feat(ssa): Complete Phase I-IV intelligent dialogue and tool system development
Phase I - Session Blackboard + READ Layer: - SessionBlackboardService with Postgres-Only cache - DataProfileService for data overview generation - PicoInferenceService for LLM-driven PICO extraction - Frontend DataContextCard and VariableDictionaryPanel - E2E tests: 31/31 passed Phase II - Conversation Layer LLM + Intent Router: - ConversationService with SSE streaming - IntentRouterService (rule-first + LLM fallback, 6 intents) - SystemPromptService with 6-segment dynamic assembly - TokenTruncationService for context management - ChatHandlerService as unified chat entry - Frontend SSAChatPane and useSSAChat hook - E2E tests: 38/38 passed Phase III - Method Consultation + AskUser Standardization: - ToolRegistryService with Repository Pattern - MethodConsultService with DecisionTable + LLM enhancement - AskUserService with global interrupt handling - Frontend AskUserCard component - E2E tests: 13/13 passed Phase IV - Dialogue-Driven Analysis + QPER Integration: - ToolOrchestratorService (plan/execute/report) - analysis_plan SSE event for WorkflowPlan transmission - Dual-channel confirmation (ask_user card + workspace button) - PICO as optional hint for LLM parsing - E2E tests: 25/25 passed R Statistics Service: - 5 new R tools: anova_one, baseline_table, fisher, linear_reg, wilcoxon - Enhanced guardrails and block helpers - Comprehensive test suite (run_all_tools_test.js) Documentation: - Updated system status document (v5.9) - Updated SSA module status and development plan (v1.8) Total E2E: 107/107 passed (Phase I: 31, Phase II: 38, Phase III: 13, Phase IV: 25) Co-authored-by: Cursor <cursoragent@cursor.com>
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424
r-statistics-service/tools/anova_one.R
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424
r-statistics-service/tools/anova_one.R
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#' @tool_code ST_ANOVA_ONE
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#' @name 单因素方差分析
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#' @version 1.0.0
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#' @description 三组及以上独立样本的均值差异比较（含事后多重比较）
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#' @author SSA-Pro Team
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library(glue)
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library(ggplot2)
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library(base64enc)
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run_analysis <- function(input) {
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# ===== 初始化 =====
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logs <- c()
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log_add <- function(msg) { logs <<- c(logs, paste0("[", Sys.time(), "] ", msg)) }
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on.exit({}, add = TRUE)
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# ===== 数据加载 =====
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log_add("开始加载输入数据")
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df <- tryCatch(
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load_input_data(input),
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error = function(e) {
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log_add(paste("数据加载失败:", e$message))
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return(NULL)
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}
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)
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if (is.null(df)) {
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return(make_error(ERROR_CODES$E100_INTERNAL_ERROR, details = "数据加载失败"))
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}
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log_add(glue("数据加载成功: {nrow(df)} 行, {ncol(df)} 列"))
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p <- input$params
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guardrails_cfg <- input$guardrails
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group_var <- p$group_var
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value_var <- p$value_var
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# ===== 参数校验 =====
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if (!(group_var %in% names(df))) {
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return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = group_var))
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}
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if (!(value_var %in% names(df))) {
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return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = value_var))
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}
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# ===== 数据清洗 =====
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original_rows <- nrow(df)
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df <- df[!is.na(df[[group_var]]) & trimws(as.character(df[[group_var]])) != "", ]
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df <- df[!is.na(df[[value_var]]), ]
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removed_rows <- original_rows - nrow(df)
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if (removed_rows > 0) {
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log_add(glue("数据清洗: 移除 {removed_rows} 行缺失值 (剩余 {nrow(df)} 行)"))
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}
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# 确保数值型
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if (!is.numeric(df[[value_var]])) {
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df[[value_var]] <- as.numeric(as.character(df[[value_var]]))
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df <- df[!is.na(df[[value_var]]), ]
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}
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# 分组信息
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df[[group_var]] <- as.factor(df[[group_var]])
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groups <- levels(df[[group_var]])
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n_groups <- length(groups)
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if (n_groups < 3) {
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return(make_error(ERROR_CODES$E003_INSUFFICIENT_GROUPS,
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col = group_var, expected = "3+", actual = n_groups))
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}
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log_add(glue("分组变量 '{group_var}' 有 {n_groups} 个水平: {paste(groups, collapse=', ')}"))
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# ===== 护栏检查 =====
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guardrail_results <- list()
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warnings_list <- c()
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use_kruskal <- FALSE
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# 每组样本量检查
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group_sizes <- table(df[[group_var]])
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min_group_n <- min(group_sizes)
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sample_check <- check_sample_size(min_group_n, min_required = 3, action = ACTION_BLOCK)
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guardrail_results <- c(guardrail_results, list(sample_check))
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log_add(glue("最小组样本量: {min_group_n}, {sample_check$reason}"))
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# 正态性检验（每组）
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if (isTRUE(guardrails_cfg$check_normality)) {
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log_add("执行正态性检验")
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normality_failed <- FALSE
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for (g in groups) {
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vals <- df[df[[group_var]] == g, value_var]
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if (length(vals) >= 3 && length(vals) <= 5000) {
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norm_check <- check_normality(vals, alpha = 0.05, action = ACTION_SWITCH, action_target = "Kruskal-Wallis")
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guardrail_results <- c(guardrail_results, list(norm_check))
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log_add(glue("组[{g}] 正态性: p = {round(norm_check$p_value, 4)}, {norm_check$reason}"))
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if (!norm_check$passed) normality_failed <- TRUE
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}
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}
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if (normality_failed) use_kruskal <- TRUE
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}
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# 方差齐性检验 (Levene)
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if (!use_kruskal) {
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tryCatch({
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homo_check <- check_homogeneity(df, group_var, value_var, alpha = 0.05, action = ACTION_WARN)
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guardrail_results <- c(guardrail_results, list(homo_check))
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log_add(glue("方差齐性 (Levene): p = {round(homo_check$p_value, 4)}, {homo_check$reason}"))
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if (!homo_check$passed) {
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warnings_list <- c(warnings_list, "方差不齐性，使用 Welch 校正的 ANOVA")
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}
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}, error = function(e) {
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log_add(paste("方差齐性检验失败:", e$message))
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})
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}
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guardrail_status <- run_guardrail_chain(guardrail_results)
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if (guardrail_status$status == "blocked") {
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return(list(status = "blocked", message = guardrail_status$reason, trace_log = logs))
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}
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if (guardrail_status$status == "switch") {
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use_kruskal <- TRUE
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warnings_list <- c(warnings_list, guardrail_status$reason)
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log_add(glue("正态性不满足，切换为 Kruskal-Wallis 检验"))
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}
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if (length(guardrail_status$warnings) > 0) {
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warnings_list <- c(warnings_list, guardrail_status$warnings)
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}
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# ===== 各组描述统计 =====
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group_stats <- lapply(groups, function(g) {
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vals <- df[df[[group_var]] == g, value_var]
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list(
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group = g,
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n = length(vals),
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mean = round(mean(vals), 3),
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sd = round(sd(vals), 3),
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median = round(median(vals), 3),
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q1 = round(quantile(vals, 0.25), 3),
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q3 = round(quantile(vals, 0.75), 3)
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)
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})
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# ===== 核心计算 =====
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if (use_kruskal) {
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log_add("执行 Kruskal-Wallis 检验")
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formula_obj <- as.formula(paste(value_var, "~", group_var))
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result <- kruskal.test(formula_obj, data = df)
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method_used <- "Kruskal-Wallis rank sum test"
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stat_name <- "H"
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# 效应量: η² (eta-squared approximation for Kruskal-Wallis)
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eta_sq <- (result$statistic - n_groups + 1) / (nrow(df) - n_groups)
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eta_sq <- max(0, as.numeric(eta_sq))
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output_results <- list(
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method = method_used,
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statistic = jsonlite::unbox(as.numeric(result$statistic)),
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statistic_name = stat_name,
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df = jsonlite::unbox(as.numeric(result$parameter)),
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p_value = jsonlite::unbox(as.numeric(result$p.value)),
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p_value_fmt = format_p_value(result$p.value),
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effect_size = list(
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eta_squared = jsonlite::unbox(round(eta_sq, 4)),
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interpretation = interpret_eta_sq(eta_sq)
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),
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group_stats = group_stats
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)
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# 事后多重比较: Dunn test (pairwise Wilcoxon)
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posthoc_result <- tryCatch({
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pw <- pairwise.wilcox.test(df[[value_var]], df[[group_var]], p.adjust.method = "bonferroni")
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pw
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}, error = function(e) {
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log_add(paste("Dunn 事后检验失败:", e$message))
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NULL
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})
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} else {
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log_add("执行单因素 ANOVA")
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formula_obj <- as.formula(paste(value_var, "~", group_var))
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# 检查方差齐性决定使用经典 ANOVA 还是 Welch ANOVA
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use_welch <- any(grepl("方差不齐性", warnings_list))
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if (use_welch) {
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result <- oneway.test(formula_obj, data = df, var.equal = FALSE)
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method_used <- "One-way ANOVA (Welch correction)"
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} else {
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aov_result <- aov(formula_obj, data = df)
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result_summary <- summary(aov_result)
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result <- list(
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statistic = result_summary[[1]]$`F value`[1],
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parameter = c(result_summary[[1]]$Df[1], result_summary[[1]]$Df[2]),
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p.value = result_summary[[1]]$`Pr(>F)`[1]
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)
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method_used <- "One-way ANOVA"
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}
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stat_name <- "F"
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# 效应量: η² (eta-squared)
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ss_between <- sum(tapply(df[[value_var]], df[[group_var]], function(x) length(x) * (mean(x) - mean(df[[value_var]]))^2))
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ss_total <- sum((df[[value_var]] - mean(df[[value_var]]))^2)
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eta_sq <- ss_between / ss_total
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f_val <- if (is.list(result)) result$statistic else as.numeric(result$statistic)
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df_val <- if (is.list(result) && !is.null(result$parameter)) {
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if (length(result$parameter) == 2) result$parameter else as.numeric(result$parameter)
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} else {
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as.numeric(result$parameter)
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}
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p_val <- if (is.list(result)) result$p.value else as.numeric(result$p.value)
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output_results <- list(
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method = method_used,
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statistic = jsonlite::unbox(as.numeric(f_val)),
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statistic_name = stat_name,
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df = if (length(df_val) == 2) as.numeric(df_val) else jsonlite::unbox(as.numeric(df_val)),
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p_value = jsonlite::unbox(as.numeric(p_val)),
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p_value_fmt = format_p_value(p_val),
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effect_size = list(
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eta_squared = jsonlite::unbox(round(eta_sq, 4)),
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interpretation = interpret_eta_sq(eta_sq)
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),
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group_stats = group_stats
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)
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# 事后多重比较: Tukey HSD (if classic ANOVA) or pairwise t-test
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posthoc_result <- tryCatch({
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if (use_welch) {
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pairwise.t.test(df[[value_var]], df[[group_var]], p.adjust.method = "bonferroni", pool.sd = FALSE)
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} else {
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TukeyHSD(aov(formula_obj, data = df))
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}
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}, error = function(e) {
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log_add(paste("事后多重比较失败:", e$message))
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NULL
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})
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}
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log_add(glue("{stat_name} = {round(as.numeric(output_results$statistic), 3)}, P = {round(as.numeric(output_results$p_value), 4)}"))
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# 整理事后比较结果
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posthoc_pairs <- NULL
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if (!is.null(posthoc_result)) {
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if (inherits(posthoc_result, "TukeyHSD")) {
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tukey_df <- as.data.frame(posthoc_result[[1]])
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posthoc_pairs <- lapply(seq_len(nrow(tukey_df)), function(i) {
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list(
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comparison = rownames(tukey_df)[i],
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diff = round(tukey_df$diff[i], 3),
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ci_lower = round(tukey_df$lwr[i], 3),
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ci_upper = round(tukey_df$upr[i], 3),
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p_adj = round(tukey_df$`p adj`[i], 4),
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p_adj_fmt = format_p_value(tukey_df$`p adj`[i]),
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significant = tukey_df$`p adj`[i] < 0.05
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)
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})
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} else if (inherits(posthoc_result, "pairwise.htest")) {
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p_matrix <- posthoc_result$p.value
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for (i in seq_len(nrow(p_matrix))) {
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for (j in seq_len(ncol(p_matrix))) {
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if (!is.na(p_matrix[i, j])) {
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if (is.null(posthoc_pairs)) posthoc_pairs <- list()
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posthoc_pairs[[length(posthoc_pairs) + 1]] <- list(
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comparison = paste(rownames(p_matrix)[i], "vs", colnames(p_matrix)[j]),
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p_adj = round(p_matrix[i, j], 4),
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p_adj_fmt = format_p_value(p_matrix[i, j]),
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significant = p_matrix[i, j] < 0.05
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)
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}
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}
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}
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}
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}
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output_results$posthoc <- posthoc_pairs
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# ===== 生成图表 =====
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log_add("生成箱线图")
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plot_base64 <- tryCatch({
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generate_anova_boxplot(df, group_var, value_var)
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}, error = function(e) {
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log_add(paste("图表生成失败:", e$message))
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NULL
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})
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# ===== 生成可复现代码 =====
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original_filename <- if (!is.null(input$original_filename) && nchar(input$original_filename) > 0) {
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input$original_filename
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} else {
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"data.csv"
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}
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reproducible_code <- glue('
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# SSA-Pro 自动生成代码
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# 工具: 单因素方差分析
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# 时间: {Sys.time()}
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# ================================
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library(ggplot2)
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# 数据准备
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df <- read.csv("{original_filename}")
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group_var <- "{group_var}"
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value_var <- "{value_var}"
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# 单因素 ANOVA
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result <- aov(as.formula(paste(value_var, "~", group_var)), data = df)
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summary(result)
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# 事后多重比较 (Tukey HSD)
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TukeyHSD(result)
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# 可视化
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ggplot(df, aes(x = .data[[group_var]], y = .data[[value_var]], fill = .data[[group_var]])) +
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geom_boxplot(alpha = 0.7) +
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theme_minimal() +
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labs(title = paste("Distribution of", value_var, "by", group_var))
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')
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# ===== 构建 report_blocks =====
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blocks <- list()
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# Block 1: 各组描述统计
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desc_headers <- c("组别", "N", "均值", "标准差", "中位数")
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desc_rows <- lapply(group_stats, function(gs) {
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c(gs$group, as.character(gs$n), as.character(gs$mean), as.character(gs$sd), as.character(gs$median))
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})
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blocks[[length(blocks) + 1]] <- make_table_block(desc_headers, desc_rows, title = "各组描述统计")
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# Block 2: 检验结果
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kv_items <- list(
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"方法" = method_used,
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"统计量" = paste0(stat_name, " = ", round(as.numeric(output_results$statistic), 3)),
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"P 值" = output_results$p_value_fmt,
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"η²" = as.character(output_results$effect_size$eta_squared),
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"效应量解释" = output_results$effect_size$interpretation
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)
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blocks[[length(blocks) + 1]] <- make_kv_block(kv_items, title = "检验结果")
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# Block 3: 事后多重比较
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if (!is.null(posthoc_pairs) && length(posthoc_pairs) > 0) {
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ph_headers <- c("比较", "P 值 (校正)", "显著性")
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ph_rows <- lapply(posthoc_pairs, function(pair) {
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sig <- if (pair$significant) "*" else ""
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c(pair$comparison, pair$p_adj_fmt, sig)
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})
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blocks[[length(blocks) + 1]] <- make_table_block(ph_headers, ph_rows,
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title = "事后多重比较",
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footnote = if (use_kruskal) "Bonferroni 校正的 Wilcoxon 检验" else "Tukey HSD / Bonferroni 校正")
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}
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# Block 4: 箱线图
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if (!is.null(plot_base64)) {
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blocks[[length(blocks) + 1]] <- make_image_block(plot_base64,
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title = paste(value_var, "by", group_var),
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alt = paste("箱线图：", value_var, "按", group_var, "分组"))
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}
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# Block 5: 结论摘要
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p_val_num <- as.numeric(output_results$p_value)
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sig_text <- if (p_val_num < 0.05) "各组间存在统计学显著差异" else "各组间差异无统计学意义"
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conclusion <- glue("{method_used}: {stat_name} = {round(as.numeric(output_results$statistic), 3)}, P {output_results$p_value_fmt}。{sig_text}（η² = {output_results$effect_size$eta_squared}，{output_results$effect_size$interpretation}效应）。")
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if (!is.null(posthoc_pairs) && p_val_num < 0.05) {
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sig_pairs <- Filter(function(x) x$significant, posthoc_pairs)
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if (length(sig_pairs) > 0) {
|
||||
pair_names <- sapply(sig_pairs, function(x) x$comparison)
|
||||
conclusion <- paste0(conclusion, glue("\n\n事后比较显示以下组间差异显著：{paste(pair_names, 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 = output_results,
|
||||
report_blocks = blocks,
|
||||
plots = if (!is.null(plot_base64)) list(plot_base64) else list(),
|
||||
trace_log = logs,
|
||||
reproducible_code = as.character(reproducible_code)
|
||||
))
|
||||
}
|
||||
|
||||
# η² 效应量解释
|
||||
interpret_eta_sq <- function(eta_sq) {
|
||||
if (eta_sq < 0.01) return("微小")
|
||||
if (eta_sq < 0.06) return("小")
|
||||
if (eta_sq < 0.14) return("中等")
|
||||
return("大")
|
||||
}
|
||||
|
||||
# NULL 合并运算符
|
||||
`%||%` <- function(x, y) if (is.null(x)) y else x
|
||||
|
||||
# 辅助函数：ANOVA 箱线图
|
||||
generate_anova_boxplot <- function(df, group_var, value_var) {
|
||||
p <- ggplot(df, aes(x = .data[[group_var]], y = .data[[value_var]], fill = .data[[group_var]])) +
|
||||
geom_boxplot(alpha = 0.7, outlier.shape = 21) +
|
||||
stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "red") +
|
||||
theme_minimal() +
|
||||
labs(
|
||||
title = paste("Distribution of", value_var, "by", group_var),
|
||||
x = group_var,
|
||||
y = value_var
|
||||
) +
|
||||
scale_fill_brewer(palette = "Set2") +
|
||||
theme(legend.position = "none")
|
||||
|
||||
tmp_file <- tempfile(fileext = ".png")
|
||||
ggsave(tmp_file, p, width = max(7, length(unique(df[[group_var]])) * 1.5), height = 5, dpi = 100)
|
||||
base64_str <- base64encode(tmp_file)
|
||||
unlink(tmp_file)
|
||||
|
||||
return(paste0("data:image/png;base64,", base64_str))
|
||||
}
|
||||
316
r-statistics-service/tools/baseline_table.R
Normal file
316
r-statistics-service/tools/baseline_table.R
Normal file
@@ -0,0 +1,316 @@
|
||||
#' @tool_code ST_BASELINE_TABLE
|
||||
#' @name 基线特征表（复合工具）
|
||||
#' @version 1.0.0
|
||||
#' @description 基于 gtsummary 的一键式基线特征表生成，自动判断变量类型、选择统计方法、输出标准三线表
|
||||
#' @author SSA-Pro Team
|
||||
#' @note 复合工具：一次遍历所有变量，自动选方法（T/Wilcoxon/χ²/Fisher），合并出表
|
||||
|
||||
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)) }
|
||||
warnings_list <- c()
|
||||
|
||||
on.exit({}, add = TRUE)
|
||||
|
||||
# ===== 依赖检查 =====
|
||||
required_pkgs <- c("gtsummary", "gt", "broom")
|
||||
for (pkg in required_pkgs) {
|
||||
if (!requireNamespace(pkg, quietly = TRUE)) {
|
||||
return(make_error(ERROR_CODES$E101_PACKAGE_MISSING, package = pkg))
|
||||
}
|
||||
}
|
||||
|
||||
library(gtsummary)
|
||||
library(dplyr)
|
||||
|
||||
# ===== 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$params
|
||||
group_var <- p$group_var
|
||||
analyze_vars <- as.character(unlist(p$analyze_vars))
|
||||
|
||||
# ===== 参数校验 =====
|
||||
if (is.null(group_var) || !(group_var %in% names(df))) {
|
||||
return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = group_var %||% "NULL"))
|
||||
}
|
||||
|
||||
if (is.null(analyze_vars) || length(analyze_vars) == 0) {
|
||||
analyze_vars <- setdiff(names(df), group_var)
|
||||
log_add(glue("未指定分析变量，自动选取全部 {length(analyze_vars)} 个变量"))
|
||||
}
|
||||
|
||||
missing_vars <- analyze_vars[!(analyze_vars %in% names(df))]
|
||||
if (length(missing_vars) > 0) {
|
||||
return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND,
|
||||
col = paste(missing_vars, collapse = ", ")))
|
||||
}
|
||||
|
||||
# ===== 数据清洗 =====
|
||||
original_rows <- nrow(df)
|
||||
df <- df[!is.na(df[[group_var]]) & trimws(as.character(df[[group_var]])) != "", ]
|
||||
removed_rows <- original_rows - nrow(df)
|
||||
if (removed_rows > 0) {
|
||||
log_add(glue("分组变量缺失值清洗: 移除 {removed_rows} 行 (剩余 {nrow(df)} 行)"))
|
||||
}
|
||||
|
||||
groups <- unique(df[[group_var]])
|
||||
n_groups <- length(groups)
|
||||
if (n_groups < 2) {
|
||||
return(make_error(ERROR_CODES$E003_INSUFFICIENT_GROUPS,
|
||||
col = group_var, expected = "2+", actual = n_groups))
|
||||
}
|
||||
|
||||
# 样本量检查
|
||||
sample_check <- check_sample_size(nrow(df), min_required = 10, action = ACTION_BLOCK)
|
||||
if (!sample_check$passed) {
|
||||
return(list(status = "blocked", message = sample_check$reason, trace_log = logs))
|
||||
}
|
||||
|
||||
# 确保分组变量是因子
|
||||
df[[group_var]] <- as.factor(df[[group_var]])
|
||||
|
||||
# 选取分析列
|
||||
df_analysis <- df[, c(group_var, analyze_vars), drop = FALSE]
|
||||
|
||||
log_add(glue("分组变量: {group_var} ({n_groups} 组: {paste(groups, collapse=', ')})"))
|
||||
log_add(glue("分析变量: {length(analyze_vars)} 个"))
|
||||
|
||||
# ===== 核心计算：gtsummary =====
|
||||
log_add("使用 gtsummary 生成基线特征表")
|
||||
|
||||
tbl <- tryCatch(
|
||||
withCallingHandlers(
|
||||
{
|
||||
tbl_summary(
|
||||
df_analysis,
|
||||
by = all_of(group_var),
|
||||
missing = "ifany",
|
||||
statistic = list(
|
||||
all_continuous() ~ "{mean} ({sd})",
|
||||
all_categorical() ~ "{n} ({p}%)"
|
||||
),
|
||||
digits = list(
|
||||
all_continuous() ~ 2,
|
||||
all_categorical() ~ c(0, 1)
|
||||
)
|
||||
) %>%
|
||||
add_p() %>%
|
||||
add_overall()
|
||||
},
|
||||
warning = function(w) {
|
||||
warnings_list <<- c(warnings_list, w$message)
|
||||
log_add(paste("gtsummary 警告:", w$message))
|
||||
invokeRestart("muffleWarning")
|
||||
}
|
||||
),
|
||||
error = function(e) {
|
||||
log_add(paste("gtsummary 生成失败:", e$message))
|
||||
return(NULL)
|
||||
}
|
||||
)
|
||||
|
||||
if (is.null(tbl)) {
|
||||
return(map_r_error("gtsummary 基线特征表生成失败"))
|
||||
}
|
||||
|
||||
log_add("gtsummary 表格生成成功")
|
||||
|
||||
# ===== 提取结构化数据 =====
|
||||
tbl_df <- as.data.frame(tbl$table_body)
|
||||
|
||||
# 提取显著变量列表
|
||||
significant_vars <- extract_significant_vars(tbl, alpha = 0.05)
|
||||
log_add(glue("显著变量 (P < 0.05): {length(significant_vars)} 个"))
|
||||
|
||||
# 提取每个变量使用的统计方法
|
||||
method_info <- extract_method_info(tbl)
|
||||
|
||||
# ===== 转换为 report_blocks =====
|
||||
log_add("转换 gtsummary → report_blocks")
|
||||
blocks <- gtsummary_to_blocks(tbl, group_var, groups, analyze_vars, significant_vars)
|
||||
|
||||
# ===== 构建结构化结果 =====
|
||||
output_results <- list(
|
||||
method = "gtsummary::tbl_summary + add_p",
|
||||
group_var = group_var,
|
||||
n_groups = n_groups,
|
||||
groups = lapply(groups, function(g) {
|
||||
list(label = as.character(g), n = sum(df[[group_var]] == g))
|
||||
}),
|
||||
n_variables = length(analyze_vars),
|
||||
significant_vars = significant_vars,
|
||||
method_info = method_info,
|
||||
total_n = nrow(df)
|
||||
)
|
||||
|
||||
# ===== 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"
|
||||
}
|
||||
|
||||
vars_str <- paste0('c("', paste(analyze_vars, collapse = '", "'), '")')
|
||||
|
||||
reproducible_code <- glue('
|
||||
# SSA-Pro 自动生成代码
|
||||
# 工具: 基线特征表 (gtsummary)
|
||||
# 时间: {Sys.time()}
|
||||
# ================================
|
||||
|
||||
# 自动安装依赖
|
||||
required_packages <- c("gtsummary", "gt", "dplyr")
|
||||
new_packages <- required_packages[!(required_packages %in% installed.packages()[,"Package"])]
|
||||
if(length(new_packages)) install.packages(new_packages, repos = "https://cloud.r-project.org")
|
||||
|
||||
library(gtsummary)
|
||||
library(dplyr)
|
||||
|
||||
# 数据准备
|
||||
df <- read.csv("{original_filename}")
|
||||
group_var <- "{group_var}"
|
||||
analyze_vars <- {vars_str}
|
||||
|
||||
df_analysis <- df[, c(group_var, analyze_vars)]
|
||||
df_analysis[[group_var]] <- as.factor(df_analysis[[group_var]])
|
||||
|
||||
# 生成基线特征表
|
||||
tbl <- tbl_summary(
|
||||
df_analysis,
|
||||
by = all_of(group_var),
|
||||
missing = "ifany",
|
||||
statistic = list(
|
||||
all_continuous() ~ "{{mean}} ({{sd}})",
|
||||
all_categorical() ~ "{{n}} ({{p}}%)"
|
||||
)
|
||||
) %>%
|
||||
add_p() %>%
|
||||
add_overall()
|
||||
|
||||
# 显示结果
|
||||
tbl
|
||||
|
||||
# 导出为 Word（可选）
|
||||
# tbl %>% as_gt() %>% gt::gtsave("baseline_table.docx")
|
||||
')
|
||||
|
||||
# ===== 返回结果 =====
|
||||
log_add("分析完成")
|
||||
|
||||
return(list(
|
||||
status = "success",
|
||||
message = "基线特征表生成完成",
|
||||
warnings = if (length(warnings_list) > 0) warnings_list else NULL,
|
||||
results = output_results,
|
||||
report_blocks = blocks,
|
||||
plots = list(),
|
||||
trace_log = logs,
|
||||
reproducible_code = as.character(reproducible_code)
|
||||
))
|
||||
}
|
||||
|
||||
# ===== gtsummary → report_blocks 转换层 =====
|
||||
|
||||
#' 将 gtsummary 表格转为 report_blocks
|
||||
gtsummary_to_blocks <- function(tbl, group_var, groups, analyze_vars, significant_vars) {
|
||||
blocks <- list()
|
||||
|
||||
# 提取 tibble 格式
|
||||
tbl_data <- gtsummary::as_tibble(tbl, col_labels = FALSE)
|
||||
|
||||
# Block 1: 三线表（核心输出）
|
||||
headers <- colnames(tbl_data)
|
||||
rows <- lapply(seq_len(nrow(tbl_data)), function(i) {
|
||||
row <- as.list(tbl_data[i, ])
|
||||
lapply(row, function(cell) {
|
||||
val <- as.character(cell)
|
||||
if (is.na(val) || val == "NA") "" else val
|
||||
})
|
||||
})
|
||||
|
||||
# 标记 P < 0.05 的行
|
||||
p_col_idx <- which(grepl("p.value|p_value", headers, ignore.case = TRUE))
|
||||
|
||||
blocks[[length(blocks) + 1]] <- make_table_block(
|
||||
headers, rows,
|
||||
title = glue("基线特征表 (按 {group_var} 分组)"),
|
||||
footnote = "连续变量: Mean (SD); 分类变量: N (%); P 值由自动选择的统计方法计算",
|
||||
metadata = list(
|
||||
is_baseline_table = TRUE,
|
||||
group_var = group_var,
|
||||
has_p_values = length(p_col_idx) > 0
|
||||
)
|
||||
)
|
||||
|
||||
# Block 2: 样本量概况
|
||||
group_n_items <- lapply(groups, function(g) {
|
||||
list(key = as.character(g), value = "—")
|
||||
})
|
||||
blocks[[length(blocks) + 1]] <- make_kv_block(
|
||||
list("总样本量" = as.character(nrow(tbl$inputs$data)),
|
||||
"分组变量" = group_var,
|
||||
"分组数" = as.character(length(groups)),
|
||||
"分析变量数" = as.character(length(analyze_vars))),
|
||||
title = "样本概况"
|
||||
)
|
||||
|
||||
# Block 3: 显著变量摘要
|
||||
if (length(significant_vars) > 0) {
|
||||
conclusion <- glue("在 α = 0.05 水平下，以下变量在组间存在显著差异：**{paste(significant_vars, collapse = '**、**')}**（共 {length(significant_vars)} 个）。")
|
||||
} else {
|
||||
conclusion <- "在 α = 0.05 水平下，未发现各组间存在显著差异的基线变量。"
|
||||
}
|
||||
blocks[[length(blocks) + 1]] <- make_markdown_block(conclusion, title = "组间差异摘要")
|
||||
|
||||
return(blocks)
|
||||
}
|
||||
|
||||
#' 从 gtsummary 提取显著变量
|
||||
extract_significant_vars <- function(tbl, alpha = 0.05) {
|
||||
body <- tbl$table_body
|
||||
p_vals <- as.numeric(unlist(body$p.value))
|
||||
vars <- as.character(body$variable)
|
||||
sig_idx <- which(!is.na(p_vals) & p_vals < alpha)
|
||||
if (length(sig_idx) == 0) return(character(0))
|
||||
unique(vars[sig_idx])
|
||||
}
|
||||
|
||||
#' 提取每个变量使用的统计方法
|
||||
extract_method_info <- function(tbl) {
|
||||
body <- tbl$table_body
|
||||
p_vals <- as.numeric(unlist(body$p.value))
|
||||
has_p <- which(!is.na(p_vals))
|
||||
if (length(has_p) == 0) return(list())
|
||||
|
||||
test_names <- if ("test_name" %in% colnames(body)) as.character(unlist(body$test_name)) else rep("unknown", nrow(body))
|
||||
|
||||
lapply(has_p, function(i) {
|
||||
list(
|
||||
variable = as.character(body$variable[i]),
|
||||
test_name = test_names[i] %||% "unknown",
|
||||
p_value = round(p_vals[i], 4),
|
||||
p_value_fmt = format_p_value(p_vals[i])
|
||||
)
|
||||
})
|
||||
}
|
||||
|
||||
# NULL 合并运算符
|
||||
`%||%` <- function(x, y) if (is.null(x)) y else x
|
||||
272
r-statistics-service/tools/fisher.R
Normal file
272
r-statistics-service/tools/fisher.R
Normal file
@@ -0,0 +1,272 @@
|
||||
#' @tool_code ST_FISHER
|
||||
#' @name Fisher 精确检验
|
||||
#' @version 1.0.0
|
||||
#' @description 小样本或稀疏列联表的精确独立性检验（卡方检验的替代方法）
|
||||
#' @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$params
|
||||
var1 <- p$var1
|
||||
var2 <- p$var2
|
||||
|
||||
# ===== 参数校验 =====
|
||||
if (!(var1 %in% names(df))) {
|
||||
return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = var1))
|
||||
}
|
||||
if (!(var2 %in% names(df))) {
|
||||
return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = var2))
|
||||
}
|
||||
|
||||
# ===== 数据清洗 =====
|
||||
original_rows <- nrow(df)
|
||||
df <- df[!is.na(df[[var1]]) & trimws(as.character(df[[var1]])) != "", ]
|
||||
df <- df[!is.na(df[[var2]]) & trimws(as.character(df[[var2]])) != "", ]
|
||||
|
||||
removed_rows <- original_rows - nrow(df)
|
||||
if (removed_rows > 0) {
|
||||
log_add(glue("数据清洗: 移除 {removed_rows} 行缺失值 (剩余 {nrow(df)} 行)"))
|
||||
}
|
||||
|
||||
# ===== 护栏检查 =====
|
||||
guardrail_results <- list()
|
||||
warnings_list <- c()
|
||||
|
||||
sample_check <- check_sample_size(nrow(df), min_required = 4, action = ACTION_BLOCK)
|
||||
guardrail_results <- c(guardrail_results, list(sample_check))
|
||||
log_add(glue("样本量检查: N = {nrow(df)}, {sample_check$reason}"))
|
||||
|
||||
guardrail_status <- run_guardrail_chain(guardrail_results)
|
||||
|
||||
if (guardrail_status$status == "blocked") {
|
||||
return(list(
|
||||
status = "blocked",
|
||||
message = guardrail_status$reason,
|
||||
trace_log = logs
|
||||
))
|
||||
}
|
||||
|
||||
# ===== 构建列联表 =====
|
||||
contingency_table <- table(df[[var1]], df[[var2]])
|
||||
log_add(glue("列联表维度: {nrow(contingency_table)} x {ncol(contingency_table)}"))
|
||||
|
||||
if (nrow(contingency_table) < 2 || ncol(contingency_table) < 2) {
|
||||
return(make_error(ERROR_CODES$E003_INSUFFICIENT_GROUPS,
|
||||
col = paste(var1, "或", var2),
|
||||
expected = 2,
|
||||
actual = min(nrow(contingency_table), ncol(contingency_table))))
|
||||
}
|
||||
|
||||
is_2x2 <- nrow(contingency_table) == 2 && ncol(contingency_table) == 2
|
||||
|
||||
# 期望频数信息（仅供报告）
|
||||
expected <- chisq.test(contingency_table)$expected
|
||||
low_expected_count <- sum(expected < 5)
|
||||
total_cells <- length(expected)
|
||||
low_expected_pct <- low_expected_count / total_cells
|
||||
|
||||
if (low_expected_pct > 0) {
|
||||
log_add(glue("期望频数 < 5 的格子: {low_expected_count}/{total_cells} ({round(low_expected_pct * 100, 1)}%)"))
|
||||
}
|
||||
|
||||
# ===== 核心计算 =====
|
||||
log_add("执行 Fisher 精确检验")
|
||||
|
||||
result <- tryCatch({
|
||||
if (is_2x2) {
|
||||
fisher.test(contingency_table)
|
||||
} else {
|
||||
fisher.test(contingency_table, simulate.p.value = TRUE, B = 10000)
|
||||
}
|
||||
}, error = function(e) {
|
||||
log_add(paste("Fisher 检验失败:", e$message))
|
||||
return(NULL)
|
||||
})
|
||||
|
||||
if (is.null(result)) {
|
||||
return(map_r_error("Fisher 精确检验计算失败，列联表可能过大"))
|
||||
}
|
||||
|
||||
method_used <- result$method
|
||||
|
||||
output_results <- list(
|
||||
method = method_used,
|
||||
p_value = jsonlite::unbox(as.numeric(result$p.value)),
|
||||
p_value_fmt = format_p_value(result$p.value)
|
||||
)
|
||||
|
||||
if (!is.null(result$estimate)) {
|
||||
output_results$odds_ratio = jsonlite::unbox(as.numeric(result$estimate))
|
||||
}
|
||||
if (!is.null(result$conf.int)) {
|
||||
output_results$conf_int = as.numeric(result$conf.int)
|
||||
}
|
||||
|
||||
observed_matrix <- matrix(
|
||||
as.numeric(contingency_table),
|
||||
nrow = nrow(contingency_table),
|
||||
ncol = ncol(contingency_table),
|
||||
dimnames = list(rownames(contingency_table), colnames(contingency_table))
|
||||
)
|
||||
|
||||
output_results$contingency_table <- list(
|
||||
row_var = var1,
|
||||
col_var = var2,
|
||||
row_levels = as.character(rownames(contingency_table)),
|
||||
col_levels = as.character(colnames(contingency_table)),
|
||||
observed = observed_matrix,
|
||||
row_totals = as.numeric(rowSums(contingency_table)),
|
||||
col_totals = as.numeric(colSums(contingency_table)),
|
||||
grand_total = jsonlite::unbox(sum(contingency_table))
|
||||
)
|
||||
|
||||
log_add(glue("P = {round(result$p.value, 4)}"))
|
||||
|
||||
# ===== 生成图表 =====
|
||||
log_add("生成堆叠条形图")
|
||||
plot_base64 <- tryCatch({
|
||||
generate_stacked_bar(contingency_table, var1, var2)
|
||||
}, 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: Fisher 精确检验
|
||||
# 时间: {Sys.time()}
|
||||
# ================================
|
||||
|
||||
library(ggplot2)
|
||||
|
||||
# 数据准备
|
||||
df <- read.csv("{original_filename}")
|
||||
var1 <- "{var1}"
|
||||
var2 <- "{var2}"
|
||||
|
||||
# 数据清洗
|
||||
df <- df[!is.na(df[[var1]]) & !is.na(df[[var2]]), ]
|
||||
|
||||
# 构建列联表
|
||||
contingency_table <- table(df[[var1]], df[[var2]])
|
||||
print(contingency_table)
|
||||
|
||||
# Fisher 精确检验
|
||||
result <- fisher.test(contingency_table)
|
||||
print(result)
|
||||
')
|
||||
|
||||
# ===== 构建 report_blocks =====
|
||||
blocks <- list()
|
||||
|
||||
# Block 1: 列联表
|
||||
table_headers <- c(var1, as.character(colnames(contingency_table)))
|
||||
table_rows <- lapply(seq_len(nrow(contingency_table)), function(i) {
|
||||
c(as.character(rownames(contingency_table)[i]), as.character(contingency_table[i, ]))
|
||||
})
|
||||
blocks[[length(blocks) + 1]] <- make_table_block(table_headers, table_rows, title = "列联表")
|
||||
|
||||
# Block 2: 检验结果
|
||||
kv_items <- list(
|
||||
"方法" = method_used,
|
||||
"P 值" = output_results$p_value_fmt
|
||||
)
|
||||
if (!is.null(output_results$odds_ratio)) {
|
||||
kv_items[["比值比 (OR)"]] <- as.character(round(as.numeric(output_results$odds_ratio), 4))
|
||||
}
|
||||
if (!is.null(output_results$conf_int)) {
|
||||
kv_items[["95% 置信区间"]] <- sprintf("[%.4f, %.4f]", output_results$conf_int[1], output_results$conf_int[2])
|
||||
}
|
||||
if (low_expected_count > 0) {
|
||||
kv_items[["期望频数 < 5 的格子"]] <- glue("{low_expected_count}/{total_cells}")
|
||||
}
|
||||
blocks[[length(blocks) + 1]] <- make_kv_block(kv_items, title = "检验结果")
|
||||
|
||||
# Block 3: 图表
|
||||
if (!is.null(plot_base64)) {
|
||||
blocks[[length(blocks) + 1]] <- make_image_block(plot_base64, title = "堆叠条形图",
|
||||
alt = paste("堆叠条形图：", var1, "与", var2, "的关联"))
|
||||
}
|
||||
|
||||
# Block 4: 结论摘要
|
||||
p_val <- as.numeric(output_results$p_value)
|
||||
conclusion <- if (p_val < 0.05) {
|
||||
glue("Fisher 精确检验显示，{var1} 与 {var2} 之间存在显著关联（P {output_results$p_value_fmt}）。")
|
||||
} else {
|
||||
glue("Fisher 精确检验显示，未发现 {var1} 与 {var2} 之间的显著关联（P {output_results$p_value_fmt}）。")
|
||||
}
|
||||
if (!is.null(output_results$odds_ratio)) {
|
||||
conclusion <- paste0(conclusion, glue(" 比值比 OR = {round(as.numeric(output_results$odds_ratio), 3)}。"))
|
||||
}
|
||||
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 = output_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：堆叠条形图
|
||||
generate_stacked_bar <- function(contingency_table, var1, var2) {
|
||||
df_plot <- as.data.frame(contingency_table)
|
||||
names(df_plot) <- c("Var1", "Var2", "Freq")
|
||||
|
||||
p <- ggplot(df_plot, aes(x = Var1, y = Freq, fill = Var2)) +
|
||||
geom_bar(stat = "identity", position = "fill") +
|
||||
scale_y_continuous(labels = scales::percent) +
|
||||
theme_minimal() +
|
||||
labs(
|
||||
title = paste("Association:", var1, "vs", var2),
|
||||
x = var1,
|
||||
y = "Proportion",
|
||||
fill = var2
|
||||
) +
|
||||
scale_fill_brewer(palette = "Set2")
|
||||
|
||||
tmp_file <- tempfile(fileext = ".png")
|
||||
ggsave(tmp_file, p, width = 7, height = 5, dpi = 100)
|
||||
base64_str <- base64encode(tmp_file)
|
||||
unlink(tmp_file)
|
||||
|
||||
return(paste0("data:image/png;base64,", base64_str))
|
||||
}
|
||||
377
r-statistics-service/tools/linear_reg.R
Normal file
377
r-statistics-service/tools/linear_reg.R
Normal file
@@ -0,0 +1,377 @@
|
||||
#' @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))
|
||||
}
|
||||
286
r-statistics-service/tools/wilcoxon.R
Normal file
286
r-statistics-service/tools/wilcoxon.R
Normal file
@@ -0,0 +1,286 @@
|
||||
#' @tool_code ST_WILCOXON
|
||||
#' @name Wilcoxon 符号秩检验
|
||||
#' @version 1.0.0
|
||||
#' @description 配对样本的非参数检验（配对 T 检验的替代方法）
|
||||
#' @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$params
|
||||
before_var <- p$before_var
|
||||
after_var <- p$after_var
|
||||
|
||||
# ===== 参数校验 =====
|
||||
if (!(before_var %in% names(df))) {
|
||||
return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = before_var))
|
||||
}
|
||||
if (!(after_var %in% names(df))) {
|
||||
return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = after_var))
|
||||
}
|
||||
|
||||
# ===== 数据清洗 =====
|
||||
original_rows <- nrow(df)
|
||||
df <- df[!is.na(df[[before_var]]) & !is.na(df[[after_var]]), ]
|
||||
|
||||
# 确保数值型
|
||||
if (!is.numeric(df[[before_var]])) {
|
||||
df[[before_var]] <- as.numeric(as.character(df[[before_var]]))
|
||||
df <- df[!is.na(df[[before_var]]), ]
|
||||
}
|
||||
if (!is.numeric(df[[after_var]])) {
|
||||
df[[after_var]] <- as.numeric(as.character(df[[after_var]]))
|
||||
df <- df[!is.na(df[[after_var]]), ]
|
||||
}
|
||||
|
||||
removed_rows <- original_rows - nrow(df)
|
||||
if (removed_rows > 0) {
|
||||
log_add(glue("数据清洗: 移除 {removed_rows} 行缺失值 (剩余 {nrow(df)} 行)"))
|
||||
}
|
||||
|
||||
# ===== 护栏检查 =====
|
||||
guardrail_results <- list()
|
||||
warnings_list <- c()
|
||||
|
||||
sample_check <- check_sample_size(nrow(df), min_required = 5, action = ACTION_BLOCK)
|
||||
guardrail_results <- c(guardrail_results, list(sample_check))
|
||||
log_add(glue("配对样本量: N = {nrow(df)}, {sample_check$reason}"))
|
||||
|
||||
guardrail_status <- run_guardrail_chain(guardrail_results)
|
||||
|
||||
if (guardrail_status$status == "blocked") {
|
||||
return(list(status = "blocked", message = guardrail_status$reason, trace_log = logs))
|
||||
}
|
||||
|
||||
# ===== 计算差值 =====
|
||||
diff_values <- df[[after_var]] - df[[before_var]]
|
||||
|
||||
# 检查差值方差（容差比较避免浮点精度问题）
|
||||
if (isTRUE(sd(diff_values) < .Machine$double.eps^0.5)) {
|
||||
return(make_error(ERROR_CODES$E007_VARIANCE_ZERO, col = paste(after_var, "-", before_var)))
|
||||
}
|
||||
|
||||
# ===== 核心计算 =====
|
||||
log_add("执行 Wilcoxon 符号秩检验")
|
||||
|
||||
result <- tryCatch({
|
||||
wilcox.test(df[[before_var]], df[[after_var]], paired = TRUE, conf.int = TRUE)
|
||||
}, error = function(e) {
|
||||
log_add(paste("Wilcoxon 检验失败:", e$message))
|
||||
return(NULL)
|
||||
})
|
||||
|
||||
if (is.null(result)) {
|
||||
return(map_r_error("Wilcoxon 符号秩检验计算失败"))
|
||||
}
|
||||
|
||||
method_used <- result$method
|
||||
log_add(glue("V = {result$statistic}, P = {round(result$p.value, 4)}"))
|
||||
|
||||
# ===== 效应量: r = Z / sqrt(N) =====
|
||||
n_pairs <- nrow(df)
|
||||
z_approx <- qnorm(result$p.value / 2)
|
||||
r_effect <- abs(z_approx) / sqrt(n_pairs)
|
||||
|
||||
r_interpretation <- if (r_effect < 0.1) "微小" else if (r_effect < 0.3) "小" else if (r_effect < 0.5) "中等" else "大"
|
||||
|
||||
# ===== 描述统计 =====
|
||||
before_vals <- df[[before_var]]
|
||||
after_vals <- df[[after_var]]
|
||||
|
||||
desc_stats <- list(
|
||||
before = list(
|
||||
variable = before_var,
|
||||
n = length(before_vals),
|
||||
mean = round(mean(before_vals), 3),
|
||||
sd = round(sd(before_vals), 3),
|
||||
median = round(median(before_vals), 3),
|
||||
q1 = round(quantile(before_vals, 0.25), 3),
|
||||
q3 = round(quantile(before_vals, 0.75), 3)
|
||||
),
|
||||
after = list(
|
||||
variable = after_var,
|
||||
n = length(after_vals),
|
||||
mean = round(mean(after_vals), 3),
|
||||
sd = round(sd(after_vals), 3),
|
||||
median = round(median(after_vals), 3),
|
||||
q1 = round(quantile(after_vals, 0.25), 3),
|
||||
q3 = round(quantile(after_vals, 0.75), 3)
|
||||
),
|
||||
difference = list(
|
||||
mean = round(mean(diff_values), 3),
|
||||
sd = round(sd(diff_values), 3),
|
||||
median = round(median(diff_values), 3)
|
||||
)
|
||||
)
|
||||
|
||||
output_results <- list(
|
||||
method = method_used,
|
||||
statistic_V = jsonlite::unbox(as.numeric(result$statistic)),
|
||||
p_value = jsonlite::unbox(as.numeric(result$p.value)),
|
||||
p_value_fmt = format_p_value(result$p.value),
|
||||
pseudomedian = if (!is.null(result$estimate)) jsonlite::unbox(round(as.numeric(result$estimate), 4)) else NULL,
|
||||
conf_int = if (!is.null(result$conf.int)) round(as.numeric(result$conf.int), 4) else NULL,
|
||||
effect_size = list(
|
||||
r = jsonlite::unbox(round(r_effect, 4)),
|
||||
interpretation = r_interpretation
|
||||
),
|
||||
descriptive = desc_stats
|
||||
)
|
||||
|
||||
# ===== 生成图表 =====
|
||||
log_add("生成配对变化图")
|
||||
plot_base64 <- tryCatch({
|
||||
generate_paired_plot(df, before_var, after_var, diff_values)
|
||||
}, 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: Wilcoxon 符号秩检验
|
||||
# 时间: {Sys.time()}
|
||||
# ================================
|
||||
|
||||
library(ggplot2)
|
||||
|
||||
# 数据准备
|
||||
df <- read.csv("{original_filename}")
|
||||
before_var <- "{before_var}"
|
||||
after_var <- "{after_var}"
|
||||
|
||||
# 数据清洗
|
||||
df <- df[!is.na(df[[before_var]]) & !is.na(df[[after_var]]), ]
|
||||
|
||||
# Wilcoxon 符号秩检验
|
||||
result <- wilcox.test(df[[before_var]], df[[after_var]], paired = TRUE, conf.int = TRUE)
|
||||
print(result)
|
||||
|
||||
# 描述统计
|
||||
cat("Before: median =", median(df[[before_var]]), "\\n")
|
||||
cat("After: median =", median(df[[after_var]]), "\\n")
|
||||
cat("Diff: median =", median(df[[after_var]] - df[[before_var]]), "\\n")
|
||||
')
|
||||
|
||||
# ===== 构建 report_blocks =====
|
||||
blocks <- list()
|
||||
|
||||
# Block 1: 描述统计
|
||||
desc_kv <- list()
|
||||
desc_kv[["配对样本量"]] <- as.character(n_pairs)
|
||||
desc_kv[[paste0(before_var, " Median [Q1, Q3]")]] <- as.character(glue("{desc_stats$before$median} [{desc_stats$before$q1}, {desc_stats$before$q3}]"))
|
||||
desc_kv[[paste0(after_var, " Median [Q1, Q3]")]] <- as.character(glue("{desc_stats$after$median} [{desc_stats$after$q1}, {desc_stats$after$q3}]"))
|
||||
desc_kv[["差值 Median"]] <- as.character(desc_stats$difference$median)
|
||||
blocks[[length(blocks) + 1]] <- make_kv_block(desc_kv, title = "样本概况")
|
||||
|
||||
# Block 2: 检验结果
|
||||
kv_result <- list(
|
||||
"方法" = method_used,
|
||||
"V 统计量" = as.character(round(as.numeric(result$statistic), 1)),
|
||||
"P 值" = output_results$p_value_fmt,
|
||||
"效应量 r" = as.character(output_results$effect_size$r),
|
||||
"效应量解释" = r_interpretation
|
||||
)
|
||||
if (!is.null(output_results$pseudomedian)) {
|
||||
kv_result[["伪中位数"]] <- as.character(output_results$pseudomedian)
|
||||
}
|
||||
if (!is.null(output_results$conf_int)) {
|
||||
kv_result[["95% 置信区间"]] <- sprintf("[%.4f, %.4f]", output_results$conf_int[1], output_results$conf_int[2])
|
||||
}
|
||||
blocks[[length(blocks) + 1]] <- make_kv_block(kv_result, title = "Wilcoxon 符号秩检验结果")
|
||||
|
||||
# Block 3: 图表
|
||||
if (!is.null(plot_base64)) {
|
||||
blocks[[length(blocks) + 1]] <- make_image_block(plot_base64,
|
||||
title = paste("配对变化:", before_var, "→", after_var),
|
||||
alt = "配对样本前后变化图")
|
||||
}
|
||||
|
||||
# Block 4: 结论摘要
|
||||
sig_text <- if (result$p.value < 0.05) "差异具有统计学意义" else "差异无统计学意义"
|
||||
direction <- if (mean(diff_values) > 0) "升高" else "降低"
|
||||
conclusion <- glue(
|
||||
"Wilcoxon 符号秩检验结果：V = {round(as.numeric(result$statistic), 1)}，P {output_results$p_value_fmt}。",
|
||||
"配对样本从 **{before_var}** 到 **{after_var}** 的变化{sig_text}",
|
||||
"（中位数{direction} {abs(desc_stats$difference$median)}，效应量 r = {output_results$effect_size$r}，{r_interpretation}效应）。"
|
||||
)
|
||||
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 = output_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：配对变化图（差值直方图 + 配对连线图）
|
||||
generate_paired_plot <- function(df, before_var, after_var, diff_values) {
|
||||
# 配对连线图
|
||||
n <- nrow(df)
|
||||
plot_df <- data.frame(
|
||||
id = rep(1:n, 2),
|
||||
time = rep(c("Before", "After"), each = n),
|
||||
value = c(df[[before_var]], df[[after_var]])
|
||||
)
|
||||
plot_df$time <- factor(plot_df$time, levels = c("Before", "After"))
|
||||
|
||||
p <- ggplot(plot_df, aes(x = time, y = value)) +
|
||||
geom_line(aes(group = id), alpha = 0.3, color = "gray60") +
|
||||
geom_point(aes(color = time), size = 2, alpha = 0.6) +
|
||||
stat_summary(fun = median, geom = "point", shape = 18, size = 5, color = "red") +
|
||||
stat_summary(fun = median, geom = "line", aes(group = 1), color = "red", linewidth = 1.2) +
|
||||
theme_minimal() +
|
||||
labs(
|
||||
title = paste("Paired Change:", before_var, "→", after_var),
|
||||
x = "",
|
||||
y = "Value"
|
||||
) +
|
||||
scale_color_manual(values = c("Before" = "#3b82f6", "After" = "#ef4444")) +
|
||||
theme(legend.position = "none")
|
||||
|
||||
tmp_file <- tempfile(fileext = ".png")
|
||||
ggsave(tmp_file, p, width = 6, height = 5, dpi = 100)
|
||||
base64_str <- base64encode(tmp_file)
|
||||
unlink(tmp_file)
|
||||
|
||||
return(paste0("data:image/png;base64,", base64_str))
|
||||
}
|
||||
Reference in New Issue
Block a user