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))
|
||||
}
|
||||
Reference in New Issue
Block a user