应用R软件metamisc程序包及CopulaREMADA程序包实现诊断准确性试验的Meta分析

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r语言performanceanalytics包的用法 -回复

r语言performanceanalytics包的用法-回复r语言是一种广泛使用的统计分析和数据可视化工具，拥有众多的功能强大的包。

其中，performanceanalytics包是一款专门用于金融分析和投资组合评估的包。

本文将一步一步回答如何使用performanceanalytics 包进行金融分析和投资组合评估。

一、安装和加载performanceanalytics包要使用performanceanalytics包，首先需要将其安装在R环境中。

在安装前，需要先安装依赖包quantmod和foreach。

安装完依赖包后，可以通过以下命令安装performanceanalytics包：Rinstall.packages("performanceanalytics")安装完成后，可以通过以下命令加载performanceanalytics包：Rlibrary(performanceanalytics)二、创建时间序列数据在进行金融分析和投资组合评估之前，首先需要准备好时间序列数据。

时间序列数据可以通过以下几种方式创建：1. 从.csv文件中导入数据如果数据已经保存在.csv文件中，可以使用read.csv函数将数据导入R 环境，然后使用as.timeSeries函数创建时间序列对象。

例如，假设我们有一个文件名为data.csv，其中包含我们要分析的数据。

可以使用以下代码将数据导入R环境：Rdata <- read.csv("data.csv")data <- as.timeSeries(data)2. 从Yahoo Finance下载数据如果数据需要从Yahoo Finance下载，可以使用quantmod包的getSymbols函数下载数据，然后使用as.timeSeries函数创建时间序列对象。

例如，以下代码将下载苹果公司（AAPL）的股票数据：Rlibrary(quantmod)getSymbols("AAPL")data <- as.timeSeries(AAPL)3. 手动创建数据如果数据量较小，也可以手动创建数据。

r语言外部验证组校准曲线

r语言外部验证组校准曲线R语言提供了多种方法来进行外部验证和校准曲线的计算和分析。

在统计建模和机器学习中，外部验证方法是用来估计模型在新数据上的性能和泛化能力的一种重要方法。

校准曲线则是用来评估模型的准确性和可靠性的一种图形化工具。

外部验证常用的方法有留出法、交叉验证和自助法。

留出法是将数据集按照一定比例划分为训练集和测试集，训练集用于建立模型，测试集用于评估模型的性能。

交叉验证则是将数据集划分为k个大小相等的子集，每次使用其中k-1个子集作为训练集，剩下的那个子集作为测试集，重复k次，最后将k次的结果取平均值作为模型的性能评估。

自助法则是从原始数据集中有放回地抽取样本构建训练集，未被抽到的样本作为测试集，重复多次构建模型和测试的过程。

R语言提供了众多的包和函数来进行外部验证和计算校准曲线的操作。

其中，caret包是一个非常流行和全面的包，提供了各种内置的外部验证方法和函数，如createDataPartition()函数用于创建数据集的训练集和测试集划分，train()函数用于训练模型，predict()函数用于预测新数据，confusionMatrix()函数用于计算模型的混淆矩阵等。

除了caret包，R语言还有其他一些包也提供了外部验证和校准曲线的功能，如boot包提供了自助法的实现，ROCR包提供了绘制校准曲线的函数。

在这些包的帮助文档中，可以找到详细的使用方法和示例代码来进行验证和曲线分析。

通过外部验证和校准曲线的分析，我们可以评估模型在新数据上的性能表现，检验模型是否过拟合或欠拟合，选择最优的模型参数和变量等。

这些方法和工具有助于我们更全面和准确地评估和改进模型，提高模型的预测能力和应用效果。

总之，R语言提供了丰富的方法和工具来进行外部验证和校准曲线的计算和分析。

通过这些方法和工具，我们可以更好地评估和改进模型，提高模型的泛化能力和应用效果。

copula r语言参数估计方法

在深度学习的数据分析中，参数估计方法是一项至关重要的工作。

在R语言中，参数估计方法有多种，其中copula是一种非常重要的方法之一。

本文将对copula在R语言中的参数估计方法进行深入探讨，以便读者更好地理解并运用这一方法。

1. copula的概念和应用copula是一种用来描述随机变量联合分布的方法。

它的重要性在于可以独立变换边际分布和相关关系，从而更灵活地建模多维随机变量之间的依赖关系。

在实际数据分析中，copula方法被广泛应用于金融风险管理、气象预测、医学统计等领域。

2. copula在R语言中的参数估计方法在R语言中，对copula进行参数估计主要使用copula包。

该包提供了各种参数估计的方法，如极大似然估计、矩方法等。

其中，极大似然估计是最常用的方法之一，通过最大化样本的似然函数来估计copula的参数。

3. 极大似然估计方法的实现步骤- 数据准备：首先需要加载相关的R包，然后准备好待分析的数据集。

- 模型选择：根据具体的数据特点和研究目的选择合适的copula模型。

- 参数估计：利用copula包中的相应函数进行参数的极大似然估计。

- 参数诊断：对估计的参数进行诊断和检验，确保参数估计的准确性和可靠性。

4. copula参数估计的应用案例分析为了更好地说明copula在R语言中的参数估计方法，我们以金融风险管理为例进行实际应用。

假设我们需要分析股票收益率之间的相关性，我们可以使用copula方法来建模多个股票收益率之间的依赖关系，从而更准确地评估投资组合的风险。

5. 个人观点和理解作为一种灵活而有效的参数估计方法，copula在R语言中的应用为我们提供了更多的数据建模选择。

通过合理选择copula模型和有效进行参数估计，我们可以更好地理解和应用多维随机变量之间的依赖关系，从而提高数据分析的深度和广度。

总结回顾本文对copula在R语言中的参数估计方法进行了全面的探讨，包括概念和应用、极大似然估计方法的实现步骤、以及应用案例分析。

详解RStudio_中使用lm_函数及summary_函数建模与模型检验的输出结果

DOI ：10.15913/ki.kjycx.2024.06.009详解RStudio中使用lm函数及summary函数建模与模型检验的输出结果廖海燕（韶关学院数学与统计学院，广东韶关 512005）摘要：使用RStudio ，通过各种随机函数生成样本数据，再使用stats 包的lm 函数及summary 函数建立线性回归模型，并对其输出结果的各项细则详细解读，叙述所用的理论与公式，并尝试用各种方法重新编程，从而对这个函数的建模原理得到更好的把握，能有助于更好地使用此函数建立合适的模型，并灵活地利用RStudio 编程实现各种建模需要的输出结果。

关键词：RStudio ；lm 函数；summary 函数；随机函数中图分类号：TP312.1 文献标志码：A 文章编号：2095-6835（2024）06-0036-03对于一份分析关于某变量影响因素的数据，倘若尝试拟合回归模型，可以考虑使用RStudio 中stats 包的lm 函数，但是经过研究发现，尚没有对于该函数各项输出结果的详细说明。

本文通过随机函数生成样本数据，再使用stats 包的lm 函数及summary 函数建立线性回归模型，并对其输出结果的各项细则详细解读，叙述所用的理论与公式。

问题为尝试拟合因变量Y 与自变量X 1，X 2，X 3，…，X p 之间的线性回归模型，模型如下所示：Y =β0+β1X 1+β2X 2+β3X 3+…+βp X p +ε （1）éëêêêêêêêêùûúúúúúúúúY 1 X 11 X 12 X 13 ⋯ X 1p Y 2 X 21 X 22 X 23 ⋯ X 2p ⋮Y n X n 1 X n 2 X n 3 ⋯ X np （2）将样本数据矩阵式（2）代入式（1），得到结果如式（3）所示：ìíîïïïïïY 1=β0+β1X 11+β2X 12+β3X 13+…+βp X 1p +ε1Y 2=β0+β1X 21+β2X 22+β3X 23+…+βp X 2p +ε2⋮Y n=β0+β1X n 1+β2X n 2+β3X n 3+…+βp X np +εn （3）式（3）的建模假定如下：误差ε1，ε2，ε3，…，εn ~iidN （0，σ2）。

R语言meta分析（1）meta包

R语言meta分析（1）meta包介绍从广义上讲，meta分析是指将几项研究结果结合起来的统计分析。

这一术语是由统计学家Gene V Glass在1976年向美国教育研究协会发表演讲中创造的。

从那时起，meta分析不仅成为医学研究的重要工具，而且在经济学，金融学，社会科学和工程学中也越来越受欢迎。

许多负责制定循证医学标准的组织，例如英国国家健康和护理卓越研究所（NICE），广泛使用meta分析。

meta分析在医学中的应用是比较直观的，比如说测试相对于标准治疗的新疗法活着新药物的功效。

现实研究中，大多数研究受限于研究条件，得到样品或者患者数目相对较少，例如，目前在上列出的最大的四项呼吸道疾病试验也仅仅有533名患者入组。

因此我们需要使用“所有信息来源”来获得更准确的结果。

但是，meta分析需要建立严格的搜索相关研究的系统评价标准。

研究者必须努力避免“选择偏倚”，“发表偏倚”和其他偏倚。

优点1）能对同一课题的多项研究结果的一致性进行评价；2）对同一课题的多项研究结果作系统性评价和总结；3）提出一些新的研究问题，为进一步研究指明方向；4）当受制于某些条件时，如时间或研究对象的限制，meta分析不失为一种选择；5）从方法学的角度，对现阶段某课题的研究设计进行评价；6）发现某些单个研究未阐明的问题；7）对小样本的临床实验研究，meta分析可以统计效能和效应值估计的精确度。

因此，设计合理，严密的meta分析文章能对证据进行更客观的评价（与传统的描述性的综述相比），对效应指标进行更准确、客观的评估，并能解释不同研究结果之间的异质性。

meta分析符合人们对客观规律的认识过程，是与循证医学的思想完全一致的，是一个巨大的进步。

主要步骤1.明确简洁地提出需要解决的问题2.制定检索策略，全面广泛地收集随机对照试验3.确定纳入和排除标准，剔除不符合要求的文献4.资料选择和提取，包括原文的结果数据、图表等5.各试验的质量评估和特征描述6.统计学处理7.结果解释、作出结论及评价8.维护和更新资料。

R软件实现meta分析

Package‘meta’January12,2010Title Meta-Analysis with RVersion1.1-8Depends R(>=2.9.1),gridAuthor Guido Schwarzer<sc@imbi.uni-freiburg.de>Maintainer Guido Schwarzer<sc@imbi.uni-freiburg.de>Date2010-01-12Description Fixed and random effects meta-analysis.Functions for tests of bias,forest and funnel plot. License GPL(>=2)Repository CRANDate/Publication2010-01-1213:14:17R topics documented:addvar (2)ci (3)Fleiss93 (4)Fleiss93cont (5)forest (6)funnel (10)funnel.meta (12)labbe (16)labbe.metabin (17)metabias (20)metabin (22)metacont (27)metacr (29)metacum (31)metagen (33)metainf (35)12addvar metaprop (37)Olkin95 (39)plot.meta (40)print.meta (43)read.mtv (46)read.rm5 (48)trimﬁll (51)trimﬁll.meta (53)Index56 addvar Additional functions for objects of class metaDescriptionThe as.data.frame method returns a data frame containing information on individual studies,e.g.,estimated treatment effect and its standard error.The function addvar can be used to add asingle variable to an object of class meta which for example is useful to conduct sub-group analysis or meta-regression.Usage##S3method for class'meta':as.data.frame(x,s=NULL,optional=FALSE,...)addvar(x,y,varname,by.x="studlab",by.y=by.x)Argumentsx An object of class meta.s NULL or a character vector giving the row names for the data frame.optional logical.If TRUE,setting row names and converting column names(to syntactic names)is optional.y A data frame with an additional covariatevarname A character specifying name of additional variableby.x,by.y Speciﬁcations of the common columns(see merge)...other argumentsValueA data frame is returned by the function as.data.frame.A single covariate is returned by the function addvar which can be added to an object of classmeta.Internally,the merge function is utilised.ci3Author(s)Guido Schwarzer<sc@imbi.uni-freiburg.de>See Alsometabin,metacont,metagenExamplesdata(Fleiss93cont)meta1<-metacont(n.e,mean.e,sd.e,n.c,mean.c,sd.c,study,data=Fleiss93cont,sm="SMD")##Generate additional variable#Fleiss93cont$group<-c(1,2,1,1,2)##Generate new variable by merging#object'meta1'and data frame'Fleiss93cont'#meta1$group<-addvar(meta1,Fleiss93cont,"group",by.y="study")as.data.frame(meta1)summary(meta1,byvar=group)ci Calculation of conﬁdence intervals(normal approximation)DescriptionCalculation of conﬁdence intervals;based on normal approximation.Usageci(TE,seTE,level=0.95)ArgumentsTE Estimated treatment effect.seTE Standard error of treatment estimate.level The conﬁdence level required.ValueList with componentsTE Estimated treatment effect.seTE Standard error of treatment estimate.lower Lower conﬁdence limits.4Fleiss93upper Upper conﬁdence limits.zscore Test statistic.p P-value of test with null hypothesis TE=0.level The conﬁdence level required.NoteThis function is primarily called from other functions of the library meta,e.g.plot.meta, summary.meta.Author(s)Guido Schwarzer<sc@imbi.uni-freiburg.de>Examplesas.data.frame(ci(170,10))as.data.frame(ci(170,10,0.99))Fleiss93Aspirin after Myocardial InfarctionDescriptionMeta-analysis on Aspirin in Preventing Death after Myocardial InfarctionUsagedata(Fleiss93)FormatA data frame with the following columns:study study labelyear year of publicationevent.e number of events in experimental groupn.e number of observations in experimental groupevent.c number of events in control groupn.c number of observations in control groupSourceFleiss JL(1993),The statistical basis of meta-analysis.Statistical Methods in Medical Research,2, 121–145.Fleiss93cont5Examplesdata(Fleiss93)metabin(event.e,n.e,event.c,n.c,data=Fleiss93,studlab=paste(study,year),sm="OR",comb.random=FALSE)Fleiss93cont Mental Health TreatmentDescriptionMeta-analysis on the Effect of Mental Health Treatment on Medical UtilisationUsagedata(Fleiss93cont)FormatA data frame with the following columns:study study labelyear year of publicationn.e number of observations in experimental groupmean.e estimated mean in experimental groupsd.e standard deviation in experimental groupn.c number of observations in control groupmean.c estimated mean in control groupsd.c standard deviation in control groupSourceFleiss JL(1993),The statistical basis of meta-analysis.Statistical Methods in Medical Research,2, 121–145.See AlsoFleiss93Examplesdata(Fleiss93cont)metacont(n.e,mean.e,sd.e,n.c,mean.c,sd.c,data=Fleiss93cont,studlab=paste(study,year),comb.random=FALSE)forest Forest plot(new plot function for objects of class meta)DescriptionDraws a forest plot in the active graphics window(using grid graphics system).Usageforest(x,byvar=x$byvar,bylab=x$bylab,print.byvar=x$print.byvar,sortvar,studlab=TRUE,level=x$level,b=x$b,comb.fixed=x$comb.fixed,comb.random=x$comb.random,overall=TRUE,text.fixed="Fixed effect model",text.random="Random effects model",lty.fixed=2,lty.random=3,xlab=NULL,xlab.pos=ref,xlim,allstudies=TRUE,weight,ref=ifelse(x$sm%in%c("RR","OR","HR"),1,0),leftcols=NULL,rightcols=NULL,leftlabs=NULL,rightlabs=NULL,lab.e=x$label.e,lab.c=x$label.c,lab.e.attach.to.col=NULL,lab.c.attach.to.col=NULL,lwd=1,at=NULL,label=TRUE,fontsize=12,boxsize=0.8,plotwidth=unit(6,"cm"),colgap=unit(2,"mm"),col.i="black",col.by="darkgray",digits=2)Argumentsx An object of class meta.byvar An optional vector containing grouping information(must be of same length asx$TE).Parameter byvar can not be used if x is an object of class metacumor metainf.bylab A character string with a label for the grouping variable.print.byvar A logical indicating whether the name of the grouping variable should be printedin front of the group labels.sortvar An optional vector used to sort the individual studies(must be of same length asx$TE).studlab A logical indicating whether study labels should be printed in the graph.Avector with study labels can also be provided(must be of same length as x$TEthen).level The level used to calculate conﬁdence intervals for individual studies.b The level used to calculate conﬁdence intervals for pooled estimates.comb.fixed A logical indicating whetherﬁxed effect estimate should be plotted.comb.random A logical indicating whether random effects estimate should be plotted. overall A logical indicating whether overall summaries should be plotted.This param-eter is useful in combination with the parameter byvar if summaries shouldonly be plotted on group level.text.fixed A character string used in the plot to label the pooledﬁxed effect estimate. text.random A character string used in the plot to label the pooled random effects estimate. lty.fixed Line type(pooledﬁxed effect estimate).lty.random Line type(pooled random effects estimate).xlab A label for the x axis.xlab.pos A numeric specifying the center of the label on the x axis.xlim The x limits(min,max)of the plot.allstudies A logical indicating whether studies with inestimable treatment effects should be plotted.weight A character string indicating which type of plotting symbols is to be used for in-dividual treatment estimates.One of missing(see Details),"same","fixed",or"random",can be abbreviated.Plot symbols have the same size for all stud-ies or represent study weights fromﬁxed effect or random effects model.ref A numerical giving the reference value to be plotted as a line in the forest plot.No reference line is plotted if parameter ref is equal to NA.leftcols A character vector specifying(additional)columns to be plotted on the left side of the forest plot(see Details).rightcols A character vector specifying(additional)columns to be plotted on the right side of the forest plot(see Details).leftlabs A character vector specifying labels for(additional)columns on left side of the forest plot(see Details).rightlabs A character vector specifying labels for(additional)columns on right side of the forest plot(see Details).lab.e Label to be used for experimental group in table heading.lab.c Label to be used for control group in table heading.lab.e.attach.to.colA character specifying the column name where label lab.e should be attachedto in table heading.lab.c.attach.to.colA character specifying the column name where label lab.c should be attachedto in table heading.lwd The line width,see par.at The points at which tick-marks are to be drawn,see grid.xaxis.label A logical value indicating whether to draw the labels on the tick marks,or an ex-pression or character vector which specify the labels to use.See grid.xaxis. fontsize The size of text(in points),see gpar.boxsize A numeric used to increase or decrease the size of boxes in the forest plot.plotwidth A unit object specifying width of the forest plot.colgap A unit object specifying gap between columns printed on left and right side offorest plot.col.i The colour for individual study results and conﬁdence limits.col.by A character specifying colour to print information on subgroups.digits Minimal number of signiﬁcant digits,see print.default.DetailsA forest plot,also called conﬁdence interval plot,is drawn in the active graphics window.Sub-groupanalyses are conducted and displayed in the plot if byvar is not missing.The forest function is based on the grid graphics system.Therefore,to plot a newﬁgure inan existing graphics window,one has to use the grid.newpage function.In order to print theforest plot,(i)resize the graphics window,(ii)either use dev.copy2eps or dev.copy2pdf.For basic forest plots,the plot.meta function can be used.Information from object x is utilised if argument weight is missing.Weights from theﬁxed effectmodel are used(weight="fixed")if parameter x$comb.fixed is TRUE;weights from therandom effects model are used(weight="random")if parameter x$comb.random is TRUEand x$comb.fixed is FALSE.The parameters leftcols and rightcols can be used to specify columns which are plotted onthe left and right side of the forest plot,respectively.If these parameters are NULL,the followingdefault columns will be plotted.Parameter rightcols:(i)estimated treatment effect with level-conﬁdence interval,(ii)in ad-dition,weights of theﬁxed and/or random effects model will be given,if comb.fixed=TRUEand/or comb.random=TRUE.For an object of class metacum or metainf only the estimatedtreatment effect with level-conﬁdence interval are plotted.Parameter leftcols:(i)leftcols=c("studlab","event.e","n.e","event.c","n.c")for an object of class metabin,(ii)leftcols=c("studlab","n.e","mean.e","sd.e","n.c","mean.c","sd.c")for an object of class metacont,(iii)leftcols=c("studlab", "TE","seTE")for an object of class metagen,(iv)leftcols=c("studlab","event","n")for an object of class metaprop,(v)leftcols=c("studlab")for an object of classmetacum or metainf.The parameters leftlabs and rightlabs can be used to specify column headings which areplotted on left and right side of the forest plot,respectively.For certain columns predeﬁned labelsexist.If the parameters leftlabs and rightlabs are NULL,the following default labels willbe used:for columns c("studlab","TE","seTE","n.e","n.c","event.e","event.c","mean.e","mean.c","sd.e","sd.c","effect","ci","w.fixed","w.random")the labels c("Study","TE","seTE","Total","Total","Events","Events","Mean","Mean","SD","SD",summary measure,level for confidence interval,"W(fixed)","W(random)").For additional columns the column name willbe used as label.It is possible to only provide labels for new columns(see Examples).If parameters lab.e and lab.c are NULL,"Experimental"and"Control"are used as labels forexperimental and control group,respectively.For subgroups(argument byvar not NULL),results for theﬁxed effect model will be plotted if both arguments comb.fixed and comb.random are TRUE.In order to plot results for the random effects model within subgroups,use comb.fixed==FALSE and comb.random==TRUE.Review Manager5(RevMan5)is the current software used for preparing and maintaining Cochrane Reviews(/revman/).In RevMan5,subgroup analyses can be deﬁned and data from a Cochrane review can be imported to R using the function read.rm5.Ifa meta-analysis is then conducted using function metacr,information on subgroups is availablein R(components byvar,bylab,and print.byvar,byvar in an object of class"meta").Accordingly,by using function metacr there is no need to deﬁne subgroups in order to redo the statistical analysis conducted in the Cochrane review.Author(s)Guido Schwarzer<sc@imbi.uni-freiburg.de>See Alsoplot.meta,metabin,metacont,metagenExamplesdata(Olkin95)meta1<-metabin(event.e,n.e,event.c,n.c,data=Olkin95,subset=c(41,47,51,59),sm="RR",meth="I",studlab=paste(author,year))grid.newpage()####Do forest plot##forest(meta1,comb.fixed=TRUE,comb.random=TRUE)grid.newpage()####Change set of columns printed on left side##of forest plot##forest(meta1,comb.fixed=TRUE,comb.random=FALSE,leftcols="studlab")grid.newpage()#### 1.Change order of columns on left side## 2.Attach labels to columns'event.e'and'event.c'##instead of columns'n.e'and'n.c'##forest(meta1,10funnel leftcols=c("studlab","n.e","event.e","n.c","event.c"),lab.e.attach.to.col="event.e",lab.c.attach.to.col="event.c",comb.fixed=TRUE)Olkin95$studlab<-paste(Olkin95$author,Olkin95$year)####Add variables'year'and'author'to meta-analysis object##meta1$year<-addvar(meta1,Olkin95,"year")meta1$author<-addvar(meta1,Olkin95,"author")grid.newpage()####Specify column labels only for newly created variables##'year'and'author'##forest(meta1,leftcols=c("studlab","event.e","n.e","event.c","n.c","author","year"),leftlabs=c("Author","Year of Publ"),comb.fixed=TRUE)funnel Generic function to produce a funnel plot.DescriptionDraw a funnel or radial plot to assess funnel plot asymmetry in the active graphics window.A contour-enhanced funnel plot can be produced for assessing causes of funnel plot asymmetry. Usagefunnel(x,y,...)Argumentsx An object of class meta,or estimated treatment effect in individual studies.y Standard error of estimated treatment effect(mandatory if x not of class meta)....Graphical parameters as in par may also be passed as arguments.DetailsFor simple funnel plots,funnel.default will be used.For an object of class meta the function funnel.meta will be used instead.A funnel plot or radial plot,also called Galbraith plot,is drawn in the active graphics window.Ifcomb.fixed is TRUE,the pooled estimate of theﬁxed effect model is plotted.If level is not NULL,the corresponding conﬁdence limits are drawn.funnel11 In the funnel plot,if yaxis is"se",the standard error of the treatment estimates is plotted on the y axis which is likely to be the best choice(Sterne&Egger,2001).Other possible choices for yaxis are"invvar"(inverse of the variance),"invse"(inverse of the standard error),and "size"(study size).For yaxis!="size",contour-enhanced funnel plots can be produced(Peters et al.,2008)by specifying the contour levels(argument contour.levels).By default(argument col.contour missing),suitable gray levels will be used to distinguish the contours.Different colours can be cho-sen by argument col.contour.Author(s)Guido Schwarzer<sc@imbi.uni-freiburg.de>,Petra Graham<pgraham@.au> ReferencesGalbraith RF(1988a),Graphical display of estimates having differing standard errors.Technomet-rics,30,271–281.Galbraith RF(1988b),A note on graphical presentation of estimated odds ratios from several clini-cal trials.Statistics in Medicine,7,889–894.Light RJ&Pillemer DB(1984),Summing Up.The Science of Reviewing Research.Cambridge: Harvard University Press.Peters JL,Sutton AJ,Jones DR,Abrams KR,Rushton L(2008),Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry.Journal of Clinical Epidemiology,61,991–996.Sterne JAC&Egger M(2001),Funnel plots for detecting bias in meta-analysis:Guidelines on choice of axis.Journal of Clinical Epidemiology,54,1046–1055.See Alsometabias,funnel.default,funnel.metaExamplesdata(Olkin95)meta1<-metabin(event.e,n.e,event.c,n.c,data=Olkin95,subset=c(41,47,51,59),studlab=paste(author,year),sm="RR",meth="I")oldpar<-par(mfrow=c(2,2))####Funnel plots##funnel(meta1)####Same result as code above:##funnel(meta1$TE,meta1$seTE,sm="RR")####Funnel plot with confidence intervals,##fixed effect estimate and contours##cc<-funnel(meta1,comb.fixed=TRUE,level=0.95,contour=c(0.9,0.95,0.99))$col.contour legend(0.05,0.05,c("0.1>p>0.05","0.05>p>0.01","<0.01"),fill=cc) ####Contour-enhanced funnel plot with user-chosen colours##funnel(meta1,comb.fixed=TRUE,level=0.95,contour=c(0.9,0.95,0.99),col.contour=c("darkgreen","green","lightgreen"),lwd=2,cex=2,pch=16,studlab=TRUE,cex.studlab=1.25)legend(0.05,0.05,c("0.1>p>0.05","0.05>p>0.01","<0.01"),fill=c("darkgreen","green","lightgreen"))par(oldpar)funnel.meta Plot to assess funnel plot asymmetryDescriptionDraw a funnel or radial plot to assess funnel plot asymmetry in the active graphics window.A contour-enhanced funnel plot can be produced for assessing causes of funnel plot asymmetry.Usage##Default S3method:funnel(x,y,xlim=NULL,ylim=NULL,xlab=NULL,ylab=NULL,comb.fixed=FALSE,comb.random=FALSE,axes=TRUE,pch=21,text=NULL,cex=1,lty.fixed=2,lty.random=9,lwd=1,lwd.fixed=lwd,lwd.random=lwd,col="black",bg="darkgray",col.fixed="black",col.random="black",log="",yaxis="se",sm=NULL,contour.levels=NULL,col.contour,ref=ifelse(sm%in%c("RR","OR","HR"),1,0),level=NULL,studlab=FALSE,cex.studlab=0.8,...)##S3method for class'meta':funnel(x,y,xlim=NULL,ylim=NULL,xlab=NULL,ylab=NULL,comb.fixed=x$comb.fixed,comb.random=x$comb.random,axes=TRUE,pch=21,text=NULL,cex=1,lty.fixed=2,lty.random=9,lwd=1,lwd.fixed=lwd,lwd.random=lwd,col="black",bg="darkgray",col.fixed="black",col.random="black",log="",yaxis="se",sm=NULL,contour.levels=NULL,col.contour,ref=ifelse(x$sm%in%c("RR","OR","HR"),1,0),level=x$level,studlab=FALSE,cex.studlab=0.8,...)radial(x,y,xlim=NULL,ylim=NULL,xlab="Inverse of standard error",ylab="Standardised treatment effect(z-score)",comb.fixed=TRUE,axes=TRUE,pch=1,text=NULL,cex=1,col=NULL,level=NULL,...)Argumentsx An object of class meta,or estimated treatment effect in individual studies.y Standard error of estimated treatment effect(mandatory if x not of class meta).xlim The x limits(min,max)of the plot.ylim The y limits(min,max)of the plot.xlab A label for the x axis.ylab A label for the y axis.comb.fixed A logical indicating whether the pooledﬁxed effect estimate should be plotted.comb.random A logical indicating whether the pooled random effects estimate should be plot-ted.axes A logical indicating whether axes should be drawn on the plot.pch The plotting symbol used for individual studies.text A character vector specifying the text to be used instead of plotting symbol.cex The magniﬁcation to be used for plotting symbol.lty.fixed Line type(pooledﬁxed effect estimate).lty.random Line type(pooled random effects estimate).col A vector with colour of plotting symbols.bg A vector with background colour of plotting symbols(only used if pch in21:25).col.fixed Color of line representignﬁxed effect estimate.col.random Color of line representign random effects estimate.lwd The line width for conﬁdence intervals(if level is not NULL).lwd.fixed The line width forﬁxed effect estimate(if comb.fixed is not NULL).lwd.random The line width for random effects estimate(if comb.random is not NULL).log A character string which contains"x"if the x axis is to be logarithmic,"y"if the y axis is to be logarithmic and"xy"or"yx"if both axes are to belogarithmic(applies only to function funnel).yaxis A character string indicating which type of weights are to be used.Either"se","invvar","invse",or"size"(applies only to function funnel).sm A character string indicating underlying summary measure,e.g.,"RD","RR","OR","AS","MD","SMD"(applies only to function funnel).contour.levelsA numeric vector specifying contour levels to produce contour-enhanced funnelplot.col.contour Colour of contours.ref Reference value(null effect)used to produce contour-enhanced funnel plot.level The conﬁdence level utilised in the plot.For the funnel plot,conﬁdence limitsare not drawn if yaxis="size".studlab A logical indicating whether study labels should be printed in the graph.Avector with study labels can also be provided(must be of same length as x$TEthen).cex.studlab Size of study labels....Graphical parameters as in par may also be passed as arguments.DetailsA funnel plot or radial plot,also called Galbraith plot,is drawn in the active graphics window.Ifcomb.fixed is TRUE,the pooled estimate of theﬁxed effect model is plotted.If level is not NULL,the corresponding conﬁdence limits are drawn.In the funnel plot,if yaxis is"se",the standard error of the treatment estimates is plotted on the y axis which is likely to be the best choice(Sterne&Egger,2001).Other possible choices for yaxis are"invvar"(inverse of the variance),"invse"(inverse of the standard error),and "size"(study size).For yaxis!="size",contour-enhanced funnel plots can be produced(Peters et al.,2008)by specifying the contour levels(argument contour.levels).By default(argument col.contour missing),suitable gray levels will be used to distinguish the contours.Different colours can be cho-sen by argument col.contour.Author(s)Guido Schwarzer<sc@imbi.uni-freiburg.de>,Petra Graham<pgraham@.au>ReferencesGalbraith RF(1988a),Graphical display of estimates having differing standard errors.Technomet-rics,30,271–281.Galbraith RF(1988b),A note on graphical presentation of estimated odds ratios from several clini-cal trials.Statistics in Medicine,7,889–894.Light RJ&Pillemer DB(1984),Summing Up.The Science of Reviewing Research.Cambridge: Harvard University Press.Peters JL,Sutton AJ,Jones DR,Abrams KR,Rushton L(2008),Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry.Journal of Clinical Epidemiology,61,991–996.Sterne JAC&Egger M(2001),Funnel plots for detecting bias in meta-analysis:Guidelines on choice of axis.Journal of Clinical Epidemiology,54,1046–1055.See Alsometabias,metabin,metagenExamplesdata(Olkin95)meta1<-metabin(event.e,n.e,event.c,n.c,data=Olkin95,subset=c(41,47,51,59),studlab=paste(author,year),sm="RR",meth="I")####Radial plot##radial(meta1,level=0.95)oldpar<-par(mfrow=c(2,2))####Funnel plots##funnel(meta1)####Same result as code above:##funnel(meta1$TE,meta1$seTE,sm="RR")####Funnel plot with confidence intervals,##fixed effect estimate and contours##cc<-funnel(meta1,comb.fixed=TRUE,level=0.95,contour=c(0.9,0.95,0.99))$col.contour legend(0.05,0.05,16labbe c("0.1>p>0.05","0.05>p>0.01","<0.01"),fill=cc) ####Contour-enhanced funnel plot with user-chosen colours##funnel(meta1,comb.fixed=TRUE,level=0.95,contour=c(0.9,0.95,0.99),col.contour=c("darkgreen","green","lightgreen"),lwd=2,cex=2,pch=16,studlab=TRUE,cex.studlab=1.25) legend(0.05,0.05,c("0.1>p>0.05","0.05>p>0.01","<0.01"),fill=c("darkgreen","green","lightgreen"))par(oldpar)labbe LÁbbe plotDescriptionGeneric function for drawing a L\’Abbe plot.Usagelabbe(x,y,...)Argumentsx The x coordinates of points of the L\’Abbe plot.Alternatively,an object of class metabin.y The y coordinates of the L\’Abbe plot,optional if x is an appropriate structure....Parameters used in other L\’Abbe plot functions.DetailsGeneric function for drawing a L\’Abbe plot.Author(s)Guido Schwarzer<sc@imbi.uni-freiburg.de>ReferencesL’Abbe KA,Detsky AS,O’Rourke K(1987),Meta-analysis in clinical research.Annals of Internal Medicine,107,224–233.See Alsolabbe.metabin,metabinExamplesdata(Olkin95)meta1<-metabin(event.e,n.e,event.c,n.c,data=Olkin95,studlab=paste(author,year),sm="RR")####L'Abbe plot##labbe(meta1)labbe.metabin LÁbbe plotDescriptionDraw a L\’Abbe plot.Usage##S3method for class'metabin':labbe(x,y,xlim,ylim,xlab=NULL,ylab=NULL,TE.fixed=x$TE.fixed,TE.random=x$TE.random,comb.fixed=x$comb.fixed,comb.random=x$comb.random,axes=TRUE,pch=21,text=NULL,cex=1,col="black",bg="lightgray",lwd=1,lwd.fixed=lwd,lwd.random=lwd,lty.fixed=2,lty.random=9,sm=x$sm,weight,studlab=FALSE,cex.studlab=0.8,...)##Default S3method:labbe(x,y,xlim,ylim,xlab=NULL,ylab=NULL,TE.fixed,TE.random,comb.fixed=FALSE,comb.random=FALSE,axes=TRUE,pch=21,text=NULL,cex=1,。

R_数据处理、绘图、编程与统计检验解析

apTreeshape 进化树分析
FD
geiger
功能多样性分析
物种形成速率与进化分析
5
常用R程序包(II)
picante raster seqinr 群落系统发育多样性分析栅格数据分析与处理 DNA序列分析
sp
spatstat splancs stats Bioconductor
空间数据处理
空间点格局分析，模型拟合与检验空间与时空点格局分析 R统计学包生物学数据分析工具
！！免费、软件本身及程序包的源代码公开。
2
菜单栏快捷按钮
控制台光标:等待输入
R登陆界面(Windows版)
路径：开始>所有程序>R 2.11.0
3
R程序包（R Packages）
程序包是什么？
R程序包是多个函数的集合，具有详细的说明和示例。 Window下的R程序包是经过编译的zip包。
每个程序包包含R函数、数据、帮助文件、描述文件等。
相应的方法绘制相应的图形。这就是面向对象编程的思想。
12
R有哪些函数?
查询的方法：Help>Html help>packages log() log10() exp() sin() cos() tan() asin() acos() binom.test()
fisher.test()
chisq.test() glm(y ~ x1+x2+x3, binomial)
7
程序包使用
程序包的中函数，都要先导入，再使用，因此导入程序包是第一步。在控制台中输入如下命令： library(affy) 程序包内的函数的用法与R内置的基本函数用法一样。 library(affy)

R语言关于“包”的知识点总结

R语⾔关于“包”的知识点总结R语⾔的包是R函数，编译代码和样本数据的集合。

它们存储在R语⾔环境中名为“library”的⽬录下。

默认情况下，R语⾔在安装期间安装⼀组软件包。

随后添加更多包，当它们⽤于某些特定⽬的时。

当我们启动R语⾔控制台时，默认情况下只有默认包可⽤。

已经安装的其他软件包必须显式加载以供将要使⽤它们的R语⾔程序使⽤。

所有可⽤的R语⾔包都列在R语⾔的包。

下⾯是⽤于检查，验证和使⽤R包的命令列表。

检查可⽤R语⾔的包获取包含R包的库位置.libPaths()当我们执⾏上⾯的代码，它产⽣以下结果。

它可能会根据您的电脑的本地设置⽽有所不同。

[2] "C:/Program Files/R/R-3.2.2/library"获取已安装的所有软件包列表library()当我们执⾏上⾯的代码，它产⽣以下结果。

它可能会根据您的电脑的本地设置⽽有所不同。

Packages in library ‘C:/Program Files/R/R-3.2.2/library':base The R Base Packageboot Bootstrap Functions (Originally by Angelo Cantyfor S)class Functions for Classificationcluster "Finding Groups in Data": Cluster AnalysisExtended Rousseeuw et al.codetools Code Analysis Tools for Rcompiler The R Compiler Package获取当前在R环境中加载的所有包search()当我们执⾏上述代码时，它产⽣了以下结果。

它会根据你的个⼈电脑的本地设置⽽异。

[1] ".GlobalEnv" "package:stats" "package:graphics"[4] "package:grDevices" "package:utils" "package:datasets"[7] "package:methods" "Autoloads" "package:base"安装⼀个新的软件包有两种⽅法来添加新的R包。

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应用R软件metamisc程序包及CopulaREMADA程序包实现诊断准确性试验的Meta分析
王权;杨廉洁;何倩;喻亚宇;许杨鹏;张超
【期刊名称】《中国循证心血管医学杂志》
【年(卷),期】2016(008)004
【摘要】The Meta-analysis of diagnostic test accuracy (DTA) is a research method that comprehensive evaluates the accuracy of diagnostic test evidence. The metamisc package and CopulaREMADA package in R software are used for implementing Meta-analysis and graphic plotting of DTA based on classic frequency study approach. Compared with traditional bivariate model, the analysis models established by these two packages can reduce intergroup difference and simplify the tedious iterative operation process, which make DTA evaluation indicator more accurate and efficient.%诊断性准确性试验（diagnostic test accuracy，DTA）Meta分析是一种全面评价诊断试验证据准确性的研究方法，R软件metamisc程序包与CopulaREMADA程序包是基于经典频率学方法用于DTA Meta分析制作及图形绘制的程序包，与传统的双变量模型相比，其所建立的分析模型减少了组间差异，简化了其繁琐的迭代运算过程，使诊断试验评价指标更加准确与高效。

【总页数】4页(P392-395)
【作者】王权;杨廉洁;何倩;喻亚宇;许杨鹏;张超
【作者单位】442000 十堰，十堰市太和医院湖北医药学院附属医院口腔
科;442000 十堰，十堰市太和医院湖北医药学院附属医院院务办公室;442000 十堰，湖北医药学院口腔医学院12级;442000 十堰，湖北医药学院口腔医学院12级;442000 十堰，湖北医药学院口腔医学院12级;442000十堰，十堰市太和医院湖北医药学院附属医院循证医学中心
【正文语种】中文
【中图分类】R4
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