diff --git a/Dockerfile b/Dockerfile
index 2e3fac845305b098091e97375bd2a3fcee06dc90..f1508482d1bcaf998b7579ff9248e1c2179322b7 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -10,17 +10,16 @@ RUN R -e "install.packages(c('shiny', 'shinydashboard', 'remotes','readxl'), rep
 RUN echo 'options(radiant.shinyFiles = FALSE)' >> /usr/local/lib/R/etc/Rprofile.site
 COPY . /srv/shiny-server/
 COPY set_path.R /usr/local/lib/R/etc/Rprofile.site.d/00-radiant-path.R
+RUN echo 'source("/srv/shiny-server/radiant.model/R/coxp.R")' >> /usr/local/lib/R/etc/Rprofile.site
 
-# 安装所有子模块
-RUN R -e "remotes::install_local('/srv/shiny-server/radiant.data',dependencies=TRUE, type='source', upgrade='never')"
-RUN R -e "remotes::install_local('/srv/shiny-server/radiant.basics',dependencies=TRUE, type='source', upgrade='never')"
-RUN R -e "remotes::install_local('/srv/shiny-server/radiant.model',dependencies=TRUE, type='source', upgrade='never')"
-RUN R -e "remotes::install_local('/srv/shiny-server/radiant.multivariate',dependencies=TRUE, type='source', upgrade='never')"
-RUN R -e "remotes::install_local('/srv/shiny-server/radiant.design',dependencies=TRUE, type='source', upgrade='never')"
-RUN R -e "remotes::install_local('/srv/shiny-server/radiant.quickgen',dependencies=TRUE, type='source', upgrade='never')"
-
-# 安装主 radiant 应用
-RUN R -e "remotes::install_local('/srv/shiny-server/radiant-master',dependencies=TRUE, type='source', upgrade='never')"
+# 安装所有模块
+RUN R -e "remotes::install_local('/srv/shiny-server/radiant.data',dependencies=TRUE, type='source', upgrade='never',force=TRUE)"
+RUN R -e "remotes::install_local('/srv/shiny-server/radiant.basics',dependencies=TRUE, type='source', upgrade='never',force=TRUE)"
+RUN R -e "remotes::install_local('/srv/shiny-server/radiant.model',dependencies=TRUE, type='source', upgrade='never',force=TRUE)"
+RUN R -e "remotes::install_local('/srv/shiny-server/radiant.multivariate',dependencies=TRUE, type='source', upgrade='never',force=TRUE)"
+RUN R -e "remotes::install_local('/srv/shiny-server/radiant.design',dependencies=TRUE, type='source', upgrade='never',force=TRUE)"
+RUN R -e "remotes::install_local('/srv/shiny-server/radiant.quickgen',dependencies=TRUE, type='source', upgrade='never',force=TRUE)"
+RUN R -e "remotes::install_local('/srv/shiny-server/radiant-master',dependencies=TRUE, type='source', upgrade='never',force=TRUE)"
 
 WORKDIR /data
 CMD ["R", "-e", "radiant::radiant(host='0.0.0.0', port=3838)"]
diff --git a/radiant-master/inst/translations/translation_zh.csv b/radiant-master/inst/translations/translation_zh.csv
index 01224b3b0a00da08ac0b7527a93b9d3662089c23..993fcb67a4a53ddc8036b5a03698d9710ed066a4 100644
--- a/radiant-master/inst/translations/translation_zh.csv
+++ b/radiant-master/inst/translations/translation_zh.csv
@@ -137,6 +137,7 @@ Probabilities:,概率：,"goodness_ui.R,randomizer_ui.R"
 "Enter probabilities (e.g., 1/2 1/2)",输入概率（例如：1/2 1/2）,"goodness_ui.R,randomizer_ui.R"
 Discrete,离散分布,"prob_calc_ui.R,simulater_ui.R"
 F,F 分布,prob_calc_ui.R
+t,t 分布,prob_calc_ui.R
 Log normal,对数正态分布,"prob_calc_ui.R,simulater_ui.R"
 Poisson,泊松分布,"prob_calc_ui.R,simulater_ui.R"
 Values,数值,prob_calc_ui.R
@@ -456,7 +457,7 @@ All,全部,"view_ui.R,evalbin_ui.R, evalreg_ui.R"
 "Table slice {input$view_tab_slice} will be applied on Download, Store, or Report",表格切片 {input$view_tab_slice} 将应用于下载、存储或报告,view_ui.R
 Dataset ',数据集 ',view_ui.R
 ' was successfully added to the datasets dropdown. Add code to Report > Rmd or Report > R to (re)create the dataset by clicking the report icon on the bottom left of your screen.,' 已成功添加到数据集下拉菜单中。通过点击左下角的报告图标，在 Report > Rmd 或 Report > R 中添加代码以（重新）创建数据集。,view_ui.R
-Distribution,分布,"visualize_ui.R,logistic_ui.R"
+Distribution,分布图,"visualize_ui.R,logistic_ui.R"
 Surface,表面图,visualize_ui.R
 Line,折线图,"visualize_ui.R,regress_ui.R"
 Box-plot,箱线图,visualize_ui.R
@@ -818,7 +819,7 @@ Odds, "赔率", "logistic_ui.R"
 Correlations, "相关性", "logistic_ui.R"
 Model fit, "模型拟合", "logistic_ui.R"
 Coefficient (OR) plot, "系数（OR）图", "logistic_ui.R"
-Influential observations, "影响观察值", "logistic_ui.R"
+Influential observations, "影响点图", "logistic_ui.R"
 This analysis requires a response variable with two levels and one or more explanatory variables. If these variables are not available please select another dataset.,该分析需要一个具有两个级别的响应变量以及一个或多个解释变量。如果这些变量不可用，请选择另一个数据集。, "logistic_ui.R"
 Drop intercept,去除截距项,mnl_ui.R
 RRRs,相对风险比 (RRR),mnl_ui.R
@@ -1182,8 +1183,12 @@ Estimate class probabilities,估计类别概率,svm_ui.R
 Choose Excel file:,选择Excel文件：,manage_ui.R
 Sheet index (1-based):,工作表索引（从1开始）：,manage_ui.R
 First row as header,第一行为表头,manage_ui.R
-Time variable:,生存时间变量：,cox_ui.R
-Status variable:,事件状态变量：,cox_ui.R
+Time variable:,生存时间变量：,coxp_ui.R
+Status variable:,事件状态变量：,coxp_ui.R
 AI running...,大模型运行中...,quickgen_ai_ui.R
 Warning:Please enter a request related to descriptive statistics or visualization.,警告：请输入与描述性统计或可视化相关的请求。,quickgen_ai_ui.R
 Boxplot,箱型图,homo_variance_test_ui.R
+Survival curves,生存曲线,coxp_ui.R
+Cumulative hazard,累积风险,coxp_ui.R
+Schoenfeld residuals,Schoenfeld残差图,coxp_ui.R
+Martingale residuals,鞅残差图,coxp_ui.R
diff --git a/radiant.model/R/coxp.R b/radiant.model/R/coxp.R
index f0db521f7a0227f929af8fcd004cd088adb34936..cc51f4a32a9ee3472a1ac9dd35814bac1d93b73d 100644
--- a/radiant.model/R/coxp.R
+++ b/radiant.model/R/coxp.R
@@ -63,9 +63,9 @@ coxp <- function(dataset,
   }
   
   if ("robust" %in% check) {
-    model <- survival::coxph(form, data = dataset, robust = TRUE)
+    model <- survival::coxph(form, data = dataset, robust = TRUE, x = TRUE, y = TRUE)
   } else {
-    model <- survival::coxph(form, data = dataset)
+    model <- survival::coxph(form, data = dataset, x = TRUE, y = TRUE)
   }
   
   ## 失败模型保护
@@ -78,12 +78,12 @@ coxp <- function(dataset,
   n  <- nrow(dataset)          # 样本量
   n_event <- sum(dataset[[status]])  # 事件数
   conc <- tryCatch(
-  survival::concordancefit(
-    y = Surv(dataset[[time]], dataset[[status]]),
-    x = predict(model, type = "lp"),
-    data = dataset
-  )$concordance,
-  error = function(e) NA
+    survival::concordancefit(
+      y = Surv(dataset[[time]], dataset[[status]]),
+      x = predict(model, type = "lp"),
+      data = dataset
+    )$concordance,
+    error = function(e) NA
   )
   ## 打包返回
   out <- as.list(environment())
@@ -96,6 +96,9 @@ coxp <- function(dataset,
   out$n       <- n
   out$n_event <- n_event
   out$concordance <- conc
+  out$model$model <- dataset[, all.vars(form), drop = FALSE]
+  out$time_var  <- time
+  out$status_var <- status
   add_class(out, c("coxp", "model"))
 }
 
@@ -149,7 +152,7 @@ summary.coxp <- function(object, dec = 3, ...) {
   
   colnames(coeff) <- c(" ", "Coef", "SE", "z", "p", " ", "HR", "HR.lower", "HR.upper")
   print.data.frame(coeff, row.names = FALSE)
-
+  
   cat("\nSignif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n")
   
   ## 模型检验
@@ -174,11 +177,11 @@ predict.coxp <- function(object, pred_data = NULL, pred_cmd = "",
   if (is.null(pred_data)) {
     newdata <- envir$.model_frame %||% object$model$model
   } else {
-    # 获取预测数据集（只取模型需要的变量，但先全取以便校验）
+    # 获取预测数据集
     newdata <- get_data(pred_data, vars = NULL, envir = envir)
     
     # 变量存在性校验
-    model_evar <- object$evar  # 模型使用的解释变量
+    model_evar <- object$evar  
     pred_cols  <- colnames(newdata)
     missing_vars <- setdiff(model_evar, pred_cols)
     
@@ -295,7 +298,6 @@ print.coxp.predict <- function(x, ..., n = 10) {
     return(invisible(x))
   }
   
-  # 应用新列名
   colnames(x_df) <- new_colnames
   
   cat("Cox Proportional Hazards Regression\n")
@@ -359,77 +361,314 @@ store.coxp.predict <- function(dataset, object, name = "hr", ...) {
 
 #' @export
 plot.coxp <- function(x, plots = "none", incl = NULL, incl_int = NULL,
-                      conf_lev = 0.95, intercept = FALSE,
-                      shiny = FALSE, custom = FALSE, ...) {
+                      conf_lev = 0.95, intercept = FALSE, lines = "",
+                      nrobs = -1, shiny = FALSE, custom = FALSE, ...) {
+  # 输入验证：检查模型是否有效
   if (is.character(x)) return(x)
-  if (is.empty(plots) || plots == "none") return(invisible())
+  if (is.null(x$model)) {
+    return("** 无效的模型输入：未找到模型数据 **")
+  }
+  
+  # 检查是否有选择的图表类型
+  if (length(plots) == 0 || all(plots == "none")) {
+    if (shiny) {
+      return("** 无图表可显示。请选择图表类型或重新运行计算 **")
+    } else {
+      return(invisible())
+    }
+  }
   
   plot_list <- list()
+  nrCol <- 2  # 默认2列布局
   
+  # 1. 系数图 (coef)
   if ("coef" %in% plots) {
-    # 提取系数和 CI
-    coef_df <- broom::tidy(x$model, conf.int = TRUE, conf.level = conf_lev)
-    coef_df$hr <- exp(coef_df$estimate)
-    coef_df$hr_low <- exp(coef_df$conf.low)
-    coef_df$hr_high <- exp(coef_df$conf.high)
-    coef_df$term <- coef_df$term
-    
-    if (!intercept) {
-      coef_df <- coef_df[!grepl("Intercept", coef_df$term), ]
-    }
-    
-    if (length(incl) > 0) {
-      incl_regex <- paste0("^(", paste(incl, collapse = "|"), ")")
-      coef_df <- coef_df[grepl(incl_regex, coef_df$term), ]
-    }
-    
-    if (nrow(coef_df) == 0) {
-      plot_list[["coef"]] <- "** No coefficients to plot **"
-    } else {
-      p <- ggplot(coef_df, aes(x = term, y = hr, ymin = hr_low, ymax = hr_high)) +
-        geom_pointrange() +
-        geom_hline(yintercept = 1, linetype = "dashed", color = "red") +
-        scale_x_discrete(limits = rev) +
-        coord_flip() +
-        labs(x = "", y = "Hazard Ratio (HR)", title = "Coefficient Plot (HR)")
-      plot_list[["coef"]] <- p
-    }
+    nrCol <- 1
+    tryCatch({
+      coef_df <- broom::tidy(x$model, conf.int = TRUE, conf.level = conf_lev)
+      coef_df$hr <- exp(coef_df$estimate)
+      coef_df$hr_low <- exp(coef_df$conf.low)
+      coef_df$hr_high <- exp(coef_df$conf.high)
+      
+      # 处理包含的变量
+      if (length(incl) > 0) {
+        incl_regex <- paste0("^(", paste(incl, collapse = "|"), ")")
+        coef_df <- coef_df[grepl(incl_regex, coef_df$term), ]
+      }
+      
+      if (nrow(coef_df) == 0) {
+        plot_list[["coef"]] <- "** 无系数可绘制（可能已排除所有变量）**"
+      } else {
+        p <- ggplot2::ggplot(coef_df, 
+                             ggplot2::aes(x = .data$term, y = .data$hr, 
+                                          ymin = .data$hr_low, ymax = .data$hr_high)) +
+          ggplot2::geom_pointrange() +
+          ggplot2::geom_hline(yintercept = 1, linetype = "dashed", color = "red") +
+          ggplot2::scale_x_discrete(limits = rev(coef_df$term)) +
+          ggplot2::coord_flip() +
+          ggplot2::labs(x = "", y = "风险比 (HR)", 
+                        title = "Cox模型系数（风险比）") +
+          ggplot2::theme(axis.text.y = ggplot2::element_text(hjust = 0))
+        plot_list[["coef"]] <- p
+      }
+    }, error = function(e) {
+      plot_list[["coef"]] <- paste0("** 系数图生成失败：", e$message, " **")
+    })
   }
   
+  # 2. 分布直方图 (dist)
   if ("dist" %in% plots) {
-    data <- x$model$model
-    vars <- c(x$time, x$status, x$evar)
-    for (v in vars) {
-      if (v %in% colnames(data)) {
-        p <- visualize(data, xvar = v, bins = 30, custom = TRUE)
-        plot_list[[paste0("dist_", v)]] <- p
+    tryCatch({
+      dist_data <- x$model$model
+      if (is.null(x$evar) || length(x$evar) == 0) {
+        plot_list[["dist"]] <- "** 无解释变量可绘制分布 **"
+      } else {
+        for (v in x$evar) {
+          if (v %in% colnames(dist_data)) {
+            p <- visualize(dist_data, xvar = v, bins = 10, custom = TRUE) + 
+              ggplot2::labs(title = paste(v, "的分布"))
+            plot_list[[paste0("dist_", v)]] <- p
+          } else {
+            plot_list[[paste0("dist_", v)]] <- paste0("** 变量 ", v, " 不在数据中 **")
+          }
+        }
       }
-    }
+    }, error = function(e) {
+      plot_list[["dist_error"]] <- paste0("** 分布图生成失败：", e$message, " **")
+    })
   }
   
+  # 3. 特征重要性图 (vip)
   if ("vip" %in% plots) {
-    coef_df <- broom::tidy(x$model)
-    coef_df$Importance <- abs(coef_df$estimate)
-    coef_df <- coef_df[order(coef_df$Importance, decreasing = TRUE), ]
-    p <- visualize(coef_df, xvar = "term", yvar = "Importance", type = "bar", custom = TRUE) +
-      coord_flip() + labs(title = "Variable Importance (|coef|)")
-    plot_list[["vip"]] <- p
+    nrCol <- 1
+    tryCatch({
+      if (length(x$evar) < 2) {
+        plot_list[["vip"]] <- "** 至少需要2个变量才能生成特征重要性图 **"
+      } else {
+        coef_df <- broom::tidy(x$model)
+        if (!"statistic" %in% colnames(coef_df)) {
+          plot_list[["vip"]] <- "** 无法获取统计量，无法生成特征重要性图 **"
+        } else {
+          coef_df$Importance <- abs(coef_df$statistic)  # 使用z统计量
+          coef_df <- coef_df[order(coef_df$Importance, decreasing = TRUE), ]
+          
+          p <- visualize(coef_df, xvar = "term", yvar = "Importance", 
+                         type = "bar", custom = TRUE) +
+            ggplot2::coord_flip() + 
+            ggplot2::labs(title = "特征重要性", x = "", y = "重要性") +
+            ggplot2::theme(axis.text.y = ggplot2::element_text(hjust = 0))
+          plot_list[["vip"]] <- p
+        }
+      }
+    }, error = function(e) {
+      plot_list[["vip"]] <- paste0("** 特征重要性图生成失败：", e$message, " **")
+    })
+  }
+  
+  # 4. 生存曲线图 (survival)
+  if ("survival" %in% plots) {
+    nrCol <- 1
+    tryCatch({
+      # 检查时间和事件变量是否存在
+      if (is.null(x$time_var) || is.null(x$status_var)) {
+        plot_list[["survival"]] <- "** 模型缺少时间变量或事件变量，无法生成生存曲线 **"
+      } else if (!x$time_var %in% colnames(x$model$model)) {
+        plot_list[["survival"]] <- paste0("** 时间变量 '", x$time_var, "' 不在模型数据中 **")
+      } else if (!x$status_var %in% colnames(x$model$model)) {
+        plot_list[["survival"]] <- paste0("** 事件变量 '", x$status_var, "' 不在模型数据中 **")
+      } else {
+        # 检查是否有事件发生
+        event_count <- sum(x$model$model[[x$status_var]] == 1, na.rm = TRUE)
+        if (event_count == 0) {
+          plot_list[["survival"]] <- "** 数据中无事件发生（status全为0），无法生成生存曲线 **"
+        } else {
+          # 计算生存曲线
+          surv_fit <- survival::survfit(x$model, conf.int = TRUE, conf.lev = conf_lev)
+          if (is.null(surv_fit) || length(surv_fit$time) == 0) {
+            plot_list[["survival"]] <- "** 无法计算生存曲线，请检查模型和数据 **"
+          } else {
+            # 构建生存数据框
+            surv_df <- data.frame(
+              time = surv_fit$time,
+              surv = surv_fit$surv,
+              lower = ifelse(exists("lower", surv_fit), surv_fit$lower, NA),
+              upper = ifelse(exists("upper", surv_fit), surv_fit$upper, NA)
+            )
+            
+            p <- ggplot2::ggplot(surv_df, ggplot2::aes(x = .data$time, y = .data$surv)) +
+              ggplot2::geom_line(color = "blue", linewidth = 1) +
+              ggplot2::geom_ribbon(ggplot2::aes(ymin = .data$lower, ymax = .data$upper),
+                                   alpha = 0.2, fill = "blue", na.rm = TRUE) +
+              ggplot2::labs(title = "基线生存函数",
+                            x = "时间", y = "生存概率") +
+              ggplot2::ylim(0, 1)
+            plot_list[["survival"]] <- p
+          }
+        }
+      }
+    }, error = function(e) {
+      plot_list[["survival"]] <- paste0("** 生存曲线生成失败：", e$message, " **")
+    })
+  }
+  
+  # 5. 累积风险图 (cumhaz)
+  if ("cumhaz" %in% plots) {
+    nrCol <- 1
+    tryCatch({
+      # 检查时间和事件变量是否存在
+      if (is.null(x$time_var) || is.null(x$status_var)) {
+        plot_list[["cumhaz"]] <- "** 模型缺少时间变量或事件变量，无法生成累积风险曲线 **"
+      } else if (!x$time_var %in% colnames(x$model$model)) {
+        plot_list[["cumhaz"]] <- paste0("** 时间变量 '", x$time_var, "' 不在模型数据中 **")
+      } else if (!x$status_var %in% colnames(x$model$model)) {
+        plot_list[["cumhaz"]] <- paste0("** 事件变量 '", x$status_var, "' 不在模型数据中 **")
+      } else {
+        # 检查是否有事件发生
+        event_count <- sum(x$model$model[[x$status_var]] == 1, na.rm = TRUE)
+        if (event_count == 0) {
+          plot_list[["cumhaz"]] <- "** 数据中无事件发生（status全为0），无法生成累积风险曲线 **"
+        } else {
+          # 计算生存曲线用于转换为累积风险
+          surv_fit <- survival::survfit(x$model)
+          if (is.null(surv_fit) || length(surv_fit$time) == 0) {
+            plot_list[["cumhaz"]] <- "** 无法计算累积风险，请检查模型和数据 **"
+          } else {
+            # 构建累积风险数据框，处理surv=0的情况
+            cumhaz_df <- data.frame(
+              time = surv_fit$time,
+              cumhaz = ifelse(surv_fit$surv > 0 & !is.na(surv_fit$surv), 
+                              -log(surv_fit$surv), NA)
+            )
+            
+            # 检查是否有有效数据
+            valid_rows <- sum(!is.na(cumhaz_df$cumhaz))
+            if (valid_rows < 2) {
+              plot_list[["cumhaz"]] <- "** 有效数据不足，无法生成累积风险曲线 **"
+            } else {
+              p <- ggplot2::ggplot(cumhaz_df, ggplot2::aes(x = .data$time, y = .data$cumhaz)) +
+                ggplot2::geom_line(color = "red", linewidth = 1, na.rm = TRUE) +
+                ggplot2::labs(title = "基线累积风险函数",
+                              x = "时间", y = "累积风险")
+              plot_list[["cumhaz"]] <- p
+            }
+          }
+        }
+      }
+    }, error = function(e) {
+      plot_list[["cumhaz"]] <- paste0("** 累积风险曲线生成失败：", e$message, " **")
+    })
   }
   
-  if ("pdp" %in% plots || "pred_plot" %in% plots) {
-    plot_list[["pdp"]] <- "** PDP not yet implemented for Cox **"
+  # 6. Schoenfeld残差图 (schoenfeld)
+  if ("schoenfeld" %in% plots) {
+    nrCol <- 1
+    tryCatch({
+      # 检查事件变量是否存在
+      if (is.null(x$status_var)) {
+        plot_list[["schoenfeld"]] <- "** 模型缺少事件变量，无法计算Schoenfeld残差 **"
+      } else if (!x$status_var %in% colnames(x$model$model)) {
+        plot_list[["schoenfeld"]] <- paste0("** 事件变量 '", x$status_var, "' 不在模型数据中 **")
+      } else {
+        # 检查事件数是否足够（至少2个）
+        event_count <- sum(x$model$model[[x$status_var]] == 1, na.rm = TRUE)
+        if (event_count < 2) {
+          plot_list[["schoenfeld"]] <- "** 事件数不足（至少需要2个事件），无法检验PH假设 **"
+        } else {
+          # 计算Schoenfeld残差
+          schoenfeld <- survival::cox.zph(x$model)
+          if (is.null(schoenfeld) || is.null(schoenfeld$time) || is.null(schoenfeld$y)) {
+            plot_list[["schoenfeld"]] <- "** 无法计算Schoenfeld残差（可能PH假设严重违反）**"
+          } else {
+            # 处理单变量和多变量情况
+            residual_vec <- if (is.matrix(schoenfeld$y)) {
+              schoenfeld$y[, 1]  # 多变量时取第一列
+            } else {
+              schoenfeld$y       # 单变量时直接使用向量
+            }
+            
+            # 构建残差数据框
+            resid_df <- data.frame(
+              time = schoenfeld$time,
+              residual = residual_vec
+            )
+            
+            # 检查有效数据
+            if (nrow(resid_df) < 2 || all(is.na(resid_df$residual))) {
+              plot_list[["schoenfeld"]] <- "** 有效残差不足，无法生成Schoenfeld图 **"
+            } else {
+              p <- ggplot2::ggplot(resid_df, 
+                                   ggplot2::aes(x = .data$time, y = .data$residual)) +
+                ggplot2::geom_point(alpha = 0.6, na.rm = TRUE) +
+                ggplot2::geom_smooth(method = "loess", color = "red", se = TRUE, na.rm = TRUE) +
+                ggplot2::geom_hline(yintercept = 0, linetype = "dashed") +
+                ggplot2::labs(title = "Schoenfeld残差 - 检验PH假设",
+                              x = "时间", y = "标准化Schoenfeld残差")
+              plot_list[["schoenfeld"]] <- p
+            }
+          }
+        }
+      }
+    }, error = function(e) {
+      plot_list[["schoenfeld"]] <- paste0("** Schoenfeld残差图生成失败：", e$message, " **")
+    })
   }
   
-  if ("influence" %in% plots) {
-    plot_list[["influence"]] <- "** Influence plot not yet implemented **"
+  # 7. 鞅残差图 (martingale)
+  if ("martingale" %in% plots) {
+    nrCol <- 1
+    tryCatch({
+      # 计算鞅残差和线性预测值
+      martingale_resid <- residuals(x$model, type = "martingale")
+      linear_pred      <- predict(x$model, type = "lp")
+      if (is.null(martingale_resid) || is.null(linear_pred)) {
+        plot_list[["martingale"]] <- "** 模型无法计算残差或线性预测值（可能拟合失败）**"
+      } else if (length(martingale_resid) != length(linear_pred)) {
+        plot_list[["martingale"]] <- "** 残差与线性预测值长度不匹配，无法绘图 **"
+      } else {
+        resid_df <- data.frame(linear_pred = linear_pred,
+                               martingale  = martingale_resid) %>%
+          stats::na.omit()
+        if (nrow(resid_df) < 10) {
+          plot_list[["martingale"]] <- paste0("** 有效数据点不足（仅", nrow(resid_df), "个），无法生成有意义的图 **")
+        } else {
+          p <- ggplot2::ggplot(resid_df,
+                               ggplot2::aes(x = .data$linear_pred, y = .data$martingale)) +
+            ggplot2::geom_point(alpha = 0.6) +
+            ggplot2::labs(title = "鞅残差 vs 线性预测值",
+                          x = "线性预测值", y = "鞅残差")
+          
+          if ("line" %in% lines)
+            p <- p + ggplot2::geom_smooth(method = "lm", se = FALSE,
+                                          color = "red", linewidth = 1.2)
+          
+          if ("loess" %in% lines)
+            p <- p + ggplot2::geom_smooth(method = "loess", color = "blue", se = TRUE)
+          
+          if ("jitter" %in% lines)
+            p <- p + ggplot2::geom_jitter(width = 0.2, height = 0.2,
+                                          alpha = 0.3, color = "darkgreen")
+          
+          plot_list[["martingale"]] <- p
+        }
+      }
+    }, error = function(e) {
+      plot_list[["martingale"]] <- paste0("** 鞅残差图生成失败：", e$message, " **")
+    })
   }
   
-  # 输出
-  if (length(plot_list) == 0) return(invisible())
+  # 检查是否有可显示的图
+  if (length(plot_list) == 0) {
+    return("** 无法生成任何图表。请检查模型设置和数据有效性 **")
+  }
+  
+  # 输出图表
   if (custom) {
-    if (length(plot_list) == 1) return(plot_list[[1]]) else return(plot_list)
+    if (length(plot_list) == 1) plot_list[[1]] else plot_list
   } else {
-    patchwork::wrap_plots(plot_list, ncol = 2) %>%
-      (function(x) if (isTRUE(shiny)) x else print(x))
+    if (length(plot_list) == 1) {
+      plot_list[[1]]
+    } else {
+      patchwork::wrap_plots(plot_list, ncol = nrCol)
+    }
   }
 }
\ No newline at end of file
diff --git a/radiant.model/inst/app/tools/analysis/coxp_ui.R b/radiant.model/inst/app/tools/analysis/coxp_ui.R
index fe9c2a25fa7c3ef656e1802f9b04d9a204ac0960..9aaad93383a8a6da62a8445149b73ffe13991ac4 100644
--- a/radiant.model/inst/app/tools/analysis/coxp_ui.R
+++ b/radiant.model/inst/app/tools/analysis/coxp_ui.R
@@ -8,11 +8,11 @@ coxp_lines <- setNames(c("line", "loess", "jitter"),
                        c(i18n$t("Line"), i18n$t("Loess"), i18n$t("Jitter")))
 
 coxp_plots <- setNames(
-  c("none", "dist", "vip", "pred_plot", "pdp", "coef", "influence"),
+  c("none", "dist", "vip", "coef", "survival", "cumhaz", "schoenfeld", "martingale"),
   c(i18n$t("None"), i18n$t("Distribution"), 
-    i18n$t("Permutation Importance"), i18n$t("Prediction plots"),
-    i18n$t("Partial Dependence"), i18n$t("Coefficient plot"), 
-    i18n$t("Influential observations"))
+    i18n$t("Permutation Importance"), i18n$t("Coefficient plot"),
+    i18n$t("Survival curves"), i18n$t("Cumulative hazard"),
+    i18n$t("Schoenfeld residuals"), i18n$t("Martingale residuals"))
 )
 
 ## 2. 参数收集 -------------------------------------------------------------
@@ -34,7 +34,41 @@ coxp_inputs <- reactive({
   args
 })
 
-coxp_plot_inputs <- reactive({ list() })
+coxp_plot_inputs <- reactive({
+  args <- list()
+
+  # 传递用户设置的置信水平
+  args$conf_lev <- input$coxp_conf_lev %||% 0.95
+  
+  # 1. 处理 plots 参数
+  if (is.empty(input$coxp_plots, "none")) {
+    args$plots <- character(0) 
+  } else {
+    args$plots <- c(input$coxp_plots) 
+  }
+  
+  # 2. 处理 incl 参数 (用于 coef)
+  if (input$coxp_plots == "coef") {
+    args$incl <- input$coxp_incl
+    args$intercept <- input$coxp_intercept 
+  }
+  
+  # 3. 处理 lines 参数 (用于 martingale)
+  if (input$coxp_plots == "martingale") {
+    args$lines <- input$coxp_lines
+  }
+  
+  # 4. 处理 nrobs 参数
+  if (input$coxp_plots %in% c("dist", "martingale")) {
+    args$nrobs <- input$coxp_nrobs
+  }
+  
+  # 5. 添加 shiny 标志
+  args$shiny <- TRUE
+  
+  return(args)
+})
+
 
 coxp_pred_inputs <- reactive({
   args <- coxp_pred_args
@@ -99,27 +133,6 @@ output$ui_coxp_incl <- renderUI({
   )
 })
 
-output$ui_coxp_incl_int <- renderUI({
-  req(available(input$coxp_evar))
-  choices <- character(0)
-  vars <- input$coxp_evar
-  if (length(vars) > 1) {
-    choices <- iterms(vars, 2)
-  } else {
-    updateSelectInput(session, "coxp_incl_int", choices = choices, selected = choices)
-    return()
-  }
-  selectInput(
-    "coxp_incl_int",
-    label = i18n$t("2-way interactions to explore:"),
-    choices = choices,
-    selected = state_multiple("coxp_incl_int", choices),
-    multiple = TRUE,
-    size = min(8, length(choices)),
-    selectize = FALSE
-  )
-})
-
 ## 5. 预测 / 绘图 / 刷新按钮 ----------------------------------------------
 observeEvent(input$dataset, {
   updateSelectInput(session, "coxp_predict", selected = "none")
@@ -187,15 +200,10 @@ output$ui_coxp <- renderUI({
         selectInput("coxp_plots", i18n$t("Plots:"), choices = coxp_plots,
                     selected = state_single("coxp_plots", coxp_plots)),
         conditionalPanel(
-          condition = "input.coxp_plots == 'coef' || input.coxp_plots == 'pdp' || input.coxp_plots == 'pred_plot'",
-          uiOutput("ui_coxp_incl"),
-          conditionalPanel(
-            condition = "input.coxp_plots == 'coef'",
-            checkboxInput("coxp_intercept", i18n$t("Include intercept"), state_init("coxp_intercept", FALSE))
-          ),
-          conditionalPanel(
-            condition = "input.coxp_plots == 'pdp' | input.coxp_plots == 'pred_plot'",
-            uiOutput("ui_coxp_incl_int")
+          condition = "input.coxp_plots == 'martingale'",
+          checkboxGroupInput(
+            "coxp_lines", i18n$t("Add lines:"), coxp_lines,
+            selected = state_group("coxp_lines", "loess")
           )
         )
       )
@@ -214,30 +222,34 @@ coxp_plot <- reactive({
   if (is.empty(input$coxp_plots, "none")) return()
   plot_width <- 650
   plot_height <- 500
-  nr_vars <- length(input$coxp_evar) + 1
-  
-  if (input$coxp_plots == "dist") {
-    plot_height <- (plot_height / 2) * ceiling(nr_vars / 2)
-  } else if (input$coxp_plots == "dashboard") {
-    plot_height <- 1.5 * plot_height
-  } else if (input$coxp_plots == "correlations") {
-    plot_height <- 150 * nr_vars
-    plot_width <- 150 * nr_vars
-  } else if (input$coxp_plots == "coef") {
-    incl <- paste0("^(", paste0(input$coxp_incl, "[|]*", collapse = "|"), ")")
-    nr_coeff <- sum(grepl(incl, .coxp()$coeff$label))
-    plot_height <- 300 + 20 * nr_coeff
-  } else if (input$coxp_plots %in% c("scatter", "resid_pred")) {
-    plot_height <- (plot_height / 2) * ceiling((nr_vars - 1) / 2)
-  } else if (input$coxp_plots == "vip") {
-    plot_height <- max(500, 30 * nr_vars)
-  } else if (input$coxp_plots %in% c("pdp", "pred_plot")) {
-    nr_vars <- length(input$coxp_incl) + length(input$coxp_incl_int)
-    plot_height <- max(250, ceiling(nr_vars / 2) * 250)
-    if (length(input$coxp_incl_int) > 0) {
-      plot_width <- plot_width + min(2, length(input$coxp_incl_int)) * 90
-    }
-  }
+  nr_vars <- length(input$coxp_evar)
+  switch(input$coxp_plots,
+         "dist" = {
+           plot_height <- (plot_height / 2) * ceiling((nr_vars + 2) / 2)  # +2 for time/status
+         },
+         "coef" = {
+           incl <- paste0("^(", paste0(input$coxp_incl, "[|]*", collapse = "|"), ")")
+           nr_coeff <- if (is.empty(input$coxp_incl)) nr_vars else length(input$coxp_incl)
+           plot_height <- 300 + 20 * nr_coeff
+         },
+         "vip" = {
+           plot_height <- max(500, 30 * nr_vars)
+         },
+         "survival" = {
+           plot_height <- 400
+           plot_width <- 600
+         },
+         "cumhaz" = {
+           plot_height <- 400
+           plot_width <- 600
+         },
+         "schoenfeld" = {
+           plot_height <- 400
+         },
+         "martingale" = {
+           plot_height <- 400
+         }
+  )
   list(plot_width = plot_width, plot_height = plot_height)
 })
 
@@ -398,7 +410,7 @@ coxp_available <- reactive({
   if (not_pressed(input$coxp_run)) return(i18n$t("** Press the Estimate button to estimate the model **"))
   if (is.empty(input$coxp_plots, "none")) return(i18n$t("Please select a plot from the drop-down menu"))
   if (coxp_available() != "available") return(coxp_available())
-  valid_plots <- c("coef", "dist", "vip", "pdp", "pred_plot", "influence")
+  valid_plots <- c("dist", "vip", "coef", "survival", "cumhaz", "schoenfeld", "martingale")
   if (!input$coxp_plots %in% valid_plots) {
     return(i18n$t("Selected plot type is not supported for Cox models."))
   }
@@ -406,7 +418,7 @@ coxp_available <- reactive({
     check_for_pdp_pred_plots("coxp")
   }
   withProgress(message = i18n$t("Generating plots"), value = 1, {
-    do.call(plot, c(list(x = .coxp()), coxp_plot_inputs(), shiny = TRUE))
+    do.call(plot, c(list(x = .coxp()), coxp_plot_inputs()))
   })
 })
 
@@ -417,7 +429,11 @@ coxp_report <- function() {
   inp_out <- list(list(prn = TRUE), "")
   figs <- FALSE
   if (!is.empty(input$coxp_plots, "none")) {
-    inp <- check_plot_inputs(coxp_plot_inputs())
+    plot_inp <- coxp_plot_inputs()
+    if (is.null(plot_inp$plots) || length(plot_inp$plots) == 0) {
+      plot_inp$plots <- ""
+    }
+    inp <- check_plot_inputs(plot_inp)
     inp_out[[2]] <- clean_args(inp, list())
     inp_out[[2]]$custom <- FALSE
     outputs <- c(outputs, "plot")