本文首发于公众号：医学和生信笔记

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医学和生信笔记，专注R语言在临床医学中的使用，R语言数据分析和可视化。主要分享R语言做医学统计学、meta分析、网络药理学、临床预测模型、机器学习、生物信息学等。

前面介绍了使用tidymodels画校准曲线，不知道大家学会了没？

众所周知，tidymodels目前还不支持一键绘制校准曲线！相同类型的mlr3也是不支持的！大家多去github提issue，加速对校准曲线的支持！

今天介绍mlr3怎么画校准曲线，还是那句话，校准曲线就是散点图，你非说是折线图也行......

加载R包

首先还是加载数据和R包，和之前的数据一样的。

library(mlr3verse)## Loading required package: mlr3library(mlr3pipelines)library(mlr3filters)

建立任务

然后是对数据进行划分训练集和测试集，对数据进行预处理，为了和之前的tidymodels进行比较，这里使用的数据和预处理步骤都是和之前一样的。

# 读取数据all_plays <- readRDS("../000files/all_plays.rds")

# 建立任务pbp_task <- as_task_classif(all_plays, target="play_type")

# 数据划分split_task <- partition(pbp_task, ratio=0.75)

task_train <- pbp_task$clone()$filter(split_task$train)task_test <- pbp_task$clone()$filter(split_task$test)

数据预处理

建立任务后就是建立数据预处理步骤，这里采用和上篇推文tidymodels中一样的预处理步骤：

# 数据预处理pbp_prep <- po("select", # 去掉3列               selector = selector_invert(                 selector_name(c("half_seconds_remaining","yards_gained","game_id")))               ) %>>%  po("colapply", # 把这两列变成因子类型     affect_columns = selector_name(c("posteam","defteam")),     applicator = as.factor) %>>%   po("filter", # 去除高度相关的列     filter = mlr3filters::flt("find_correlation"), filter.cutoff=0.3) %>>%  po("scale", scale = F) %>>% # 中心化  po("removeconstants") # 去掉零方差变量

建立模型

先选择随机森林模型。

rf_glr <- as_learner(pbp_prep %>>% lrn("classif.ranger", predict_type="prob")) rf_glr$id <- "randomForest"

很多人喜欢在训练集中使用10折交叉验证，但其实这对于提高模型表现没什么用~尤其是临床预测模型这个领域~因为你的模型表现好不好很大程度上取决于你的数据好不好！鸭子是不会变成天鹅的

rr <- resample(task = task_train,               learner = rf_glr,               resampling = rsmp("cv",folds = 10),               store_models = T)## INFO  [18:25:28.412] [mlr3] Applying learner 'randomForest' on task 'all_plays' (iter 1/10) ## INFO  [18:25:58.497] [mlr3] Applying learner 'randomForest' on task 'all_plays' (iter 2/10) ## INFO  [18:26:29.302] [mlr3] Applying learner 'randomForest' on task 'all_plays' (iter 3/10) ## INFO  [18:27:02.512] [mlr3] Applying learner 'randomForest' on task 'all_plays' (iter 4/10) ## INFO  [18:27:31.100] [mlr3] Applying learner 'randomForest' on task 'all_plays' (iter 5/10) ## INFO  [18:28:01.090] [mlr3] Applying learner 'randomForest' on task 'all_plays' (iter 6/10) ## INFO  [18:28:30.868] [mlr3] Applying learner 'randomForest' on task 'all_plays' (iter 7/10) ## INFO  [18:29:01.464] [mlr3] Applying learner 'randomForest' on task 'all_plays' (iter 8/10) ## INFO  [18:29:32.870] [mlr3] Applying learner 'randomForest' on task 'all_plays' (iter 9/10) ## INFO  [18:30:03.747] [mlr3] Applying learner 'randomForest' on task 'all_plays' (iter 10/10)

评价模型

先看看在训练集中的表现。

混淆矩阵：

rr$prediction()$confusion##         truth## response  pass   run##     pass 31932  9886##     run   8819 18345

混淆矩阵可视化：

autoplot(rr$prediction())

查看其他结果：

rr$aggregate(msrs(c("classif.auc","classif.acc","classif.bbrier")))##    classif.auc    classif.acc classif.bbrier ##      0.7979179      0.7288424      0.1790592

喜闻乐见ROC曲线：

autoplot(rr,type = "roc")

喜闻乐见的prc曲线：

autoplot(rr, type = "prc")

箱线图：

autoplot(rr, measure = msr("classif.auc"))

以上所有介绍的图形和评价方法都在之前的推文详细介绍过了~不会的赶紧翻看：mlr3实现多个模型评价和比较

训练集的校准曲线

先画训练集的校准曲线，毫无难度，看不懂的可以加群一起讨论~

prediction <- as.data.table(rr$prediction())head(prediction)##    row_ids truth response prob.pass   prob.run## 1:       6   run      run 0.4294702 0.57052982## 2:      30  pass     pass 0.7730236 0.22697638## 3:      48   run      run 0.2052662 0.79473378## 4:      94  pass     pass 0.6593303 0.34066970## 5:     106  pass     pass 0.5731238 0.42687625## 6:     108  pass     pass 0.9365055 0.06349447

方法1：

suppressPackageStartupMessages(library(tidyverse))library(ggsci)

calibration_df <- prediction %>%    mutate(pass = if_else(truth == "pass", 1, 0),          pred_rnd = round(prob.pass, 2)          ) %>%   group_by(pred_rnd) %>%   summarize(mean_pred = mean(prob.pass),            mean_obs = mean(pass),            n = n()            )

ggplot(calibration_df, aes(mean_pred, mean_obs))+   geom_point(aes(size = n), alpha = 0.5)+  scale_color_lancet()+  geom_abline(linetype = "dashed")+  labs(x="Predicted Probability", y= "Observed Probability")+  theme_minimal()

第2种方法，大家比较喜欢的折线图！

cali_df <- prediction %>%   arrange(prob.pass) %>%   mutate(pass = if_else(truth == "pass", 1, 0),         group = c(rep(1:100,each=680), rep(101,982))         ) %>%   group_by(group) %>%   summarise(mean_pred = mean(prob.pass),            mean_obs = mean(pass)            )

ggplot(cali_df, aes(mean_pred, mean_obs))+   geom_line(size=1)+  labs(x="Predicted Probability", y= "Observed Probability")+  theme_minimal()

是不是和上一篇中的tidymodels画出来的一模一样？没错，就是一样的，就是这么简单，想怎么画就怎么画 ！

训练集的校准曲线

先把模型用在测试集上，得到预测结果，然后画图！

cv_pred <- rf_glr$train(task_train)$predict(task_test)

cv_pred_df <- as.data.table(cv_pred)head(cv_pred_df)

   row_ids truth response prob.pass  prob.run1:       2  pass      run 0.4213731 0.57862692:       5  pass     pass 0.8475027 0.15249733:       6   run      run 0.3782730 0.62172704:      12  pass     pass 0.6308144 0.36918565:      14  pass     pass 0.8371294 0.16287066:      15   run      run 0.1837391 0.8162609

先画个喜闻乐见的校准曲线：

cali_df <- cv_pred_df %>%   arrange(prob.pass) %>%   mutate(pass = if_else(truth == "pass", 1, 0),         group = c(rep(1:100,each=229), rep(101,94))         ) %>%   group_by(group) %>%   summarise(mean_pred = mean(prob.pass),            mean_obs = mean(pass)            )

ggplot(cali_df, aes(mean_pred, mean_obs))+   geom_line(size=1)+  labs(x="Predicted Probability", y= "Observed Probability")+  theme_minimal()

另一种颜值高点的校准曲线，给你点颜色瞧瞧！

calibration_df <- cv_pred_df %>%    mutate(pass = if_else(truth == "pass", 1, 0),          pred_rnd = round(prob.pass, 2)          ) %>%   group_by(pred_rnd) %>%   summarize(mean_pred = mean(prob.pass),            mean_obs = mean(pass),            n = n()            ) %>%   mutate(group = case_when(n < 100 ~ "<100",                           n < 200 ~ "<200",                           n < 300 ~ "<300",                           n < 400 ~ "<400",                           TRUE ~ "≥400"                           ))## Error in mutate(., pass = if_else(truth == "pass", 1, 0), pred_rnd = round(prob.pass, : object 'cv_pred_df' not found

ggplot(calibration_df, aes(mean_pred, mean_obs))+   geom_point(aes(size = n, color = group))+  scale_color_jama()+  geom_abline(linetype = "dashed")+  labs(x="Predicted Probability", y= "Observed Probability")+  theme_minimal()## Error in FUN(X[[i]], ...): object 'group' not found

配色略诡异...

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校准曲线，你学会了吗？

我知道并没有，比如，多条画一起怎么搞？生存资料的怎么搞？

关于这两个问题，可以翻看我之前的推文

加群即可免费获得今日示例数据！

本文首发于公众号：医学和生信笔记

“

医学和生信笔记，专注R语言在临床医学中的使用，R语言数据分析和可视化。主要分享R语言做医学统计学、meta分析、网络药理学、临床预测模型、机器学习、生物信息学等。