Mrbayes中文使用说明

叶贝斯公式的原理及应用1. 叶贝斯公式的原理叶贝斯公式是一种统计学中常用的公式，用于计算在已知条件下发生某个事件的概率。

它基于贝叶斯定理，将先验概率与后验概率结合起来，从而得到一个更准确的概率估计。

叶贝斯公式的数学表达为：P(A|B) = P(B|A) * P(A) / P(B)其中，P(A|B)表示在事件B发生的条件下事件A发生的概率，P(B|A)表示在事件A发生的条件下事件B发生的概率，P(A)和P(B)分别表示事件A和事件B的概率。

叶贝斯公式的原理是基于条件概率的推导，通过已知信息来计算未知信息的概率。

它常用于分类问题、信息检索等领域。

2. 叶贝斯公式的应用叶贝斯公式在实际应用中有着广泛的应用，下面列举了一些常见的应用场景。

2.1 文本分类叶贝斯公式在文本分类中有着重要的应用。

通过统计文本中不同单词的出现频率，可以计算出不同类别的文本在某个单词出现的条件概率。

然后使用叶贝斯公式来计算给定一段待分类的文本属于某个类别的概率，从而实现文本分类的任务。

2.2 垃圾邮件过滤叶贝斯公式在垃圾邮件过滤中也被广泛应用。

通过统计已知分类的邮件中不同单词的出现频率，可以计算出某个单词在垃圾邮件中出现的条件概率和在非垃圾邮件中出现的条件概率。

然后使用叶贝斯公式来计算一封未知分类的邮件是垃圾邮件的概率，从而进行垃圾邮件过滤。

2.3 医学诊断叶贝斯公式在医学诊断中也有着重要的应用。

通过统计不同疾病患者的症状出现频率，可以计算出某个症状在某个疾病中出现的条件概率。

然后使用叶贝斯公式来计算一个患者患有某个疾病的概率，从而辅助医生进行准确定断。

2.4 信息检索叶贝斯公式在信息检索中也有着重要的应用。

通过统计文档中不同单词的出现频率，可以计算出某个单词在某个类别的文档中出现的条件概率。

然后使用叶贝斯公式来计算一个查询词为某个类别的文档的概率，从而进行信息检索。

3. 总结叶贝斯公式是一种重要的统计学公式，它基于贝叶斯定理，将先验概率与后验概率结合起来计算事件发生的概率。

How to download and compile the most recent version of MrBayes 3.2 (Mac and Unix) from the MrBayes svn repository on SourceForge1. If a Mac, open Terminal (located in Applications/Utilities). Then check that you have gcc installed by typing$ which gccThis should result in the directory location of your current copy of gcc, if you have one installed. If not, install one from the Developer Tools CD that came with your computer. Most Unix systems will already have gcc installed.2. Type (on one line):svn co https:///svnroot/mrbayes/trunk/src mrbayesYou should now get a number of files downloaded to your directory, in a folder named ”mrbayes”.3. Change to the mrbayes directory by typing:$ cd mrbayes4. Create the Makefile, which contains the instructions for the compiler (the ”make” command), by typing:$ ./configure5. Now compile the program by typing:$ makeIt will take a few minutes for the compiler to assemble the binary version of the program.6. Run the program by typing:$ ./mbYou may want to put the executable in your path. Consult a Unix savvy person on how to do this.Compiling and running the MPI version of MrBayes1. Download the source code and shift to the ”mrbayes” directory as described above.2. In step 4, use the following command to create the Makefile instead:$ ./configure --enable-mpi=yes3. Now compile the program by typing$ makeIt will take a few minutes for the compiler to assemble the binary version of the program. If the first entry on each line printed during the compilation step is ”mpicc”, you are compiling the parallel version. If it is ”gcc”, something went wrong during the configure step and you are compiling the serial version instead.If you have already compiled the serial version of the program in the same directory, you first need to remove the compiled objects by running$ make cleanThen you run the ”make” command as above. Note that the compiled program is going to be called ”mb” both for the serial and the parallel version, so the compilation of the parallel version will overwrite the serial version unless you rename or move the latter executable first.4. Run the parallel version of the program using the command$ mpirun -np 2 ./mbwhere 2 is the number of available processors or processor cores. The MrBayes header should say that you are running the parallel version and it should also give the number of processors (cores) available.5. In practical use, it is often convenient to run the MPI version of MrBayes in batch mode. For instance, you can prepare a Nexus batch file ”batch.nex”, which contains a MrBayes block. To use such a file and have the screen output written to the log file”log.txt”, use the command:$ mpirun -np 2 ./mb batch.nex > log.txt &You can now look into the end of the log file every now and then to see what the run is doing currently using$ tail log.txtIf you wish to continuously follow what is being printed to the log file, you can use$ tail -f log.txtThere are many other ways of running the MPI version of MrBayes. Clusters often come with special instructions on how to run mpi programs; they typically involve launching the MrBayes MPI runs through an appropriate script. Consult your supercomputer support for instructions.。

输入Help，窗口列出命令列表。

Help <command>，单命令介绍，包括该命令当前状态。

如：help lset。

Manual，在mrbayes文件夹会产生一个命令详细介绍的文件。

（依次输入命令，完成简单也最常用的分析）：Execute filename.nex，打开待分析文件，文件必须和mrbayes程序在同一目录下。

Lset nst=6 rates=invgamma，该命令设置进化模型为with gamma-distributed rate variation across sites和a proportion of invariable sites的GTR模型。

模型可根据需要更改，不过一般无须更改。

mcmc ngen=10000 samplefreq=10，保证在后面的可能性分布中probability distribution至少取到1000个样品。

默认取样频率：every 100th generation。

如果分裂频率分支频率split frequencies的标准偏差standard deviation在100,000代generations以后低于0.01，当程序询问：“Continue the analysis?(yes/no)”，回答no；如果高于0.01，yes继续直到该值低于0.01。

sump burnin=250（在此为1000个样品，即任何相当于你取样的25％的值），参数总结summarize the parameter，程序会输出一个关于样品（sample）的替代模型参数的总结表，包括mean，mode和95 % credibility interval ofeach parameter，要保证所有参数PSRF（the potential scale reduction factor）的值接近1.0，如果不接近，分析时间要延长。

sumt burnin=250，总结树summarize tree。

派利斯中文使用手册第一章：介绍派利斯是一种强大的翻译工具，可以帮助用户实时翻译各种语言。

本手册将向您介绍派利斯的基本功能和使用方法。

第二章：安装和登录3.注册成功后，使用您的账户信息登录派利斯。

第三章：主要功能1.实时翻译：打开派利斯应用程序后，在输入框中输入您要翻译的文字或语句。

选择源语言和目标语言，然后点击“翻译”按钮即可实时翻译。

3.语音翻译：选择“语音翻译”功能。

按住录音按钮，说出要翻译的语句，松开按钮。

派利斯将尝试将语音转化为文字，并进行翻译。

第四章：高级功能3.字典功能：在派利斯应用程序中，选择“字典”功能。

输入要查询的单词或词组，派利斯将提供释义和示例用法。

第五章：常见问题1.为什么翻译结果不准确？派利斯尽力提供准确的翻译，但由于语言的复杂性和多义性，翻译结果可能存在误差。

2.如何提升翻译准确度？可以参考翻译结果的上下文，或尝试将长句拆分为更简洁的表达方式进行翻译。

第六章：使用技巧1.精确翻译：尽量提供清晰、准确的输入文本，以获取更准确的翻译结果。

2.听写模式：选择“语音翻译”功能后，点击“听写”按钮。

派利斯将在最终翻译前提供一段时间供您确认听写结果是否准确。

3.手写输入：在选择源语言后，可以通过点击键盘按钮来切换到手写输入模式，然后使用手指书写字母和汉字。

第七章：常用短语1.问候和礼节用语2.旅行用语3.饮食用语第八章：常用表达1.感谢与道歉2.询问与告知3.请求与回答第九章：技术支持本手册为派利斯中文使用手册，介绍了派利斯的基本功能、高级功能、常见问题、使用技巧以及常用短语和表达等内容。

希望通过本手册，用户能够更好地使用派利斯进行准确、便捷的翻译。

Package‘BayesSampling’October12,2022Type PackageTitle Bayes Linear Estimators for Finite PopulationVersion1.1.0Date2021-04-24Maintainer Pedro Soares Figueiredo<*********************>Description Allows the user to apply the Bayes Linear approach tofinite population with the Sim-ple Random Sampling-BLE_SRS()-andthe Stratified Simple Random Sampling design-BLE_SSRS()-(both without replacement),to the Ratio estimator(using auxiliaryinformation)-BLE_Ratio()-and to categorical data-BLE_Categorical().The Bayes linear estimation approach is applied to a general linear regression model forfi-nite population prediction in BLE_Reg()and it is also possible to achieve the design based estimators using vague prior distributions.Based on Gonçalves,K.C.M,Moura,F.A.S and Migon,H.S.(2014)<https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886, https:///pedrosfig/BayesSamplingLicense GPL-3Encoding UTF-8LazyData trueRoxygenNote7.1.1Depends R(>=3.5)Imports MASS,Matrix,stats,matrixcalcSuggests knitr,rmarkdown,TeachingSamplingVignetteBuilder knitrLanguage en-USNeedsCompilation noAuthor Pedro Soares Figueiredo[aut,cre](<https:///0000-0003-2279-2881>),Kelly C.M.Gonçalves[aut,ths](<https:///0000-0002-4524-547X>)12BigCityRepository CRANDate/Publication2021-05-0119:00:02UTCR topics documented:BigCity (2)BLE_Categorical (3)BLE_Ratio (4)BLE_Reg (6)BLE_SRS (7)BLE_SSRS (9)C (10)create1 (11)E_beta (11)E_theta_Reg (12)T_Reg (12)VT_Reg (13)V_beta (13)V_theta_Reg (14)Index15 BigCity Full Person-level Population DatabaseDescriptionThis data set corresponds to some socioeconomic variables from150266people of a city in a par-ticular year.Usagedata(BigCity)FormatA data.frame with150266rows and12variables:HHID The identifier of the household.It corresponds to an alphanumeric sequence(four letters andfive digits).PersonID The identifier of the person within the household.NOTE it is not a unique identifier of a person for the whole population.It corresponds to an alphanumeric sequence(five letters and two digits).Stratum Households are located in geographic strata.There are119strata across the city.PSU Households are clustered in cartographic segments defined as primary sampling units(PSU).There are1664PSU and they are nested within strata.BLE_Categorical3Zone Segments clustered within strata can be located within urban or rural areas along the city.Sex Sex of the person.Income Per capita monthly income.Expenditure Per capita monthly expenditure.Employment A person’s employment status.Poverty This variable indicates whether the person is poor or not.It depends on income. Sourcehttps:///package=TeachingSamplingReferencesPackage‘TeachingSampling’;see BigCityBLE_Categorical Bayes Linear Method for Categorical DataDescriptionCreates the Bayes Linear Estimator for Categorical DataUsageBLE_Categorical(ys,n,N,m=NULL,rho=NULL)Argumentsys k-vector of sample proportion for each category.n sample size.N total size of the population.m k-vector with the prior proportion of each strata.If NULL,sample proportion for each strata will be used(non-informative prior).rho matrix with the prior correlation coefficients between two different units within categories.It must be a symmetric square matrix of dimension k(or k-1).IfNULL,non-informative prior will be used.ValueA list containing the following components:•est.prop-BLE for the sample proportion of each category•Vest.prop-Variance associated with the above•Vs.Matrix-Vs matrix,as defined by the BLE method(should be a positive-definite matrix)•R.Matrix-R matrix,as defined by the BLE method(should be a positive-definite matrix)Sourcehttps://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886ReferencesGonçalves,K.C.M,Moura,F.A.S and Migon,H.S.(2014).Bayes Linear Estimation for Finite Pop-ulation with emphasis on categorical data.Survey Methodology,40,15-28.Examples#2categoriesys<-c(0.2614,0.7386)n<-153N<-15288m<-c(0.7,0.3)rho<-matrix(0.1,1)Estimator<-BLE_Categorical(ys,n,N,m,rho)Estimatorys<-c(0.2614,0.7386)n<-153N<-15288m<-c(0.7,0.3)rho<-matrix(0.5,1)Estimator<-BLE_Categorical(ys,n,N,m,rho)Estimator#3categoriesys<-c(0.2,0.5,0.3)n<-100N<-10000m<-c(0.4,0.1,0.5)mat<-c(0.4,0.1,0.1,0.1,0.2,0.1,0.1,0.1,0.6)rho<-matrix(mat,3,3)BLE_Ratio Ratio BLEDescriptionCreates the Bayes Linear Estimator for the Ratio"estimator"UsageBLE_Ratio(ys,xs,x_nots,m=NULL,v=NULL,sigma=NULL,n=NULL)Argumentsys vector of sample observations or sample mean(sigma and n parameters will be required in this case).xs vector with values for the auxiliary variable of the elements in the sample or sample mean.x_nots vector with values for the auxiliary variable of the elements not in the sample.m prior mean for the ratio between Y and X.If NULL,mean(ys)/mean(xs)will be used(non-informative prior).v prior variance of the ratio between Y and X(bigger than sigma^2).If NULL,it will tend to infinity(non-informative prior).sigma prior estimate of variability(standard deviation)of the ratio within the popula-tion.If NULL,sample variance of the ratio will be used.n sample size.Necessary only if ys and xs represent sample means(will not be used otherwise).ValueA list containing the following components:•est.beta-BLE of Beta•Vest.beta-Variance associated with the above•est.mean-BLE for each individual not in the sample•Vest.mean-Covariance matrix associated with the above•est.tot-BLE for the total•Vest.tot-Variance associated with the aboveSourcehttps://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886ReferencesGonçalves,K.C.M,Moura,F.A.S and Migon,H.S.(2014).Bayes Linear Estimation for Finite Pop-ulation with emphasis on categorical data.Survey Methodology,40,15-28.Examplesys<-c(10,8,6)xs<-c(5,4,3.1)x_nots<-c(1,20,13,15,-5)m<-2.5v<-10sigma<-2Estimator<-BLE_Ratio(ys,xs,x_nots,m,v,sigma)Estimator6BLE_Reg #Same example but informing sample means and sample size instead of sample observations ys<-mean(c(10,8,6))xs<-mean(c(5,4,3.1))n<-3x_nots<-c(1,20,13,15,-5)m<-2.5v<-10sigma<-2Estimator<-BLE_Ratio(ys,xs,x_nots,m,v,sigma,n)EstimatorBLE_Reg General BLE caseDescriptionCalculates the Bayes Linear Estimator for Regression models(general case)UsageBLE_Reg(ys,xs,a,R,Vs,x_nots,V_nots)Argumentsys response variable of the samplexs explicative variable of the samplea vector of means from BetaR covariance matrix of BetaVs covariance of sample errorsx_nots values of X for the individuals not in the sampleV_nots covariance matrix of the individuals not in the sampleValueA list containing the following components:•est.beta-BLE of Beta•Vest.beta-Variance associated with the above•est.mean-BLE of each individual not in the sample•Vest.mean-Covariance matrix associated with the above•est.tot-BLE for the total•Vest.tot-Variance associated with the aboveSourcehttps://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886ReferencesGonçalves,K.C.M,Moura,F.A.S and Migon,H.S.(2014).Bayes Linear Estimation for Finite Pop-ulation with emphasis on categorical data.Survey Methodology,40,15-28.Examplesxs<-matrix(c(1,1,1,1,2,3,5,0),nrow=4,ncol=2)ys<-c(12,17,28,2)x_nots<-matrix(c(1,1,1,0,1,4),nrow=3,ncol=2)a<-c(1.5,6)R<-matrix(c(10,2,2,10),nrow=2,ncol=2)Vs<-diag(c(1,1,1,1))V_nots<-diag(c(1,1,1))Estimator<-BLE_Reg(ys,xs,a,R,Vs,x_nots,V_nots)EstimatorBLE_SRS Simple Random Sample BLEDescriptionCreates the Bayes Linear Estimator for the Simple Random Sampling design(without replacement)UsageBLE_SRS(ys,N,m=NULL,v=NULL,sigma=NULL,n=NULL)Argumentsys vector of sample observations or sample mean(sigma and n parameters will be required in this case).N total size of the population.m prior mean.If NULL,sample mean will be used(non-informative prior).v prior variance of an element from the population(bigger than sigma^2).If NULL, it will tend to infinity(non-informative prior).sigma prior estimate of variability(standard deviation)within the population.If NULL, sample variance will be used.n sample size.Necessary only if ys represent sample mean(will not be used otherwise).ValueA list containing the following components:•est.beta-BLE of Beta(BLE for every individual)•Vest.beta-Variance associated with the above•est.mean-BLE for each individual not in the sample•Vest.mean-Covariance matrix associated with the above•est.tot-BLE for the total•Vest.tot-Variance associated with the aboveSourcehttps://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886ReferencesGonçalves,K.C.M,Moura,F.A.S and Migon,H.S.(2014).Bayes Linear Estimation for Finite Pop-ulation with emphasis on categorical data.Survey Methodology,40,15-28.Examplesys<-c(5,6,8)N<-5m<-6v<-5sigma<-1Estimator<-BLE_SRS(ys,N,m,v,sigma)Estimator#Same example but informing sample mean and sample size instead of sample observations ys<-mean(c(5,6,8))N<-5n<-3m<-6v<-5sigma<-1Estimator<-BLE_SRS(ys,N,m,v,sigma,n)EstimatorBLE_SSRS Stratified Simple Random Sample BLEDescriptionCreates the Bayes Linear Estimator for the Stratified Simple Random Sampling design(without replacement)UsageBLE_SSRS(ys,h,N,m=NULL,v=NULL,sigma=NULL)Argumentsys vector of sample observations or sample mean for each strata(sigma parameter will be required in this case).h vector with number of observations in each strata.N vector with the total size of each strata.m vector with the prior mean of each strata.If NULL,sample mean for each strata will be used(non-informative prior).v vector with the prior variance of an element from each strata(bigger than sigma^2 for each strata).If NULL,it will tend to infinity(non-informative prior).sigma vector with the prior estimate of variability(standard deviation)within each strata of the population.If NULL,sample variance of each strata will be used. ValueA list containing the following components:•est.beta-BLE of Beta(BLE for the individuals in each strata)•Vest.beta-Variance associated with the above•est.mean-BLE for each individual not in the sample•Vest.mean-Covariance matrix associated with the above•est.tot-BLE for the total•Vest.tot-Variance associated with the aboveSourcehttps://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886ReferencesGonçalves,K.C.M,Moura,F.A.S and Migon,H.S.(2014).Bayes Linear Estimation for Finite Pop-ulation with emphasis on categorical data.Survey Methodology,40,15-28.10C Examplesys<-c(2,-1,1.5,6,10,8,8)h<-c(3,2,2)N<-c(5,5,3)m<-c(0,9,8)v<-c(3,8,1)sigma<-c(1,2,0.5)Estimator<-BLE_SSRS(ys,h,N,m,v,sigma)Estimator#Same example but informing sample means instead of sample observationsy1<-mean(c(2,-1,1.5))y2<-mean(c(6,10))y3<-mean(c(8,8))ys<-c(y1,y2,y3)h<-c(3,2,2)N<-c(5,5,3)m<-c(0,9,8)v<-c(3,8,1)sigma<-c(1,2,0.5)Estimator<-BLE_SSRS(ys,h,N,m,v,sigma)EstimatorC calculates the C factorDescriptioncalculates the C factorUsageC(ys,xs,R,Vs)Argumentsys response variable of the samplexs explicative variable of the sampleR covariance matrix of BetaVs covariance of sample errorscreate111 create1creates vector of1’s to be used in the estimatorsDescriptioncreates vector of1’s to be used in the estimatorsUsagecreate1(y)Argumentsy sample matrixValuevector of1’s with size equal to the number of observations in the sampleE_beta calculates the BLE for BetaDescriptioncalculates the BLE for BetaUsageE_beta(ys,xs,a,R,Vs)Argumentsys response variable of the samplexs explicative variable of the samplea vector of means from BetaR covariance matrix of BetaVs covariance of sample errors12T_Reg E_theta_Reg calculates the BLE for the individuals not in the sampleDescriptioncalculates the BLE for the individuals not in the sampleUsageE_theta_Reg(ys,xs,a,R,Vs,x_nots)Argumentsys response variable of the samplexs explicative variable of the samplea vector of means from BetaR covariance matrix of BetaVs covariance of sample errorsx_nots values of X for the individuals not in the sampleT_Reg calculates BLE for the total TDescriptioncalculates BLE for the total TUsageT_Reg(ys,xs,a,R,Vs,x_nots)Argumentsys response variable of the samplexs explicative variable of the samplea vector of means from BetaR covariance matrix of BetaVs covariance of sample errorsx_nots values of X for the individuals not in the sampleVT_Reg13 VT_Reg calculates risk matrix associated with the BLE for for the total TDescriptioncalculates risk matrix associated with the BLE for for the total TUsageVT_Reg(ys,xs,a,R,Vs,x_nots,V_nots)Argumentsys response variable of the samplexs explicative variable of the samplea vector of means from BetaR covariance matrix of BetaVs covariance of sample errorsx_nots values of X for the individuals not in the sampleV_nots covariance matrix of the individuals not in the sampleV_beta calculates the risk matrix associated with the BLE for BetaDescriptioncalculates the risk matrix associated with the BLE for BetaUsageV_beta(ys,xs,R,Vs)Argumentsys response variable of the samplexs explicative variable of the sampleR covariance matrix of BetaVs covariance of sample errors14V_theta_Reg V_theta_Reg calculates the risk matrix associated with the BLE for the individualsnot in the sampleDescriptioncalculates the risk matrix associated with the BLE for the individuals not in the sampleUsageV_theta_Reg(ys,xs,R,Vs,x_nots,V_nots)Argumentsys response variable of the samplexs explicative variable of the sampleR covariance matrix of BetaVs covariance of sample errorsx_nots values of X for the individuals not in the sampleV_nots covariance matrix of the individuals not in the sampleIndex∗datasetsBigCity,2BigCity,2,3BLE_Categorical,3BLE_Ratio,4BLE_Reg,6BLE_SRS,7BLE_SSRS,9C,10create1,11E_beta,11E_theta_Reg,12T_Reg,12V_beta,13V_theta_Reg,14VT_Reg,1315。

Mrbayes使用说明Mrbayes(运行过)文件格式为.nex，转化方法：将比对后利用DnaSp输出的NEXUS数据按如图1（tcs模板）格式进行调整，需要重新比对分析，然后将序列拷贝到模板中。

其中ntax为个体数，nchar为碱基数，即包括“-”的最大碱基数。

“-”为缺失碱基位点，注意在每条碱基名称中不能出现“-”，否则无法识别。

图11．在DOS下依次输入如下命令：exe *.nex其中*为要输入的文件名，文件需转化为.nex格式。

打开后会显示“Successfully read matrix”“Exiting data block”和“Reached end of file”。

2.如完成上步，输入下面命令，其中lset nst=?，数字为建模后得到的，如果建模后所得到的partition = 012012，两两相同则为2，如果partition = 000000，全一样则为1，如果partition = 012345，全不同则为6。

括号内的不用输入。

lset nst=2（hky）rates=invgamma3.成功后输入下一步：其中代表文件中外群样本的编号。

outgroup /4、成功后输入下一步，回车后如果文件夹中存在相同文件名则需要将相同文件替换掉，输入Y后回车。

mcmc ngen=1000000 samplefre=105、当要求的代数已经运行完毕，窗口会提示询问是否继续运行，如果回答yes，会要求输入继续运行的代数。

在回答之前，我们一般要先检查the average standard deviation of split frequencies的值，该值代表两个独立分析当前的相似性程度，越接近0越好，如果数值小于0.01则终止运行（一般在0.01-0.05之间即可），但决不能大于0.1，否则继续加10万运行。

sump burnin=25000（注：这里的burnin为buRnin,而不是buMin）6.这时会出现mybayes,输入下一步。