Linearity of expectation和LOTUS这俩用到的证明思路类似： Linearity of expectation 🔗 🔗References: (reading in the following order is highly recommended) [1] Harvard Statistics110 Lecture 10: 5-11min [2] Harvard Statistics110 Lecture 14: 42-48min [3] Standford CS109

卧姿动作 🔗动作1 静态死虫 🔗 先自然平躺，脚尖向前，屈膝，夹球，膝盖尽量靠近胸 在保证腰始终压住地面的情况下，缓慢使大腿与地面垂直，小腿与地面平行

准备工作 🔗复现发表在nature的Europeans’ support for refugees of varying background is stable over time里的Extended Data Fig. 4。原图如下： 文献提供了原始数据集，

Markdown部分 🔗1. 给标题加label 🔗### 总结 {#summary} 如果需要在其他地方引用标题，则可以选择给标题增加label. 可以在其他地方引用。例

总结对登记库数据进行EDA的workflow。总结主要有几个点： 源数据的清洗； 派生变量的生成； 数据异常的探查； Table one的制作； 可视化。 首先第

准备工作 🔗复现七月发表在nature的Like-minded sources on Facebook are prevalent but not polarizing里的figure2 和figure3。原图如下：

Introduction 🔗Tense pairs 🔗Present Simple versus Past Simple Present simple: describe facts Past simple: describe findings relating to your own reserach For example, we found that the CHD risk increased as individuals got older. Present simple sounds stronger than past simple. Past Simple versus Present Perfect Past simple: describe what happened in the past (a snapshot) Present perfect: describe what happened in the past and the continuing effect Little (virtually none) attention was paid to this issue (sentence 1) Little attention has been paid to this issue (sentence 2) Sentence 1 means attension was not paid at some point in the past, and that’s it.

学习总结利用DT包和r2rtf包对表格进行自定义格式： 高亮行列、单元格；字体加粗/斜体; 单元格靠左/右/居中。两个包的应用场景不同。其中，D

零零碎碎的一些笔记，趁着整理文件把它们归档一下🧚‍♀️ R/统计相关 🔗 bookdown中使用zotero进行文献管理操作指南 写CV时可能用到的

R program for randomization in clinical trials, including simple, blocked, stratified, stratified-blocked, and dynamic randomization. Most R packages for randomization requires complete information about the all study samples (for example, covariate values). However, in real-world setting, patients enrollment is usually conducted during a period of time, so complete information is not available. One advantage of this R program is it allows patients information to be added incementally and assign corresponding assignment for newly enrolled study samples.

工作中的一个项目用到了LSTM，对时间序列数据进行预测。算法学习笔记记录备参考。

For predictive algorithms, assessing model performance is critical. The criteria used to measure the model performance include the area under the curve (AUC) and average positive predictive value (AP), brier score and scaled brier score. However, most studies only provide point estimates of these performance metrics. As mandatory reporting of confidence intervals becomes increasingly popular in medical studies, it is crucial to construct confidence intervals for the performance estimates. There are three popular approaches, including split-sample averaged (SSA), cross-validation (CV) and bootstrapping (BS), for estimating the confidence intervals for those performance metrics.

I was looking for techniques for dealing with missing data and came across a research paper titled: Dynamic model updating (DMU) approach for statistical learning model building with missing data. My notes are archived here.

Step 1: set up server 🔗Step 2: 制作docker镜像 🔗可以理解为将需要的文件进行组合打包。主要利用dockerfile进行制作（安装opencpu, R

An overview of data types The following is a super detailed table summarizing different data types and objects in R. I found this in tibble. However, you can just pay most attention to the commonly used ones:) Class Data type Example Column header Atomic logical TRUE lgl integer 1L int double 1.5 dbl character "A" chr complex 0+1i cpl raw as.raw(1) raw list list(1) list named list list(a = 1) named list Built-in objects factor factor("