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一步一步教你做转录组分析(HISAT, StringTie andBallgown)该分析流程主要根据2016年发表在Nature Prot ocols上的一篇名为Transcript-level expressionanalysis of RNA-seq experiments with HISAT,StringTie and Ballgown的文章撰写的,主要用到以下三个软件:HISAT (http://ccb.jhu.edu/software/hisat/index.shtml)利用大量FM索引,以覆盖整个基因组,能够将RNA-Seq的读取与基因组进行快速比对,相较于STAR、Tophat,该软件比对速度快,占用内存少。
StringTie(http://ccb.jhu.edu/software/stringtie/)能够应用流神经网络算法和可选的de novo组装进行转录本组装并预计表达水平。
与Cufflin ks等程序相比,StringTie实现了更完整、更准确的基因重建,并更好地预测了表达水平。
Ballgown(https://github.com/alyssafrazee/ballgown)是R语言中基因差异表达分析的工具,能利用RNA-Seq实验的数据(StringTie, RSEM,Cufflinks)的结果预测基因、转录本的差异表达。
然而Ballgown并没有不能很好地检测差异外显子,而DEXseq、rMATS和MISO可以很好解决该问题。
一、数据下载Linux系统下常用的下载工具是wget,但该工具是单线程下载,当使用它下载较大数据时比较慢,所以选择axel,终端中输入安装命令:$sudo yum install axel然后提示输入密码获得root权限后即可自动安装,安装完成后,输入命令axel,终端会显示如下内容,表示安装成功。
Axel工具常用参数有:axel[选项][下载目录][下载地址]-s:指定每秒下载最大比特数-n:指定同时打开的线程数-o:指定本地输出文件-S:搜索镜像并从Xservers服务器下载-N:不使用代理服务器-v:打印更多状态信息-a:打印进度信息-h:该版本命令帮助-V:查看版本信息号#Axel安装成功后在终端中输入命令:$axel ftp://ftb.jhu.edu/pub/RNAseq_protocol/chrX_data.tar.gz此时在终端中会显示如下图信息,如果不想该信息刷屏,添加参数q,采用静默模式即可。
纯生信单细胞数据挖掘-全代码放送考虑到咱们生信技能树粉丝对单细胞数据挖掘的需求,我开通了一个专栏《100个单细胞转录组数据降维聚类分群图表复现》,也亲自示范了几个,不过自己带娃,读博,时间精力有限,所以把剩余的90多个任务安排了学徒,实习生,学员。
真的是太棒了,群策群力!下面是浙江大学博士三年级的小姐姐的投稿做到单细胞转录组数据降维聚类分群为止1. 这次我们要复现的单细胞数据来自International Journal of Cancer 2020年发表的一篇单细胞相关的纯生信文章。
该文章数据链接:GSE1503212. 简单浏览文章,抓取作者针对单细胞数据分析给出的一些参数信息,如下Step 0、加载需要的R包library(Seurat)library(dplyr)library(patchwork)library(mindr)library(Matrix)Step 1、数据准备#文章测序了两个病人的样本,正文figure展示的是其中一个病人的单细胞测序结果。
我们下面的分析使用正文中展示的样本#读入数据并将表达矩阵转成稀疏矩阵,减少数据对空间资源的消耗以及运算资源的消耗GSM4546857<-read.csv("GSM4546857_LSCC01_DBEC_UMI.csv",comment.char = "#")dim(GSM4546857)GSM4546857[1:4,1:4]rownames(GSM4546857)<-GSM4546857$Cell_IndexGSM4546857<-GSM4546857[,-1]GSM4546857<-t(GSM4546857)object.size(GSM4546857)#2331231144 bytesGSM4546857_sparse<-as(as.matrix(GSM4546857),"dgCMatrix")GSM4546857_sparse[1:4,1:4]object.size(GSM4546857_sparse)#166367952 bytessave(GSM4546857_sparse,file = "GSM4546857_sparse.Rdata ")dim(GSM4546857_sparse)在此,可以直观的感受一下两种矩阵的大小Step 2、创建Seurat对象#下面这段代码中,最重要的就是创建Seurat对象以及去除线粒体基因,其他都是对Seurat对象的可视化,其目的在于提高初学者对该对象的了解## =============== 创建Seurat对象tissu1 <- CreateSeuratObject(counts = GSM4546857_sparse , project = "LSCC", min.cells = 3, min.features = 200) tissu1## =============== 去除线粒体基因# The [[ operator can add columns to object metadata. This is a great place to stash QC stats#此次数据检测到大量线粒体基因grep(pattern = "^MT\\.",rownames(tissu1),value = T)tissu1[["percent.mt"]] <- PercentageFeatureSet(tissu1, patte rn = "^MT\\.")head(****************,5)summary(****************$nCount_RNA)看一看过滤前数据情况# Visualize QC metrics as a violin plotVlnPlot(tissu1, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)# densitydata<*****************library(ggplot2)p<-ggplot(data = data,aes(x=nFeature_RNA))+geom_density() p# FeatureScatter is typically used to visualize feature-feature relationships, but can be used# for anything calculated by the object, i.e. columns in obje ct metadata, PC scores etc.plot1 <- FeatureScatter(tissu1, feature1 = "nCount_RNA", fe ature2 = "percent.mt")plot2 <- FeatureScatter(tissu1, feature1 = "nCount_RNA", fe ature2 = "nFeature_RNA")plot1 + plot2这里我们主要展示数据过滤前nFeature_RNA, nCount_RNA, percent.mt的情况#Seurat官网给出的质控标准中,percent.mt < 5,但不同组织不同细胞中线粒体含量存在差异,要根据自己的具体情况进行调整,#但是太夸张啦!太夸张啦!我们这里线粒体基因阈值设置在80%,最终得到的细胞数为9796个,如果设置成95%,得到的细胞数也小于原文中的10699。
summary函数检验异常点和强影响点summary函数是一种常用的统计函数,用于检验异常点和强影响点。
异常点是指在数据集中与其他数据点明显不同的数据点,而强影响点是指对统计结果产生显著影响的数据点。
通过使用summary函数,我们可以对数据集进行快速的异常点和强影响点检验。
在使用summary函数进行异常点检验时,我们可以通过观察数据的最小值、最大值、中位数、平均值和标准差等统计量来判断是否存在异常点。
如果某个数据点的值远离其他数据点的值,或者与其他数据点的值差异较大,那么它很可能是一个异常点。
通过使用summary函数,我们可以方便地获取这些统计量,并进行比较和分析。
除了异常点检验,summary函数还可以用于检验强影响点。
强影响点是指对统计结果产生显著影响的数据点。
在统计分析中,我们通常会计算某个变量与其他变量之间的相关系数或回归系数,以了解它们之间的关系。
如果某个数据点对这些系数的计算结果产生显著影响,那么它可以被认为是一个强影响点。
通过使用summary函数,我们可以计算相关系数或回归系数,并检验是否存在强影响点。
在使用summary函数进行异常点和强影响点检验时,我们需要注意一些问题。
首先,我们需要根据具体的数据集和分析目的来选择合适的统计量进行检验。
不同的统计量可能对异常点和强影响点的检验结果产生不同的影响。
其次,我们需要结合领域知识和经验来判断异常点和强影响点的存在与否。
有时候,某个数据点可能在统计上被认为是异常点或强影响点,但在实际情况下却是合理的。
因此,我们需要综合考虑统计结果和实际情况,做出准确的判断。
总之,summary函数是一种常用的统计函数,可以用于检验异常点和强影响点。
通过观察数据的统计量,我们可以判断是否存在异常点,并通过计算相关系数或回归系数来检验强影响点。
在使用summary函数进行异常点和强影响点检验时,我们需要根据具体情况选择合适的统计量,并结合领域知识和经验做出准确的判断。
SAS中的描述性统计过程(2012-08-01 18:07:01)▼分类:数据分析挖掘标签:杂谈SAS中的描述性统计过程描述性统计指标的计算可以用四个不同的过程来实现,它们分别是means过程、summary过程、univariate过程以及tabulate过程。
它们在功能范围和具体的操作方法上存在一定的差别,下面我们大概了解一下它们的异同点。
相同点:他们均可计算出均数、标准差、方差、标准误、总和、加权值的总和、最大值、最小值、全距、校正的和未校正的离差平方和、变异系数、样本分布位置的t检验统计量、遗漏数据和有效数据个数等,均可应用by语句将样本分割为若干个更小的样本,以便分别进行分析。
不同点:(1)means过程、summary过程、univariate过程可以计算样本的偏度(skewness)和峰度(kurtosis),而tabulate过程不计算这些统计量;(2)univariate过程可以计算出样本的众数(mode),其它三个过程不计算众数;(3)summary过程执行后不会自动给出分析的结果,须引用output语句和print过程来显示分析结果,而其它三个过程则会自动显示分析的结果;(4)univariate过程具有统计制图的功能,其它三个过程则没有;(5)tabulate过程不产生输出资料文件(存储各种输出数据的文件),其它三个均产生输出资料文件。
统计制图的过程均可以实现对样本分布特征的图形表示,一般情况下可以使用的有chart过程、plot过程、gchart过程和gplot过程。
大家有没有发现前两个和后两个只有一个字母‘g’(代表graph)的差别,其实它们之间(只差一个字母g的过程之间)的统计描述功能是相同的,区别仅在于绘制出的图形的复杂和美观程度。
chart过程和plot过程绘制的图形类似于我们用文本字符堆积起来的图形,只能概括地反映出资料分布的大体形状,实际上这两个过程绘制的图形并不能称之为图形,因为他根本就没有涉及一般意义上图形的任何一种元素(如颜色、分辨率等)。
SPSS 专业技术词汇、短语的中英文对照索引% of cases 各类别所占百分比1-tailed 单尾的2 Independent Samples 两个独立样本的检验2 Related Samples 两个相关样本检验2-tailed 双尾的3-D (=dimensional) 三维-->三维散点图AAbove 高于Absolute 绝对的-->绝对值Add 加,添加Add Cases 合并个案Add cases from... 从……加个案Add Variables 合并变量Add variables from... 从……加变量Adj.(=adjusted) standardized 调整后的标准化残差Aggregate 汇总-->分类汇总Aggregate Data 对数据进行分类汇总Aggregate Function 汇总函数Aggregate Variable 需要分类汇总的变量Agreement 协议Align 对齐-->对齐方式Alignment 对齐-->对齐方式All 全部,所有的All cases 所有个案All categories equal 所有类别相等All other values 所有其他值All requested variables entered 所要求变量全部引入Alphabetic 按字母顺序的-->按字母顺序列表Alternative 另外的,备选的Analysis by groups is off 分组分析未开启Analyze 分析-->统计分析Analyze all cases, do not create groups 分析全部个案,不建立分组Annotation 注释ANOV A Table ANOV A表ANOV A table and eta (对分组变量)进行单因素方差分析并计算其η值Apply 应用Apply Data Dictionary 应用数据字典Apply Dictionary 应用数据字典Approximately 大约Approximately X% of all cases 从所有个案中随机选择约X%的个案Approximation 近似估计Area 面积Ascend 上升Ascending counts 按频数的升序排列Ascending means 按均值升值排序Ascending values 按变量值的升序排列Assign 指定,分配Assign Rank 1 to 把秩值1 分配给Assume 假定Asymp. Sig.(=Asymptotic Significance) (2-sided) 双尾渐近显著性检验Automatic 自动的Automatic Recode 自动重新编码Axis 轴Axis Title 横轴名称BBack 返回-->上一步Backward 向后-->向后剔除法Bar chart 条形图Bar Spacing 条形间距Bars Represent 条的长度所代表的统计量Based on negative ranks 基于负秩Based on time or case range 在给定范围内随机选择个案Below 低于Between Groups (Combined) 组间值Binomial 二项分布Bivariate 双变量的-->双变量相关分析Bivariate Correlations 双变量相关分析Both files provide cases 是由外部文件和当前文件二者都提供个案Bottom 底部-->下边框Break Variable 分类变量Brown-Forsythe Brown-Forsythe检验Browse 浏览CCache 贮存Cache Data 数据暂存Calculated from data 根据数据计算Cancel 取消Case 个案Case Label 个案标签Case Processing Summary 个案处理概要Casewise 以个案为单位Casewise diagnostics 个案诊断表Categories 分组数,分类变量,分类模式Categorize Variables 变量分类Category 类别-->单元格数据类型Category Axis 分类轴Cell 单元格Cell Display 单元格显示Cell Percentages 在每个单元格中输出百分比Cell Properties 单元格属性Cell Statistics 单元格中的统计量Center 中心-->居中,中间对齐Central Tendency 集中趋势Change 变化,更改Change Summary 改变汇总函数Chart 图表,图形Chart Type 统计图类型Chart values 作图数据选项Charts 确定生成的图形选项Chi-square test 卡方检验Chi-square(χ2)卡方Choose 选择Choose Destination Location 选择安装路径Classify 分类Clear 清除Close 关闭Cluster 群,组,分组Clustered 分组的-->分组条形图Clustered bar charts 分组条形图Cochran's and Mantel-Haenszel statistics :Cochran 统计量与Mantel-Haenszel 统计量Cochran's Q Cochran Q 检验Code 编码Coefficient 系数Coefficient statistics 系数统计Collinearity 共线性Collinearity diagnostics 共线性诊断Column 列-->单元格中个案数占列总数的百分比Columns 列数-->显示宽度Comma 逗号-->带逗号的数值型变量Common 共同的Compact 最低要求安装Compare 比较,对比Compare groups 分组对比Compare Means 比较平均数Compare variables 比较变量Complete 完成Compute 计算Concordance 和谐Condition 条件Confidence Interval 置信区间Contingency coefficient 列联相关的C 系数Continue 继续Contrast 对比Control 控制Convert 转换Convert numeric strings to numbers 将数值型字符转换为数字Copy 拷贝Copy Objects 拷贝对象Copy old value 拷贝旧变量Correlate 与……相关-->作相关分析Correlation 相关,关联Correlation Coefficient 相关系数Correlations 皮尔逊(Pearson)相关系数Count 个案数,次数,频数Counted value 用于计数的值Covariance 协方差Covariance Matrix 协方差矩阵Covariance ratio 协方差比率Creat new data file 创建新的数据文件Create Time Series 生成时间序列Criteria 标准(复数)Criterion 标准(单数)Cross-product 叉积Cross-product deviation 叉积离差Cross-product deviations and covariances 叉积离差与协方差Crosstab (=Cross-tabulation) 交叉列表Crosstabs 交叉列表(列联表)分析Cum.(=Cumulative) % of cases 累积百分比Cum.(=Cumulative) N of cases 累积频数Cumulative Percent 累计百分比Cumulative Sum 累积和Currency 货币-->货币型变量Current 目前的Current Selections 当前选择Current Settings 目前设置Current Status 目前状态Curve 曲线Curve Estimation 曲线估算Custom 自定义安装Custom Currency 自定义货币记法Cut 剪切Cut point 切分点Cut points for N equal groups 将数据分为N个个案数相同的组的切分点DData 数据-->数据文件的建立与编辑Data in Chart Are 图中数据为(用于统计量模式的选项)Data View 数据(编辑)窗口Database 数据库,数据文件Date 日期-->日期型变量Decimal 小数-->按小数点对齐Decimals 小数位数Define 定义Define Clusters by 以……定义分组(确定分组变量)Define Dates 定义日期Define Dichotomy 确定二分值Define Groups 确定分组Define Ranges 定义范围Define Sets 定义多选变量集Define Simple Bar 定义简单条形图Define Slices by 以……定义扇形块(确定分块变量)Define Variable Ranges 定义变量范围Degree 度,程度Degrees of freedom 自由度Delete 删除Deleted 删除掉-->删除未选个案,将残差删除Dependent 非独立的,依赖的-->因变量Dependent List 因变量/分析变量列表Derive 推导Derived 推导出的Derived axis 转换轴Descend 下降Descending counts 按频数的降序排列Descending means 按均值的降序列表Descending values 按变量值的降序排列Descriptive 描述性的Descriptive Statistics 描述统计Descriptives 描述统计Destination 目标Deviation 偏差,离差df (=degrees of freedom) 自由度Diagnostics 诊断Dialog 对话(框)Dichotomies 二分变量,二分模式Dichotomy 二分法,二分值,二分变量Dictionary 字典Directory 目录Discrete 离散的Discrete missing values 离散缺失值Dispersion 离散趋势Display 显示Display axis line 显示轴线Display chart with case labels 在图中显示个案标签Display clustered bar charts 显示聚类条图Display Data Info 显示数据的基本信息Display derived axis 显示转换轴Display frequencies tables (在输出结果中)显示频数表Display groups defined by missing values 对由缺失值定义的组也加以显示Display labels 显示标签Display legend 显示图例Display normal curve 显示正态曲线Display order 输出结果列表顺序Display summary tables 显示提要表Display the ReadMe ? 是否现在显示软件说明文件Distribution 分布Division 分,除法Do not filter cases 不过滤个案Dollar 元,美元-->带美元符号的数值型变量Dot 圆点,句号-->带圆点的数值型变量Draft output 文本输出文件Drop-line 垂线图Durbin-Watson 系列相关检验,Durbin-Watson 系数EEdit 编辑-->文件编辑Enter 进入-->强行进入法Entry 进入Equal 等于Equal Variances Assumed 等方差假定Equal Variances Not Assumed 不假定等方差Equality 相等Estimate 估计-->估计值Eta η系数(用于测量一个定类变量与一个定比/定距变量之间的相关比率)Eta Squared η2Every 每一个Every N labels 每N个标签Exactly 精确地Exactly M cases from the first N cases 在前N个个案中随机选择M个个案Exclude 排除,拒绝Exclude cases analysis by analysis 剔除分析变量为缺失值的个案Exclude cases listwise 剔除任何含有缺失值的个案Exclude cases listwise within categories 排除分类变量中的缺失值Exclude cases listwise within dichotomies 排除二分变量中的缺失值Exclude cases pairwise 剔除参与相关系数计算的两个变量中有缺失值的个案Exclude cases test-by-test 排除对比中的缺失值Excluded Variables 被拒绝变量Existing 现有的Exit 退出Expect 期望Expected 期望的-->期望频次Expected Range 理论分布范围Expected Value 期望值,理论值Exponential 指数的-->指数分布Export model information to XML file 将模型的信息输出到XML 文件External keyed table 外部文件为关键表Extreme 极端FF F检验的值Factor 因素,因子-->影响因素变量File 文件-->文件操作already sorted 数据文件已排序Filter 过滤-->过滤变量Filter variable 过滤变量Filtered 过滤掉的-->生成过滤变量Find 查找Finish 完成First Case 第一个个案(最小值)First value 第一个观测值Fixed and random effects 确定性影响因素和随机影响因素Flag 旗帜,标记;做标记Flag significant correlations 标出达到显著性水平的相关系数Font 字体Footnote 脚注Format 格式Fraction 比率Frame 框,框架Freedom 自由Frequencies 频数分析,按频数作图Frequency 频率,频数Friedman Friedman检验Function 函数GGallery 美术馆-->图形转换功能Gamma γ等级相关系数General 一般General Linear Model 一般线性模型Get from data 由样本观测值确定Go to Case 查找个案Graph 图形Graphs 统计图Grid 网格Grid line 格线Grid lines 用竖线作刻度标志Group 群,组,分组Group Based on 根据……分组Group Statistics 分组统计分析Grouped Median 分组中位值Grouping Variable 分组变量HHelp 帮助Hidden 隐藏High 最大值Histogram 直方图Homogeneity 同质性-->齐次性Homogeneity of variance 方差齐次性Homogeneity-of-variance 方差齐次性检验Horizontal 水平的,横向的Horizontal Alignment 横向对齐方式Hypothesis 假设IIf condition is satisfied 如果满足一定条件Include 包含,包括Include constant in equation 在方程中包含常数项Increment 增量Independent 独立的-->自变量Independent List 自变量/分组变量列表Independent-Samples T Test 独立样本的T 检验Independent-Samples Test 独立样本检验Indicate 指明,标明Indicate case source as variable 生成一个表明个案来源的变量Individual 单个,个体Influence 影响Influence Statistics 影响因素统计量Info (=Information) 信息Information 信息Inner 里面的Inner Frame 内框-->给图形增加内框Input Variable 输入变量Insert 插入Inside 在(某范围)之内Install (动) 安装Installation (名) 安装Into Different Variables 用重新编码的变量生成一个新变量Into Same Variables 用重新编码的变量取代原变量JJustification 对齐方式KK Independent Samples K个独立样本检验Kappa κ系数Kendall's Coefficient of Concordance 肯德尔和谐系数Kendall's tau-b 肯得尔等级相关τ-b 系数Kendall's tau-c 肯得尔等级相关τ-c 系数Kendall's W Kendall W 检验Key 关键的Key Variables 关键变量Kolmogorov-Smirnov Z 柯尔莫哥洛夫-斯米诺夫Z 检验Kruskal-Wallis H Kruskal-Wallis H检验(用秩的平方和检验)Kurtosis 峰度,峰度系数LLabel 标签-->变量名标签Label Cases by 以……为个案标签Label Text 标签文字Lambda λ系数Largest value 最大值Last Case 最后一个个案(最大值)Last value 最后一个观测值Launch 开始Launch tutorial now? 现在开始看使用辅导Layer 层-->层变量Left 左-->居左,左对齐Left/bottom 左下Legend 图例Legend Title 图例标题Less 较少,较小Less than 小于Levene StatisticLevene’s Test for Equality of Variances 方差齐性检验License 许可,授权Likelihood 似然性Likelihood Ratio 似然比卡方Line 行,线-->线形图Line Represents 单线条所代表的统计量Line Style 线型Linear 线性Linear model 线性模型Linear Regression 线性回归分析,线性回归模型Lines Represent 多线条所代表的统计量List 列表Listwise 整个数据表Location 位置Log 对数-->对数尺度Lose 丢失Lost 丢失掉Low 最小值Lower 下限Lower Bound 下限Lowest through X 从最小值到XLSD (=Least-significance difference) 能达到显著性水平的最小差异MMajor 主要的Major Divisions 大分度Mann-Whitney U 曼-维特尼U检验(用秩和检验的方法进行)Margin 边距Margins 边距设置Marker 标记,标志Match 匹配Match cases on key variables in sorted files 按排序的关键变量匹配个案Match variables across response sets 输出与多选变量进行交叉分析的匹配变量的选择数和以选择总数为基础计算的边缘频率分布Matrix 矩阵-->矩阵散点图Maximum 最小值McNemar 麦克讷马Mean 均值Mean Difference 均值差Mean of Values 变量值的平均数Mean Rank 平均秩和Mean Square 平均平方和Means 平均数分析Means and standard deviations 均值与标准差Means plot 均值分布图Measure 测量-->测量层次Measurement level 测量层次Measures of AssociationMedian 中位数-->中位数检验法Median of Values 变量值的中位数Merge 合并Merge Files 合并(数据)文件Method 方法Minimum 最大值Minor 次要的Minor Divisions 小分度Missing 缺失,变量的缺失值Missing value 缺失值Missing Value Analysis 缺失值分析Mix 混合Mixed 混合的-->混合对齐Mode 众数Mode of Values 变量值的众数Model 模型Model fit 模型配置Model Summary 模型概要More 更多(选项)Moses 莫西Moses extreme reactions 莫西极值反应Most Extreme 最极值Mult (=Multiple) Response Sets 多选变量集Multiple 多个-->多线图Multiple Response 多选变量分析Multiple Variables 多变量选项NN (=Number) 个案数N of cases 各类别的频数Name 名称-->变量名Name & Label 名称与标签Name Variable 名称变量Negative 负Negative Difference 负差Negative Rank 负秩Network 网络New 新的-->新建(各种文件)New Value 新值New Working Data File 新的当前数据文件Next 下一个-->下一步No 否,无No missing values 无缺失值Nominal 定类变量Nominal by Interval 定类变量与定距变量间的相关系数None 无Nonlinear 非线性的-->非线性模型Nonparametric 非参数的Nonparametric Tests 非参数检验Normal 正态的-->正态分布Normal curve 正态曲线Normal probability plot 残差的正态概率图Notation 记号,记法Note 便条Notes 说明文字Number 数字,编号-->数字型变量Number Above 大于设定值的个案数Number Below 小于设定值的个案数Number of Cases 个案数目,频数Number of Runs 游程数Numeric 数值的-->标准数值型变量OObject 对象Observation 观察,观测-->样本观测值Observed 观测到的-->观测到的频次Observed Prop. (=proportion) 观察到的比例Odds 几率Off 关闭,未开启Offset from 距离OK 行了-->执行Old and New Values 新旧变量值的转换Old Value 旧值Omit 省略,略去On 开启One Sample T Test 单样本T 检验One-sample K-S(Kolmogorov-Smirnov) test 单样柯尔莫哥洛夫-斯米诺夫检验One-Sample Statistics 单样本统计量One-Sample Test 单样本检验One-tailed 单尾的-->单尾检验One-Way ANOV A 简单方差分析Open 打开Open an existing data source 打开现存的数据文件Open another type of file 打开另一种类型的数据文件Open File 打开文档Option 选择,先项Options 先项Order 顺序Order by 排序选项Ordinal 定序变量Organization 组织Organize 组织(动)Organize output by groups 按分组变量组织输出Organize output by variables 各变量单独输出Orientation 定向Other summary function 其它汇总函数Outer 外面的Outer Frame 外框-->给图形增加外框Outlier 远离中心者-->远离均值的值Outliers outside X standard deviations 离均值X个标准差之外的值Output 输出-->输出文件Output 规定输出文件Output Variable 输出变量Output variables are strings 输出变量是字符型变量Outside 在(某范围)之外Overlay 重叠-->重叠散点图PPackage 包Pair 对,一对Paired 已配对的Paired Sample Correlations 配对样本相关系数Paired Sample Statistics 配对样本统计分析Paired Sample Test 配对样本检验Paired Variables 已配对样本Paired-Samples T Test 配对样本的t 检验Pairwise 成对地Parameter 参数Parametric 参数的Part and partial correlations 部分相关与偏相关系数Part correlation 部分相关系数Partial 部分,偏Partial correlation 偏相关系数Paste 粘贴PC (=personal computer) 个人电脑Pct (=Percent) 百分比Pearson 皮尔逊Pearson Chi-Square 皮尔逊卡方值Pearson Correlation 皮尔逊相关系数Percentage 百分比Percentage Above 大于设定值的个案的百分比Percentage Below 小于设定值的个案的百分比Percentages 按百分比作图Percentages Based on 百分比基于……Percentile 百分位数Percentile Values 百分位数选项栏Percents and totals based on respondents 百分比与总数基于回答者人数Personal 个人的-->个人安装Personal or Shared Installation 个人或共享安装Phi and Cramér's V 列联相关的V系数Pie chart 圆饼图,圆形图Pivot 支点,枢轴,在枢轴上的旋转运动-->表格转置(行列互换)Plot 绘图,图形Point 点Poisson 泊桑-->泊桑分布Positive 正Positive Difference 正差Positive Rank 正秩Post Hoc 发生于其后者-->确定均值有差别后的多重比较Predict 预测Predicted 预测的Predicted Values 预测值Prediction Intervals 预测区间Predictor 预测变量Preview 预览Print 打印Print Preview 打印预览Probability 概率Process 处理Processor 处理器Produce 产生,生成Provide 提供pt.(=point) 点-->象素点QQuartile 四分位数RR squared change R2 变化Random 随机的Random Number 随机数Random Number Seed 随机数初始值Random sample of cases 随机选择个案样本Range 范围,全距,极差Range of Grouping Variable 分组变量值的范围Range of missing values 缺失值范围Rank 秩Rank Assigned to Ties 对同值的秩的分配方法选项Rank Cases 对个案排秩Rank Types 秩变量类型Ratio 比率Read 读Read File 读取文件Read Text Data 读入文本格式的数据ReadMe 软件说明文件Ready 准备好了Ready to Install Files 准备安装界面Recent 最近Recently Used Data 最近用过的数据(文件)Recently Used Files 最近用过的文件Recode 重新编码Redo 恢复-->恢复上一步被撤销的操作Reference 参考,参照Reference Line 参照线Refresh 刷新Regression 回归Regression Standardized Residual 回归分析的标准化残差Remote 远程Remote server 远程服务器Removal 剔除Remove 取消-->删除Rename 重命名Replace 替换Replace Missing Values 替换缺失值Replace with mean 用平均值代替(缺失值)Replace working data file 替代当前的数据文件Report 报告Represent 代表Request 要求Reset 重设Residual 残余的,残余物-->残差Residual Statistics 残差统计表Response 回答Right 右-->居右,右对齐Right/top 右上Row 行-->单元格中个案数占行总数的百分比Row Order 行序(行的排列顺序)Run 执行Run Pending Transforms 运行等待中的转换Runs 游程Runs Test 游程检验SS.E. mean 均值的标准误S.E. of mean predictions 对均值标准误的预测Sample 样本Sample size 样本大小Satisfy 满足Save 保存Save As 另存为Save number of case in break group as variable 存储各分组变量的个案数Save standardized values as variables 将标准分存为新变量Save to New File 存为新文档Scale 尺度-->定距/定比变量Scatter 驱散-->散点Scatterplot 散点图Scientific 科学的Scientific Notation 科学计数法Script 程序Seed 种子-->初始值Select 选择Selected Label 选定的标签Selection Variable 选择变量Sequential 序列的Sequential ranks to unique values 相同值的秩次取第一个出现的秩次值Serial 系列的Serial Number 系列号Series 系列Series Displayed as 图形转换选项Server 服务器Set 集,集合Set Definition 多选变量集的定义Set Markers by 以……为标志Setting 设置Setup 安装Setup Complete 完成安装界面Setup Type 安装类型Shade 阴影Shading 阴影设置Share 共享Show 显示Sig. (=significance) 显著性,显著性水平Sign 符号-->符号检验法Significance level 显著性水平Simple 简单的-->简单条形图,单线图,简单散点图Skewness 偏度,偏度系数Slice 片,块-->扇形块Smallest value 最小值Social Science 社会科学Software 软件Software License Agreement 软件授权使用协议Somers' d Somers 等级相关d 系数Sort 分类,排序Sort Cases 对个案进行归类、排序Sort order 排序规则Sort the grouping variables 按分组变量对数据文件进行排序Source 来源Specific 专门的,特定的Specific Values 专门值Specification 规定,规格Split 分割Split Files 分割文件SPSS (Statistical Package for Social Sciences) 社会科学统计软件包SPSS Data Editor 数据窗口编辑界面SPSS/PC(for DOS) DOS 版个人电脑用SPSSStacked 分段条的-->分段条形图Standard deviation 标准差Standard Error of the Estimate 估计值的标准误Standardized 标准化的-->标准化残差Standardized Coefficient 标准化(回归)系数Standardized Residual Plots 标准化残差图Standardized value 标准分Statistic 统计量Statistical 统计的Statistics 统计学,统计量(复数)Statistics for First Layer 第一层分组的统计量Status 状态Std. Error Difference 标准误之差Std.(=Standard) Deviation 标准差Step 步Stepping 分步引入或剔除变量Stepping Method Criteria 变量引入模型或从模型中剔除的判断标准Stepwise 逐步-->逐步进入法Stop 停止,中断String 字符串-->字符型变量Studentized 将残差学生化Studentized deleted 将残差学生化并删除Style 风格,形式-->线型Subtitle 副标题Sum 总和Sum of Ranks 秩和Sum of Squares 总平方和Sum of Squares and Cross-products 离差平方和与叉积和Sum of Values 变量值的和Summaries for groups of cases 个案组概要(变量值模式)Summaries of separate variables 不同变量的概要(变量模式)Summary 概要-->统计概要Summary Function for Selected Va r i abl e s 选定变量的概要函数Suppress 压制,禁止Suppress table 隐藏表格Suppress tables with more than N categories 分组数大于N时禁止频数表在结果中输出Switch 切换Syntax 语句-->语句文件System 系统System-missing 系统缺失值System-or User-missing 系统或用户缺失值Tt t 统计量T test T 检验Table 表格Tables for 为(某变量)列表-->多选变量分析中要分析的变量Tail 尾Template 模板Tendency 趋势Test 检验Test distribution is Normal 待检分布为正态分布Test for linearity 检验线性相关Test Homogeneity of Variances 方差齐次性检验Test of Significance 显著性检验Test Pair List 待检变量对列表Test Prop.(=proportion) 待检比例Test Proportion 待检概率Test Type 检验类型,检验方法Test Value 待检值Test Variable 分析变量,待检变量Test Variable List 待检变量列表Tests for Several Independent Samples 多个独立样本检验Tick 标记Tick mark 标记Tick marks 用点作刻度标志Tick marks for skipped labels 给被越过的标签作标记Tie 同值-->相等的秩,无差(相等)Time 时间-->时间型变量Time Series 时间系列Title 标题Title Justification 标题对齐方式,标题位置Top 顶部-->上边框Total 总数-->单元格中个案数占个案总数的百分比Transform 转换-->数据转换Transpose 转置Transpose Rows and Columns 行列互换Transposition 转置(名)t-test for Equality of Means 均值相等的t 检验Tutorial 辅导-->使用辅导Two Independent Samples 两个独立样本的检验Two-Related-Samples Tests 两个相关样本检验Two-tailed 双尾的-->双尾检验Type 类型-->变量类型Typical 典型安装UUncertainty 不确定性Uncertainty coefficient 不定系数Undo 撤销-->撤销上一步操作Uniform 均匀的-->均匀分布Unique 唯一的Unpaired 不匹配Unpaired variable 不匹配变量Unselected 未被选择的Unselected Case Are 未被选中的个案的处理方法选项Unstandardized 非标准化的-->非标准化残差Unstandardized Coefficient 非标准化(回归)系数Untransposed 未被转置的Unweighted 不加权的-->不加权的个案数Unweighted missing 不加权的缺失值数Upper 上限Upper Bound 上限Use chart specifications from 用……的图形规格Use F value 以F值为标准Use filter variable 使用过滤变量Use probability of F 以F的概率为标准Use specified range 使用特定的范围Use specified values 使用给定的变量值User 用户User Information 用户信息界面Utilities 实用程序Utility 利用,用途VValid 有效的Valid Cases 有效个案Valid Percent 有效百分比Va lue 值Value label 变量值标签Va lue s 变量值标签Values are group midpoints 取值为各组中点Values are grouped midpoints 变量值为各组中点Values of individual cases 个案值(观测值模式)Va r i abl e 变量Variable label 变量名标签Variable list 变量列表,按变量顺序列表Variable Name and Label 变量名称与标签Variable View 变量(编辑)窗口Variables Are Coded As 变量编码方式Variables Entered/Removed 引入或剔除的变量Variables in Set (多选变量)集中的变量Variance 方差Vertical 垂直的,纵向的Vertical Alignment 纵向对齐方式View 观看-->窗口外观控制Viewer 浏览器WWeigh Cases 给变量加权Weight 重量,轻重-->线的粗细Weight Estimation 权重估计Weighted 加权的-->加权的个案数Weighted missing 加权的缺失值数Welch Welch 检验Welcome 欢迎(界面)Width 宽度Wilcoxon Wilcoxon 检验(用符号秩检验)Wilcoxon Signed Rank Test Wilcoxon符号秩检验Wilcoxon W 威尔科克松W 检验Window 窗口-->窗口控制Windows 视窗With normal curve 加上正态曲线Within Groups 组内值Working 当前正在用的Working Data keyed table 当前数据文件为关键表Working Date File 当前数据文件XX Axis X 轴X through highest 从X到最大值X through Y 从X到YYY Axis Y轴Yes 是ZZ Z 检验值Z Approximation Z 近似估计(整理自网络)。
Summary measures,也称为总结性统计量,是描述数据分布和集中趋势的一组统计量。
它们是一组能够帮助人们了解数据中心位置和变异程度的指标。
summary measures包括均值、中位数、众数、标准差、方差等。
这些统计量能够帮助人们更好地理解和解释数据的特征,从而更好地进行数据分析和决策。
1. 均值均值是一组数据的所有数值之和除以数据的个数。
均值是描述数据集中趋势的一种方法,它能够代表一组数据的中心位置。
均值的计算公式为:均值 = 总和 / 数据个数2. 中位数中位数是一组数据按照大小顺序排列后位于中间位置的数值。
如果数据个数为偶数,中位数是中间两个数的平均值。
中位数能够更准确地表示数据的中心位置,尤其对于存在特殊值的数据集来说,中位数的稳健性更好。
3. 众数众数是一组数据中出现次数最多的数值。
众数是描述数据中变化趋势的一种统计量,它能够表示数据中的集中现象和典型值。
4. 标准差标准差是一组数据偏离均值的平均距离。
标准差是描述数据变异程度的一种指标,它越大表示数据越分散,标准差越小表示数据越集中。
5. 方差方差是一组数据离均值的偏差平方的平均数。
方差也是描述数据变异程度的一种指标,它是标准差的平方,能够衡量数据的离散程度。
summary measures在数据分析中起着非常重要的作用,它们能够帮助人们更好地理解和描述数据的特征,从而更准确地进行数据分析和预测。
在实际应用中,人们常常根据summary measures对数据进行可视化和摘要,从而更好地从数据中发现规律和提取信息,为决策提供依据和支持。
对summary measures的理解和应用是统计学和数据分析的基础,也是各行各业工作者必备的技能之一。
summary measures 续写为了更好地理解和应用summary measures,在实际数据分析中,我们需要深入了解每个summary measures的特点、适用范围以及计算方法。
stata 熵权topsis法在Stata中实现熵权TOPSIS法需要遵循以下步骤:数据准备:首先,确保你的数据已经准备好,并且已经进行了必要的预处理。
这可能包括缺失值处理、异常值处理、数据转换等。
数据标准化:由于熵权TOPSIS法涉及到数据的标准化,你需要将原始数据转化为0-1之间的值。
在Stata中,你可以使用scale命令来实现这一点。
stata复制代码foreach var of varlist _all {replace `var' = (`var'-min(`var')/(max(`var') -min(`var')))}这段代码将所有变量的值都标准化到0-1之间。
3. 计算熵值:熵权TOPSIS法需要计算每个指标的熵值。
在Stata中,你可以使用以下代码来计算:stata复制代码foreach var of varlist _all {gen entropy_`var' = -1 * (`var' / sum(`var')) * log2(`var' / sum(`var'))}这段代码将为每个变量生成一个熵值。
4. 计算权重:基于熵值,你可以计算每个指标的权重。
在Stata中,你可以使用以下代码:stata复制代码foreach var of varlist _all {gen weight_`var' = 1 - entropy_`var' / max(en tropy_`var')}这段代码将为每个变量生成一个权重。
5. TOPSIS得分计算:最后,你可以使用权重和标准化后的数据来计算TOPSIS得分。
在Stata中,你可以使用以下代码:stata复制代码gen topsis_score = .foreach var of varlist _all {replace topsis_score = max(topsis_score, weig ht_`var' * (`var')) if !missing(topsis_score) & !missing(`var')replace topsis_score = min(topsis_score, weig ht_`var' * (`var')) if !missing(topsis_score) & !missing(`var')}这段代码将为每个观测值计算TOPSIS得分。
gwassummary分析流程英文回答:The GWAS (Genome-Wide Association Study) summary analysis process involves several steps to identify and interpret the genetic variants associated with a particular trait or disease. These steps include data quality control, statistical analysis, and functional annotation.Firstly, in the data quality control step, the raw genetic data from the study participants are processed to ensure accuracy and reliability. This involves checking for genotyping errors, removing samples with low call rates, and filtering out genetic variants that do not meet quality control criteria. By performing these quality control measures, we can ensure that the subsequent analysis is based on high-quality data.Next, in the statistical analysis step, the association between genetic variants and the trait or disease ofinterest is assessed. This is typically done by performing a logistic regression analysis for binary traits or linear regression analysis for continuous traits. The statistical models take into account various factors such as population structure, age, and sex to control for potential confounding effects. The analysis results in the identification of genetic variants that show a significant association with the trait or disease.Once the significant genetic variants are identified, the next step is to interpret their functional relevance. This is done through functional annotation, which involves analyzing the genomic location of the variants and their potential effects on gene function. Various bioinformatics tools and databases are used to annotate the variants, such as ENCODE, GTEx, and dbSNP. This helps in understanding the biological mechanisms underlying the association between the variants and the trait or disease.In addition to these main steps, there are also other considerations in GWAS summary analysis. These include adjusting for multiple testing to account for the largenumber of genetic variants tested, conducting replication studies to validate the findings, and performing pathway analysis to identify biological pathways enriched with associated variants.Overall, the GWAS summary analysis process involves rigorous data quality control, statistical analysis, and functional annotation to identify and interpret genetic variants associated with a trait or disease. It is a complex and iterative process that requires expertise in genetics, statistics, and bioinformatics.中文回答:GWAS(全基因组关联研究)摘要分析流程涉及多个步骤,以识别和解释与特定性状或疾病相关的遗传变异。
南京理工大学泰州科技学院计算机系信管专业10(2)班级专业综合实训姓名:学号:指导老师:职称:设计地点:4306 起讫时间:完成报告书时间:2014 年1 月17 日计算机科学与技术系编印二零一三年一、 AHP 、TOPSIS 、DEA 三种方法的理论基础1 层次分析法(AHP )的概述AHP 的背景层次分析法(Analytic Hierarchy Process,简称AHP)是美国运筹学家、匹兹堡大学T. L. Saaty 教授在20世纪70年代初期提出的,AHP 是对定性问题进行定量分析的一种简便、灵活而又实用的多准则决策方法。
它的特点是把复杂问题中的各种因素通过划分为相互联系的有序层次,使之条理化,根据对一定客观现实的主观判断结构(主要是两两比较)把专家意见和分析者的客观判断结果直接而有效地结合起来,将一层次元素两两比较的重要性进行定量描述。
而后,利用数学方法计算反映每一层次元素的相对重要性次序的权值,通过所有层次之间的总排序计算所有元素的相对权重并进行排序。
该方法自1982年被介绍到我国以来,以其定性分析与定量分析相结合地处理各种决策因素的特点,以及其系统灵活简洁的优点,迅速地在我国社会经济各个领域内,如能源系统分析、城市规划、经济管理、科研评价等,得到了广泛的重视和应用。
AHP 的理论基础 1、层次结构模型:(1) 最高层:只包含一个元素,表示决策分析的总目标,因此也称为总目标层。
(2) 中间层:包含若干层元素,表示实现总目标所涉及的各子目标,包括各种准则、约束、策略等,因此也成为目标层。
(3) 最低层:表示实现各决策目标的可行方案、措施等,也称为方案层。
在层次结构模型中,相邻两层次元素之间的关系用直线标明,称为作用线;元素之间不存在关系,就没有作用线。
在实际操作中,模型的层次数由系统的复杂程度和决策的实际需要而定,一般每一层次的元素个数不超过9个,过多的元素会给确定各指标权重带来困难。
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summary函数检验异常点和强影响点异常点和强影响点是数据分析中常用的概念,用于识别数据集中的特殊值和对整体结果产生较大影响的值。
在本文中,我们将探讨如何使用summary函数来检验异常点和强影响点。
让我们了解一下summary函数的基本功能。
summary函数是一种统计函数,它可以提供关于数据集中各个变量的描述性统计信息,例如最小值、最大值、中位数、均值等。
通过查看summary统计结果,我们可以初步了解数据的分布情况和异常值的存在。
那么,如何使用summary函数来检验异常点呢?首先,我们需要将数据导入到R环境中,并使用summary函数对数据进行描述性统计。
通过观察summary统计结果,我们可以查看数据的最小值和最大值。
如果数据集中存在明显偏离常规范围的数值,即超出最小值和最大值的范围,那么这些数值很可能是异常点。
举个例子来说明。
假设我们有一组身高数据,我们可以使用summary函数来检查是否存在异常值。
假设我们的数据集有100个样本,其中一个样本的身高是300cm。
我们可以使用summary函数查看数据的最小值和最大值。
如果最小值是150cm,最大值是200cm,那么300cm显然是一个异常值。
除了使用最小值和最大值来检验异常点,我们还可以使用其他统计量,例如中位数和四分位数。
通过比较这些统计量与数据集的分布情况,我们可以进一步判断是否存在异常值。
接下来,让我们看看如何使用summary函数来检验强影响点。
强影响点是指对整体结果产生较大影响的值。
在统计分析中,我们通常使用回归分析来检测强影响点。
回归分析可以用来探讨自变量与因变量之间的关系,并通过计算回归系数来衡量这种关系的强度。
在回归分析中,我们可以使用summary函数来查看回归模型的拟合优度和各个变量的回归系数。
如果某个变量的回归系数较大,那么这个变量很可能是一个强影响点。
此外,我们还可以通过观察回归模型的拟合优度来判断整体结果是否受到某个样本的强烈影响。
一、概述在数据分析和数据处理中,经常会遇到数据集中存在缺失值(NA值)的情况。
对于这些缺失值,我们需要进行适当的处理,以便保证数据的完整性和准确性。
summarize函数是一种常用的数据处理方法,它可以对数据集中的缺失值进行统计和汇总,帮助我们更好地理解数据的情况。
二、summarize函数的作用summarize函数通常用于对数据集中的变量进行统计描述。
在处理缺失值时,summarize函数可以帮助我们计算缺失值的数量、比例以及其他统计指标,从而全面了解数据集的情况。
三、summarize函数的使用方法summarize函数通常在数据分析工具或编程语言中提供,如R语言中的dplyr包、Python语言中的pandas库等。
其基本使用方法如下:1. 计算缺失值的数量:summarize函数可以使用count()或sum()等函数来计算数据集中每个变量的缺失值数量,例如:```data > summarize(missing_count = sum(is.na(variable)))```这将返回每个变量的缺失值数量。
2. 计算缺失值的比例:如果需要计算缺失值在整个数据集中的比例,可以使用mean()函数来计算:```data > summarize(missing_proportion = mean(is.na(variable))) ```这将返回每个变量的缺失值比例。
3. 统计其他指标:除了计算缺失值的数量和比例外,summarize函数还可以统计其他指标,如均值、中位数、标准差等。
这可以帮助我们更全面地了解数据的情况。
四、summarize函数的优势summarize函数作为一种通用的数据处理方法,具有以下优势:1. 灵活性:summarize函数可以根据不同的需求进行灵活的定制,从而计算出各种不同的统计指标。
这使得我们可以根据实际情况,选择最适合的统计描述方式。
2. 高效性:summarize函数通常能够快速、高效地计算出所需的统计指标,为数据分析提供便利。
SAS中的描述性统计过程(2012-08—01 18:07:01)转载▼分类:数据分析挖掘标签:杂谈SAS中的描述性统计过程描述性统计指标的计算可以用四个不同的过程来实现,它们分别是means过程、summary过程、univariate过程以及tabulate过程。
它们在功能范围和具体的操作方法上存在一定的差别,下面我们大概了解一下它们的异同点。
相同点:他们均可计算出均数、标准差、方差、标准误、总和、加权值的总和、最大值、最小值、全距、校正的和未校正的离差平方和、变异系数、样本分布位置的t检验统计量、遗漏数据和有效数据个数等,均可应用by语句将样本分割为若干个更小的样本,以便分别进行分析。
不同点:(1)means过程、summary过程、univariate过程可以计算样本的偏度(skewness)和峰度(kurtosis),而tabulate过程不计算这些统计量;(2)univariate过程可以计算出样本的众数(mode),其它三个过程不计算众数;(3)summary过程执行后不会自动给出分析的结果,须引用output语句和print过程来显示分析结果,而其它三个过程则会自动显示分析的结果;(4)univariate过程具有统计制图的功能,其它三个过程则没有;(5)tabulate过程不产生输出资料文件(存储各种输出数据的文件),其它三个均产生输出资料文件.统计制图的过程均可以实现对样本分布特征的图形表示,一般情况下可以使用的有chart过程、plot过程、gchart过程和gplot过程。
大家有没有发现前两个和后两个只有一个字母‘g’(代表graph)的差别,其实它们之间(只差一个字母g的过程之间)的统计描述功能是相同的,区别仅在于绘制出的图形的复杂和美观程度.chart过程和plot过程绘制的图形类似于我们用文本字符堆积起来的图形,只能概括地反映出资料分布的大体形状,实际上这两个过程绘制的图形并不能称之为图形,因为他根本就没有涉及一般意义上图形的任何一种元素(如颜色、分辨率等)。
名词解释:1.American Realism (1860-1914):Realism was a model of writing that gives the impression of recording or reflecting faithfully an actual way of life.Realism refers both to a literary method based on detailedaccuracydescription and to a more general attitude that rejects idealization, escapism, and other extravagant qualities of romance in favor of recognizing soberly the actual problems of life.2. in the latter half of the 19th century as a reaction against Romanticism.It focuses on commonness of the lives of the common people, and emphasizes objectivity and offers an objective rather than an idealistic view of human nature and human experience.Puritan and Puritanism①One division of English Protestant(新教)They regarded the reformation of the church under Elizabeth as incomplete, and called for further purification and simplicity to church services and the authority of the Bible.②A religious and political movement.Simply speaking, American Puritanism just refers to the spirit and ideal of puritans who settled in the North American continent in the early part of the 17th century because of religious persecutions.Their Religious Doctrines: original sin, total depravity, predestination and limited atonement through a special infusion of grace from God.3The RomanticPeriod:Beginning:from the end of 18th century American Romanticism went to the top in the 1840s, known as New England Transcendentalism.Ending: end with the outbreak of Civil War (1861~65)T h e r o m a n t i c s s h o w e d a p r o f o u n d a d m i r a t i o n a n d l o v e f o r n a t u r e.T h e b e a u t y a n d p e r f e c t i o n o f n a t u r e c o u l d p r o d u c e i n h i m u n s p e a k a b l e j o y a n d e x a l t a t i o n.4The Gilded Age an age of excess and extremes, of decline and progress, of poverty and dazzling wealth, of gloom and buoyant hope (a deterioration of American moral values; extremes of wealth and poverty)作家作品Anne Bradstreet The best of puritan poet弗雷诺Freneau:The Wild Honey Suckle.P a i n e潘恩:小册子作家(美国危机,常识)欧文I r v i n g:F a t h e r o f A m e r i c a n s h o r t s t o r i e s a n d A m e r i c a n l i t e r a t u r e.t h e f i r s t o f t h e m o d e r n m e n o f l e t t e r s t o w r i t e h i s t o r y a n d b i o g r a p h y a s l i t e r a r y e n t e r t a i n m e n t.T h e S k e t c h B o o k《见闻札记》“R i p W a n W i n k l e”瑞普.凡.温克尔“T h e L e g e n d o f S l e e p y H o l l o w”《睡谷的传说》作者自述.库伯:美国边疆文学“The American Scott”The Spy 《间谍》the first novel The Pilot领航者The Leatherstocking Tales,The Last of the Mohicans,The Deerslayer《拓荒者》The Pioneer,大草原Bryant:the American Wordsworth”Thanatopsis.死亡随想.To aWaterfowl(the peak of his work, “the most perfect brief poem in the language”诗歌赏析i t should say something more than beauty; it should carry morals(寓意).It rhymes “abab,”, while the length of each line is so different that you cannot find a regular foot. But, the two long lines in the middle of each stanza may refer to the balance in the floating of the bird. The first and the fourth lines, which are relatively shorter, look like two wings. The stanzaic form reminds one of a flying bird.Allan Poe爱伦坡father of the modern short story The Fall of the House of Usher.The Raven.Annabel Lee,To Helen诗歌赏析Simile 明喻Hyperbole夸张Alliteratio头韵Parallelism,Metaphor•The beauty of a woman with whom Poe became acquainted when he was 14.Apparently she treated him kindly and may have urged him or perhaps inspired him to write poetry. Beauty,as Poe uses the word in the poem ,appears to refer to the woman’s body as well as her soul.On the one hand ,he represents her as Helen of Troy—the quintessence(典范)of physical beauty-at the beginning of this poem.On the other hand ,he represents her as Psyche–at the end of the poem. In Greek, Psyche means soul.profound expressing the author's persistent pursuit of love and beauty.•Sings thepraises of the beauty ofwomen.Emerson爱默生Nature It’s a book which declared the birth of Transcendentalism The American Scholar被视为美国的知识独立宣言Self-reliance Thoreau梭罗Leader of American Transcendentalism Walden瓦尔登湖Hawthorne霍桑The Scarlet Letter.红字--- Through the triangular love affair in colonial America, Hawthorne censured the Puritan philosophy of life.MajorThemes Public Guilt vs. Private Guilt Punishment vs.Forgiveness SinandJudgment Civilization vs. Wilderness The Town vs. the Woods.A theocracy is a government where the civil laws are based (in whole or in part) on religious laws; civil leaders in theocracies are probably also important religious leaders.(小说summary)Melville梅尔维尔Moby Dick大白鲸the first American prose epic.Symbolism and allegory (寓言,寓意) in Moby-Dick,Land : a symbol of safety ,Sea: adventure and danger,The Pequod: the human world 1.Search for truth The story deals with the human pursuit of truth and the meaningofexistence.2.Conflict between Good and Evil.3. Conflict between Man and Nature.4. Isolation between man and man; man and nature; man and society.人必须承认自己灵魂中的“恶”;人虽然可以观察世界,或者对世界有一定的影响力量,但从根本来说,人不能左右自然、征服自然;人只要不冒失的自取灭亡,大自然便乐于让他平静的生活。
stata summarize 变量缩写标题:Stata Summarize 变量缩写:一种简洁高效的统计分析方法概述:Stata是一种功能强大的统计分析软件,它提供了丰富的命令和功能,可以帮助研究人员进行各种统计分析和数据处理。
其中,summarize命令是Stata中常用的命令之一,用于对数据进行描述性统计分析。
本文将介绍如何使用Stata的summarize命令进行变量缩写和统计分析,并探讨其在实际研究中的应用。
一、什么是summarize命令summarize命令是Stata中的一个基本命令,用于计算和展示变量的基本统计信息。
通过summarize命令,我们可以得到变量的均值、标准差、最大值、最小值以及观测数量等统计指标。
这些统计指标可以帮助我们了解数据的分布情况和变量的特征。
二、summarize命令的基本用法在Stata中,我们可以使用summarize命令对一个或多个变量进行统计分析。
其基本语法如下:summarize varname其中,varname表示需要进行统计分析的变量名称。
我们可以通过在命令中输入一个或多个变量名称,来同时对多个变量进行统计分析。
例如,我们可以使用以下命令对变量“income”和“age”进行统计分析:summarize income age三、summarize命令的输出结果summarize命令的输出结果包括以下几个部分:1. Obs数:表示观测数量,即数据中的样本量。
2. Mean:表示均值,即变量的平均值。
3. Std. Dev.:表示标准差,即变量值的离散程度。
4. Min和Max:分别表示变量的最小值和最大值。
5. Percentiles:表示变量的分位数,包括25%,50%,75%等。
6. Skewness和Kurtosis:分别表示偏度和峰度,用于描述变量分布的偏斜和尖峰程度。
四、summarize命令的高级用法除了基本的描述性统计指标之外,summarize命令还可以进行更多高级的统计分析。
Thanatopsis Summary"Thanatopsis" starts by talking about nature's ability to make us feel better. The speaker tells us that nature can make pain less painful. It can even lighten our dark thoughts about death. He tells us that, when we start to worry about death, we should go outside and listen to the voice of nature. That voice reminds us that we will indeed vanish when we die and mix back into the earth.The voice of nature also tells us that when we die, we won’t be alone. Every person who has ever lived is in the ground ("the great tomb of man") and everyone who is alive will be soon dead and in the ground too. This idea is meant to be comforting, and the poem ends by telling us to think of death like a happy, dream-filled sleep.Lines 1-8 SummaryGet out the microscope, because we’re going through this poem line-by-line. Line 1To him who in the love of Nature holds∙The first line of this poem is confusing all by itself, so read through it to the middle of the third line – that’s where the first idea ends (at the semicolon after "language").Now let’s go back to line 1.∙Here the speaker is introducing us to a certain kind of guy who loves nature.Line 2Communion with her visible forms, she speaks∙This guy has an almost holy relationship with nature. He "holds communion" (like you would do in a church) with things like rocks and trees and rivers (those areexamples of "visible forms" of nature). In these moments of communion natureactually "speaks" to this guy.∙Nature is the "she" mentioned at the end of the line. That’s an example ofpersonification, a pretty common poetic trick.∙(We’ll go along with Bryant and use the capital letter "N" for Nature, because in this case she’s more like a person than a thing).Lines 3-5A various language; for his gayer hoursShe has a voice of gladness, and a smileAnd eloquence of beauty, and she glides∙Nature talks to her lover in different ways, depending on the way he’s feeling.When he is feeling happy (in "his gayer hours") Nature smiles, and speaks to him happily ("with a voice of gladness"). In these moments, she has the "eloquence"(smooth and lovely speech) "of beauty" (line 5).Lines 6-8Into his darker musings, with a mildAnd healing sympathy, that steals awayTheir sharpness, ere he is aware. When thoughts∙Sometimes the nature lover is feeling mopey and is brooding over depressing thoughts. Then Nature "glides" in and makes him feel better.∙In these moments, Nature treats him with gentle sympathy, which heals him. She takes away the pain ("sharpness") of his thoughts before he even realizes it. Basically, when this guy’s feeling lousy, Nature fixes him up. She mightLines 9-13 SummaryGet out the microscope, because we’re going through this poem line-by-line. Line 9-10Of the last bitter hour come like a blightOver thy spirit, and sad images∙Still, this guy isn’t just having any old depressing thoughts. He’s really worried about death ("the last bitter hour").∙These thoughts about death come like a plague or disease (a "blight") on his spirit.(By the way, "blight" is a pretty good word. It's often used when referring to diseases plants get. Check out these pictures of potato blight for an example. Can you imagine this guy's spirit getting potato blight? Nasty.)∙Did you see what happened there? The speaker of the poem isn’t just talking about a random lover of nature now (the "him" from line 1). All of a sudden, he’stalking about you. Here, for the first time, in line 10, he talks about "thy" (your) spirit.The poem has switched from musing about nature to giving you advice.Line 11Of the stern agony, and shroud, and pall,∙We're pretty sure everyone has thought about what it will be like to die. You have, haven't you? Here the speaker gives us some strong images of those scary thoughts.∙He talks about the "stern agony" of dying, which we think is a great phrase. Death doesn’t just hurt, it hurts in a sharp, severe, serious way. We think of mean substitute teachers and hall monitors as being "stern," but usually those guys don't cause agony.Death does, though.∙The speaker also uses a couple of useful, death-related words, so we’ll break those down for you.∙ A "shroud" is the cloth you use to wrap up a dead body.∙"Pall" is another good, spooky death word. It can mean a cloth that covers a coffin, or it can mean the coffin itself (like when people talk about "pallbearers" at a funeral).Line 12And breathless darkness, and the narrow house,∙Here we get some more death imagery, only this time even scarier.∙The speaker helps us imagin e the "breathless darkness" of the grave and the "narrow house" of the coffin. These lines are really claustrophobic, aren’t they? They make us feel like we’re trapped in some suffocating prison. We're feeling a bitpanicky now.Line 13Make thee to shudder, and grow sick at heart;—∙The speaker is definitely building a mood here. He wants us to think about those moments where we worry so much about death that we "shudder" and "grow sick at heart."∙Soak up this scary feeling, because he’s about to change things up on us.Lines 14-21 SummaryGet out the microscope, because we’re going through this poem line-by-line.Lines 14-15Go forth, under the open sky, and listTo Nature’s teachings, while from all around—∙Suddenly, we’re set free. The speaker tells us to go outside, "under the open sky." That’s a big relief, given that just two lines ago, we were trapped in a grave underground (line 12).∙Suddenly we’re back with "Nature" and we’re being told to "list" (a fancy poetic way of saying listen) to her "teachings." Those teachings are all around us in the great outdoors.Lines 16-17Earth and her waters, and the depths of air—Comes a still voice— Yet a few days, and thee∙The "voice" of Nature comes from the "Earth," the "waters," and the "air."∙It’s really important that the poem calls Nature's voice a "still" voice. That means, calm and quiet, and it gives this line a feeling of peace and comfort. Things are going to be OK.∙Or maybe not. What's up with the "Yet a few days" bit? Well, at the end of line 16, there’s another shift. Apparently something is going to happen in a few days, and we're guessing it's going to have something to do with death...Line 18The all-beholding sun shall see no more∙Bad news! Apparently we’re going to die in a few days.∙The speaker tries to make it sound pretty, but really he's telling us we're going to die soon. Even the sun, which sees everything ("all-beholding") won’t be able to see us any more. We’re just going to vanish.Lines 19-21In all his course; nor yet in the cold ground,Where thy pale form was laid, with many tears,Nor in the embrace of ocean, shall exist∙Now we go on a little tour, as the speaker tells us all the places we won’t be afterwe die.∙We won’t be on land, where the sun runs "all his course" (that’s the path the sun follows over a day).∙We also won’t be in the "cold ground," where our crying relatives bury our corpse during our funeral.∙We won’t be in the "embrace of the ocean" either.∙So wait. Where will we be? We've talked about a land burial and a sea burial.What' next? Cremation?∙This poem’s kind of a bummer right now, but there’s good news coming, we promiseLines 22-30 SummaryGet out the microscope, because we’re going through this poem line-by-line. Lines 22-23Thy image. Earth, that nourished thee, shall claimThy growth, to be resolved to earth again,∙Phew. No cremation.∙So where does our "image" go when we die, if it isn’t sinking in the sea or being buried in the ground? Well, it goes back to the Earth.∙It was "nourishment" from the Earth that allowed our body to grow, and now our body will be turned ("resolved") back into earth again. This is like that old expression you may have heard – "Ashes to ashes, dust to dust."Lines 24-25And, lost each human trace, surrendering upThine individual being, shalt thou go∙When we die, according to this poem’s version of things, we lose what made us human ("each human trace"). We give up our "individual being."∙Basically, after you die, you stop being the person you used to be.Lines 26-27To mix for ever with the elements,To be a brother to the insensible rock∙Our speaker is really in love with this image of returning to the Earth, so now he just riffs on it a little.∙He tells us our bodies will "mix […] with the elements." We’ll basically be no different from an "insensible rock." Insensible just means "unable to feel." So, all the touch and sight and hearing and emotion that made us human will be gone, leaving us no different from rocks.Lines 28-29And to the sluggish clod, which the rude swainTurns with his share, and treads upon. The oak∙Just to make sure we got the point, the poem drives it in again, this time with some fancy vocab words.∙Now the speaker tells us we’ll be like a "sluggish clod" after we’re dead. A"clod" is a chunk of dirt, and "sluggish" lets us know how lifeless and heavy we’ll be.∙The speaker really works this image of our bodies turning into dirt. Here he talks about how a country boy (aka a "swain" – a pretty popular dude in old nature poems) digs up that clod of dirt with his plow ("share") and walks ("treads") all over it. That’s just how low you’ll be after you’re dead. Even the swains get to step on you.Bummer.∙Are you feeling comforted yet? Um, we're guessing no. Hey, Mr. Speaker, you're going to have to try a bit harder.Line 30Shall send his roots abroad, and pierce thy mould.∙Our dead bodies will be food for oak trees, as they send their roots out through the earth. Those roots will pierce the "mould" (soil) of our bodies.∙We think that last image is really vivid – a little bit violent, but also sort of beautiful.Bodies mixing with trees? OK, it could be worse.Lines 31-37 SummaryGet out the microscope, because we’re going through this poem line-by-line. Lines 31-32Yet not to thine eternal resting-placeShalt thou retire alone, nor couldst thou wish∙This is a big turn in the poem. Up until now, we’ve only been talking about the sad and scary aspects of dying. Is the idea of your body turning into oak treeMiracle-Gro comforting? Sure it's better than that claustrophobic coffin, but it's still not great.∙Now we get a big "Yet." Even though there’s some bad news about going to our "eternal resting place," the speaker wants us to know that we won’t go there ("retire") all by ourselves.Line 33Couch more magnificent. Thou shalt lie down∙In fact, the speaker tells us we’re headed for a "magnificent" and comfy resting place, like a "couch." That sounds pretty good, right? Way better than the "narrow house" we were worrying about in line 12.∙We’re making a big swing here, from creepy to comforting.Lines 34-36With patriarchs of the infant world—with kings,The powerful of the earth—the wise, the good,Fair forms, and hoary seers of ages past,∙The speaker tells us that when we die, we’ll "lie down" with all kinds of fancy and important people.∙There will be "patriarchs" (that means fathers, heads of families, or male leaders) from long ago when the Earth was young ("the infant world"). This also makes us think of the Biblical patriarchs, like Abraham.∙There will also be kings and others who are "powerful," "wise" and "good."∙In this final resting place, there will be beautiful people ("fair forms"). There will also be old ("hoary") prophets ("seers").∙Maybe it’s hard to see where this is headed, but he’s building to a point, we promise.Line 37All in one mighty sepulchre. The hills∙All of these important people from lines 34-36 will lie down with you in one giant tomb ("sepulcher"). That giant tomb, of course, is the Earth.∙That’s what this whole section of the poem is about, the idea that when we die we all lie down together in one big grave. What’s cool is how Bryant can make that sound like a good thing. You're feeling a bit special now, aren't you?Lines 38-45 SummaryGet out th e microscope, because we’re going through this poem line-by-line. Line 38Rock-ribbed and ancient as the sun,—the vales∙Now the speaker starts a description of the whole earth, of the geography of our globe. Remember, "Thanatopsis" started out as a nature poem, and now we’reheaded back to those themes.∙He begins by talking about the hills. He refers to them as "rock-ribbed and ancient as the sun." We sort of love that image – it makes the hills sound like giant, oldsleeping animals.Line 39Stretching in pensive quietness between;∙Next he describes the quiet, thoughtful ("pensive") valleys that stretch outbetween the hills. Again, the idea that a valley could be thoughtful makes this whole imaginary landscape feel kind of alive.Line 40-41The venerable woods—rivers that moveIn majesty, and the complaining brooks∙Just a few more stops on this tour of the world’s landscape:∙We take a quick peek at the "venerable" (that means something old and deserving of respect) forests. We picture these woods as being like Fangorn in Lord of the Rings – ancient, and full of wise Ents.∙We also see the majestic rivers, and their little cousins, "the complaining brooks."(A brook is a little stream, and the speaker calls them complaining to create an imageof the constant, burbling sound they make.)Line 42-43That make the meadows green; and, poured round all,Old Ocean’s gray and melancholy waste,—∙After that, the speaker takes us to some pretty meadows, which the brooks have watered and turned green.∙Finally, we arrive at the "gray and melancholy waste" of the "Old Ocean," which surrounds everything else.∙ A couple things to notice about that last image. First, Bryant spends a lot of times telling us how old (or "ancient" or "hoary" or "venerable") everything in the world is. We think that adds to the peaceful, serious tone of this poem. Second, we’re back to some grim imagery – does "melancholy waste" sound nice to you? – an echo of the sad moments in the first stanza.Lines 44-45Are but the solemn decorations allOf the great tomb of man. The golden sun,∙All of these places in Nature – hills, valleys, forests, streams, the ocean – are compared to "decorations" on a "tomb." Every last hill and valley and river is just a way to spruce up the giant grave that all humans will share.∙Like a lot of moments in this poem, it’s not super-happy. Still, there is a kind of quiet beauty to the idea of "solemn decorations."Lines 46-50 SummaryGet out the microscope, because we’re going through this poem line-by-line. Line 46The planets, all the infinite host of heaven,∙Suddenly we zoom up to the sky, and out to the sun and the planets. This is one of the cool tricks Bryant uses. Even though this is generally a sad, quiet poem, it’s also full of activity and energy and life.∙Notice that the speaker also mentions the "infinite host of heaven." This could just be a reference to all the things that are in the sky (stars, planets, moons, etc).∙It could also be a little religious hint. The Bible (Luke 2:13) talks about a "heavenlyhost," meaning an army of angels.Lines 47-48Are shining on the sad abodes of death,Through the still lapse of ages. All that tread∙All those beautiful things in the sky – whether they're planets or angels – are looking down on the unhappy little homes ("abodes") of death.∙Man, everything, even the sun, reminds our speaker that death is unavoidable.The sun keeps shining and people keep dying forever and ever. This process keeps going "through the still lapse of the ages."∙Do you feel that sense of calm again? Things are always quiet and "still," and the passage ("lapse") of time continues no matter what we do.Lines 49-50The globe are but a handful to the tribesThat slumber in its bosom.—Take the wings∙Here we keep digging into the "earth as a big tomb" metaphor.∙The speaker says that all the people who are now alive are just a "handful"compared to the dead people buried in the ground. Actually, we got curious about this idea, and it turns out to be true. You can read about it here.Lines 51-57 SummaryGet out the microscope, because we’re going through this poem line-by-line. Line 51Of morning, pierce the Barcan wilderness,∙All right, buckle your seatbelts, because we’re about to go off on another crazy poetic journey.∙The speaker first imagines us flying off to the deserts of North Africa ("the Barcan wilderness"). He’s having some fun with his ability to transport us suddenly to far away lands, and to call up images of strange, exotic landscapes. It's sort of like BBC's Planet Earth documentary series.Line 52Or lose thyself in the continuous woods∙Now we fly off in the other direction, across the continent of North America, to the western coast.∙In Bryant’s day, people didn't associate the West Coast with cities like LA, San Francisco, Portland, and Seattle. It was still a serious wilderness of endless trees ("continuous woods"). In its way, the American West in the early 19th century was as untamed as the African desert.Lines 53-54Where rolls the Oregon, and hears no sound,Save his own dashings—yet the dead are there:∙The speaker puts us on the shores of "the Oregon," which is an old name for the mighty Columbia River. He asks us to imagine that the wilderness around is so silent that there’s no sound except the noise of the river ("his own dashings").∙Now the payoff for this little trip: even in the western woods, so far away from civilization, there are still dead people in the ground. That’s the one reality youcan’t escape: death.Lines 55-57And millions in those solitudes, since firstThe flight of years began, have laid them downIn their last sleep—the dead reign there alone.∙In fact, there aren’t just a few dead people in these wild, far-away places("solitudes"). Since the beginning of time, "millions" of people have gone underground for the big sleep. Even in places that seem completely empty of people, the dead rule there alone.∙Basically, dead people are here, dead people are there, dead people areeverywhereLines 58-66 SummaryGet out the microscope, because we’re going through this poem line-by-line.Lines 58-59So shalt thou rest, and what if thou withdrawIn silence from the living, and no friend∙The whole point of this poem is that this is going to happen to you. You’re going to "rest" like all those other people.∙It’s a little scary, huh? Maybe you’re worried that, when you "withdraw," none of your friends will notice your "departure."Lines 60-61Take note of thy departure? All that breatheWill share thy destiny. The gay will laugh∙This poem isn’t trying to freak you out. The speaker doesn’t want you to feel terrible or worry about your death. He wants you to think about it in a calm, relaxed way, to see how it fits in with the natural order of the world.∙Everyone alive ("all that breathe") is going to die. All human beings are headed for the same place. In a way, it’s obvious, but this poem forces us to think about it really carefully.Lines 62-64When thou art gone, the solemn brood of carePlod on, and each one as before will chaseHis favorite phantom; yet all these shall leave∙After we are gone, life will go on. Happy people ("the gay" in line 61) will keep on laughing. Unhappy people ("the solemn brood") will continue to trudge on, weighed down by their worry ("care").∙All these people – the happy and the unhappy – will continue to go about their business, even if, in the end, that business doesn’t amount to anything more than chasing "phantoms." To quote rock legends Kansas: "Dust in the wind, all we are is dust in the wind." Don’t you forget it.Lines 65-66Their mirth and their employments, and shall comeAnd make their bed with thee. As the long train∙Eventually, all of those people you left behind when you died are coming to "make their bed" next to you.∙We’re taking the long view in this poem. Instead of getting caught up in your private worries, think about how we all end up in the same place.Lines 67-72 SummaryGet out the microscope, because we’re going through this poem line-by-line. Line 67Of ages glide away, the sons of men,∙We’re pretty sure you’ve got the idea by now, but the speaker is headed for his big conclusion, so we’re not about to stop him. We’re going to get one last survey of the history of humanity ("the long train / of ages"). As that history "glides away," all the "sons of men" pass away too.Lines 68-70The youth in life’s green spring, and he who goesIn the full strength of years, matron and maid,The speechless babe, and the gray-headed man—∙Once more: everyone dies. Young people, in the "green spring" of life, willeventually die.∙People in the prime ("full strength") of life will die too.∙The old woman ("matron") will die, but so will the young woman ("maid").∙The same goes for babies who are too young to talk and old men with grey hair.∙Death is the great equalizer. Doesn’t matter who or when or what you are – you will die.Lines 70-72Shall one by one be gathered to thy side,By those, who in their turn shall follow them.∙All the people listed in lines 68-70 are going to come and lie down next to you in the earth. They will be laid in the grave by people who will then eventually diethemselves. It’s an endless chain, all of us following each other into the grave,whether we like it or notLines 67-72 SummaryGet out the microscope, because we’re going through this poem line-by-line. Line 67Of ages glide away, the sons of men,∙We’re pretty sure you’ve got the idea by now, but the speaker is headed for his big conclusion, so we’re not about to stop him. We’re going to get one last survey of the history of humanity ("the long train / of ages"). As that history "glides away," all the "sons of men" pass away too.Lines 68-70The youth in life’s green spring, and he who goesIn the full strength of years, matron and maid,The speechless babe, and the gray-headed man—∙Once more: everyone dies. Young people, in the "green spring" of life, willeventually die.∙People in the prime ("full strength") of life will die too.∙The old woman ("matron") will die, but so will the young woman ("maid").∙The same goes for babies who are too young to talk and old men with grey hair.∙Death is the great equalizer. Doesn’t matter who or when or what you are – you will die.Lines 70-72Shall one by one be gathered to thy side,By those, who in their turn shall follow them.∙All the people listed in lines 68-70 are going to come and lie down next to you in the earth. They will be laid in the grave by people who will then eventually diethemselves. It’s an endless chain, all of us following each other into the grave,whether we like it or notLines 73-81 SummaryGet out the microscope, because we’re going through this poem line-by-line. Lines 73-74So live, that when thy summons comes to joinThe innumerable caravan, which moves∙If that was the end of the poem, it would be pretty depressing. Now, though, all ofa sudden, Bryant switches the mood up a little.∙The speaker says: "So live." Enjoy the time you have. Sooner or later you will hear the call ("the summons") of death. You will join the endless train of people leaving this life. We’re still talking about death, but there’s some hope, a reminder of theimportance of life.Lines 75-77To that mysterious realm, where each shall takeHis chamber in the silent halls of death,Thou go not, like the quarry-slave at night,∙We’re all headed for what our speaker calls "that mysterious realm," whatShakespeare called "that undiscovered country." We’re all going to get a room("chamber") in the quiet "halls of death."∙Still, we shouldn’t go as if we were being forced, like slaves in darkness. Lines 78-79Scourged to his dungeon, but, sustained and soothedBy an unfaltering trust, approach thy grave,∙Instead of acting like we are being whipped ("scourged") into some underground prison, we should trust that what is happening to us is good. We should be comforted and soothed by our belief in the comfort and rightness of death.∙Notice that he doesn’t say exactly what we should be trusting in. This isimportant. There’s no clear religious message here, just some general comfort. Lines 80-81Like one who wraps the drapery of his couchAbout him, and lies down to pleasant dreams.We end the poem with an image that we think is actually really beautiful. After all that grim contemplation of death, the speaker closes things on a soothing, comforting note. He says that we should get ready to die like someone wrapping a blanket ("drapery") around him and getting ready for a happy, dream-filled sleep. Kind of nice, huh?。