +
    geom_point(alpha = 0.5, color = "#3b82f6") +
    geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
    geom_smooth(method = "loess", se = FALSE, color = "orange", linewidth = 0.8) +
    theme_minimal() +
    labs(title = "Residuals vs Fitted", x = "Fitted values", y = "Residuals")

  # Q-Q 图
  p2 <- ggplot(diag_df, aes(sample = std_residuals)) +
    stat_qq(alpha = 0.5, color = "#3b82f6") +
    stat_qq_line(color = "red", linetype = "dashed") +
    theme_minimal() +
    labs(title = "Normal Q-Q Plot", x = "Theoretical Quantiles", y = "Standardized Residuals")

  # 拼图
  if (requireNamespace("gridExtra", quietly = TRUE)) {
    combined <- gridExtra::arrangeGrob(p1, p2, ncol = 2)
    tmp_file <- tempfile(fileext = ".png")
    ggsave(tmp_file, combined, width = 12, height = 5, dpi = 100)
  } else {
    tmp_file <- tempfile(fileext = ".png")
    ggsave(tmp_file, p1, width = 7, height = 5, dpi = 100)
  }

  base64_str <- base64encode(tmp_file)
  unlink(tmp_file)

  return(paste0("data:image/png;base64,", base64_str))
}