02_Total_Ben_Fischer

Package‘BWGS’October12,2022Type PackageTitle BreedWheat Genomic Selection PipelineVersion0.2.1Description Package for Breed Wheat Genomic Selection Pipeline.The R package'BWGS'is developed by Louis Gau-tier Tran<****************************>and Gilles Charmet<**********************>.This repository is forked from original repository<https://forgemia.inra.fr/umr-gdec/bwgs>and modified as a R package.License GPL(>=2)Encoding UTF-8LazyData trueDepends R(>=3.5.0)Imports rrBLUP,BGLR,glmnet,randomForest,brnn,e1071,stringiURL https:///byzheng/BWGSBugReports https:///byzheng/BWGS/issuesRoxygenNote7.1.1NeedsCompilation noAuthor Louis Gautier Tran[aut],Gilles Charmet[aut],Bangyou Zheng[cre]Maintainer Bangyou Zheng<**********************>Repository CRANDate/Publication2021-09-2312:40:02UTCR topics documented:bwgs.cv (2)bwgs.predict (6)inra47k (9)removeSaveAt (10)1Index11 bwgs.cv Genomic Prediction with cross validationDescriptionThe bwgs.cv function carries out cross-validation using genotypic and phenotypic data from a ref-erence population,with options for genotypic matrix processing and genomic breeding value esti-mation.Usagebwgs.cv(geno,pheno,FIXED="NULL",MAXNA=0.2,MAF=0.05,pop.reduct.method="NULL",sample.pop.size="NULL",geno.reduct.method="NULL",reduct.marker.size="NULL",pval="NULL",r2="NULL",MAP="NULL",geno.impute.method="NULL",predict.method="NULL",nFolds,nTimes)Argumentsgeno Matrix(n x m)of genotypes for the training population:n lines with m markers.Genotypes should be coded-1,0,1.Missing data are allowed and coded as NA.pheno Vector(n x1)of"phenotypes",i.e.observations or pre-processed,corrected val-ues.This vector should have no missing values,otherwise missing values(NA)will be omitted in both pheno and geno.In afirst step,bwgs.cv checks whetherrownames(geno)match with names(pheno).If not the case,the common ele-ments(intersect)are selected in both geno and pheno for further analyses.If aMAPfile is provided,the selected set of markers are also sorted out in MAP.FIXED A matrix offixed effect,to be used with some methods such as those included in BGLR,MUST have same rownames as geno and coded(-101) MAXNA The maximum proportion of missing value which is admitted forfiltering marker columns in geno.Default value is0.2MAF The minimum allele frequency forfiltering marker colums in geno;default value is0.05pop.reduct.methodMethod for reducing the size of the training population.Can be used for teach-ing purposes,no real interest in real life if the entire population is already geno-typed and phenotyped.Default value is NULL(all training set used).Proposedmethods are:•RANDOM:a subset of sample.pop.size is randomly selected for trainingthe model,and the unselected part of the population is used for validation.The process is repeated nFolds*nTimes to have the same number of repli-cates than with cross-validation.•OPTI:the optimization algorithm based on CDmean(Rincent et al2012)toselect a subset which maximizes average CD(coefficient of determination)in the validation set.Since the process is long and has some stochasticcomponent,it is repeated only nTimes.sample.pop.sizeThe size of the subset of individuals in the training set(both geno and pheno)selected by pop.reduct.method if not NULL.geno.reduct.methodAllows sampling a subset of markers for speeding up computing time and/oravoid introducing more noise than informative markers.Options are:•RMR:Random sampling(without replacement)of a subset of markers.Tobe used with the parameter“reduct.marker.size”.•LD(with r2and MAP):enables“pruning”of markers which are in LD>r2.Only the marker with the least missing values is kept for each pair in LD>r2.To allow faster computation,r2is estimated chromosome by chromosome,so a MAPfile is required with information of marker assignation to chromo-somes.The MAPfile should contain at least three columns:marker_name,chromosome_name and distance_from_origin(either genetic of physicaldistance,only used for sorting markers,LD being re-estimated from markerData).•ANO(with pval):one-way ANOV A are carried out with R function lm ontrait“pheno”.Every markers are tested one at a time,and only markerswith pvalue<pval are kept for GEBV prediction.•ANO+LD(with pval and r2,MAP is facultative):combines afirst step ofmarker selection with ANO,then a second step of pruning using LD option. reduct.marker.sizeSpecifies the number of markers for the genotypic reduction using RMR(reduct.size<m).pval p value for ANO method,0<pval<1.r2Coefficient of linkage disequilibrium(LD).Setting0<r2<1if the genotypic re-duction method is in LD or ANO+LD.MAP A matrix with markers in rows and at least ONE columns with colnames= "chrom".Used for computing r2within linkage groups.geno.impute.methodAllow missing marker data imputation using the two methods proposed in func-tion A.mat of package rrBLUP,namely:•MNI:missing data are replaced by the mean allele frequency of the marker(column in geno)•EMI:missing data are replaced using an expectation-maximization meth-ods described in function A.mat(Endelman&Janninck2012).Default value is NULL.Note that these imputation methods are only suited when there are a few miss-ing value,typically in marker data from SNP chips of KasPAR.They are NOTsuited for imputing marker data from low density to high density designs,andwhen there are MANY missing Data as typically provided by GBS.More so-phisticated software(e.g.Beagles,Browning&Browning2016)should be usedbefore BWGS.predict.method The options for genomic breeding value prediction methods.The available op-tions are:•GBLUP:performs G-BLUP using a marker-based relationship matrix,im-plemented through BGLR R-library.Equivalent to ridge regression(RR-BLUP)of marker effects.•EGBLUP:performs EG-BLUP,i.e.BLUP using a"squared"relationshipmatrix to model epistatic2x2interactions,as described by Jiang&Reif(2015),using BGLR library•RR:ridge regression,using package glmnet.In theory,strictly equivalentto gblup.•LASSO:Least Absolute Shrinkage and Selection Operator is another pe-nalized regression methods which yield more shrinked estimates than RR.Run by glmnet library.•EN:Elastic Net(Zou and Hastie,2005),which is a weighted combinationof RR and LASSO,using glmnet librarySeveral Bayesian methods,using the BGLR library:•BRR:Bayesian ridge regression:same as rr-blup,but bayesian resolution.Induces homogeneous shrinkage of all markers effects towards zero withGaussian distribution(de los Campos et al,2013)•BL:Bayesian LASSO:uses an exponential prior on marker variances pri-ors,leading to double exponential distribution of marker effects(Park&Casella2008)•BA:Bayes A uses a scaled-t prior distribution of marker effects.(Meuwis-sen et al2001).•BB:Bayes B,uses a mixture of distribution with a point mass at zero andwith a slab of non-zero marker effects with a scaled-t distribution(Habieret al2011).•BC:Bayes C same as Bayes B with a slab with Gaussian distribution.A more detailed description of these methods can be found in Perez&de losCampos2014(/BGLR-extdoc.pdf).Three semi-parametricmethods:•RKHS:reproductive kernel Hilbert space and multiple kernel MRKHS,us-ing BGLR(Gianola and van Kaam2008).Based on genetic distance and akernel function to regulate the distribution of marker effects.This methodsis claimed to be effective for detecting non additive effects.•RF:Random forest regression,using randomForest library(Breiman,2001,Breiman and Cutler2013).This methods uses regression models on treenodes which are rooted in bootstrapping data.Supposed to be able to cap-ture interactions between markers•SVM:support vector machine,run by e1071library.For details,see Chang,Chih-Chung and Lin,Chih-Jen:LIBSVM:a library for Support Vector Ma-chines .tw/~cjlin/libsvm•BRNN:Bayesian Regularization for feed-forward Neural Network,withthe R-package BRNN(Gianola et al2011).To keep computing time inreasonable limits,the parameters for the brnn function are neurons=2andepochs=20.nFolds Number of folds for the cross-validation.Smallest value recommended is nFolds =3.nTimes Number of independent replicates for the cross-validation.Smallest value rec-ommended is nTimes=3.ValueThe class bwgs.cv returns a list containing:•summary:Summary of cross-validation,including mean and standard deviation of predictive ability(i.e.correlation between phenotype and GEBV,estimated on the validation fold,then averaged over replicates(nTimes),Time taken by the computation and number of markers•cv:Vector of predictive abilities averaged over nFolds,for each of the nTimes replicates•sd:Standard deviation of the nTimes predictive abilities•MSEP:Square root of the mean-squared error of prediction,averaged over Ntimes•SDMSEP:Standard deviation of the Square root of the mean-squared error of prediction, averaged over Ntimes•bv_table:Matrix of dimension n x4.Columns are:–Real BV,i.e.pheno vector–Predict BV:the nx1vector of GEBVs–gpreSD:Standart deviation of estimated GEBV–CD:coefficient of determination for each GEBV,estimated as sqrt Note that gpredSD andCD are only available for methods using the BGLR library,namely GBLUP,EGBLUP,BA,BB,BC,BL,RKHS and MKRKHS.These two columns contain NA for methods RF,RR,LASSO,EN and SVM.Examplesdata(inra)#Cross validation using GBLUP methodcv_gblup<-bwgs.cv(TRAIN47K,YieldBLUE,geno.impute.method="mni",predict.method="gblup",nFolds=10,nTimes=1)bwgs.predict Computes the GEBV prediction for the target population with onlygenotypic Data using the options for model selection.DescriptionComputes the GEBV prediction for the target population with only genotypic Data using the options for model selection.Usagebwgs.predict(geno_train,pheno_train,geno_target,FIXED_train="NULL",FIXED_target="NULL",MAXNA=0.2,MAF=0.05,geno.reduct.method="NULL",reduct.size="NULL",r2="NULL",pval="NULL",MAP=NULL,geno.impute.method="NULL",predict.method="GBLUP")Argumentsgeno_train Matrix(n x m)of genotypes for the training population:n lines with m markers.Genotypes should be coded as-1,0,1,NA.Missing data are allowed and codedas NA.pheno_train Vector(n x1)of phenotype for the training phenotypes.This vector should have no missing values.Otherwise,missing values(NA)will be omitted in bothpheno_train and geno_train.geno_target Matrix(z x m)of genotypes for the target population:z lines with the same m markers as in geno_train.Genotypes should be coded as-1,0,1,NA.Missingdata are allowed and coded as NA.Other arguments are identical to those ofbwgs.cv,except pop_reduct_method,nTimes and nFolds,since the prediction isrun only once,using the whole training population for model estimation,thenapplied to the target population.FIXED_train A matrix offixed effect for training,to be used with some methods such as those included in BGLR,MUST have same rownames as geno and coded(-101)FIXED_target A matrix offixed effect for targeting,to be used with some methods such as those included in BGLR,MUST have same rownames as geno and coded(-101)MAXNA The maximum proportion of missing value which is admitted forfiltering marker columns in geno.Default value is0.2MAF The minimum allele frequency forfiltering marker colums in geno;default value is0.05geno.reduct.methodAllows sampling a subset of markers for speeding up computing time and/oravoid introducing more noise than informative markers.Options are:•RMR:Random sampling(without replacement)of a subset of markers.Tobe used with the parameter“reduct.marker.size”.•LD(with r2and MAP):enables“pruning”of markers which are in LD>r2.Only the marker with the least missing values is kept for each pair in LD>r2.To allow faster computation,r2is estimated chromosome by chromosome,so a MAPfile is required with information of marker assignation to chromo-somes.The MAPfile should contain at least three columns:marker_name,chromosome_name and distance_from_origin(either genetic of physicaldistance,only used for sorting markers,LD being re-estimated from markerData).•ANO(with pval):one-way ANOV A are carried out with R function lm ontrait“pheno”.Every markers are tested one at a time,and only markerswith pvalue<pval are kept for GEBV prediction.•ANO+LD(with pval and r2,MAP is facultative):combines afirst step ofmarker selection with ANO,then a second step of pruning using LD option. reduct.size Specifies the number of markers for the genotypic reduction using RMR(reduct.size <m).r2Coefficient of linkage disequilibrium(LD).Setting0<r2<1if the genotypic re-duction method is in LD or ANO+LD.pval p value for ANO method,0<pval<1.MAP Afile with markers in rows ane at least ONE columns with colnames="chrom".Used for computing r2within linkage groups.geno.impute.methodAllow missing marker data imputation using the two methods proposed in func-tion A.mat of package rrBLUP,namely:•MNI:missing data are replaced by the mean allele frequency of the marker(column in geno)•EMI:missing data are replaced using an expectation-maximization meth-ods described in function A.mat(Endelman&Janninck2012).Default value is NULL.Note that these imputation methods are only suited when there are a few miss-ing value,typically in marker data from SNP chips of KasPAR.They are NOTsuited for imputing marker data from low density to high density designs,andwhen there are MANY missing Data as typically provided by GBS.More so-phisticated software(e.g.Beagles,Browning&Browning2016)should be usedbefore BWGS.predict.method The options for genomic breeding value prediction methods.The available op-tions are:•GBLUP:performs G-BLUP using a marker-based relationship matrix,im-plemented through BGLR R-library.Equivalent to ridge regression(RR-BLUP)of marker effects.•EGBLUP:performs EG-BLUP,i.e.BLUP using a"squared"relationshipmatrix to model epistatic2x2interactions,as described by Jiang&Reif(2015),using BGLR library•RR:ridge regression,using package glmnet.In theory,strictly equivalentto gblup.•LASSO:Least Absolute Shrinkage and Selection Operator is another pe-nalized regression methods which yield more shrinked estimates than RR.Run by glmnet library.•EN:Elastic Net(Zou and Hastie,2005),which is a weighted combinationof RR and LASSO,using glmnet librarySeveral Bayesian methods,using the BGLR library:•BRR:Bayesian ridge regression:same as rr-blup,but bayesian resolution.Induces homogeneous shrinkage of all markers effects towards zero withGaussian distribution(de los Campos et al,2013)•BL:Bayesian LASSO:uses an exponential prior on marker variances pri-ors,leading to double exponential distribution of marker effects(Park&Casella2008)•BA:Bayes A uses a scaled-t prior distribution of marker effects.(Meuwis-sen et al2001).•BB:Bayes B,uses a mixture of distribution with a point mass at zero andwith a slab of non-zero marker effects with a scaled-t distribution(Habieret al2011).•BC:Bayes C same as Bayes B with a slab with Gaussian distribution.A more detailed description of these methods can be found in Perez&de losCampos2014(/BGLR-extdoc.pdf).Three semi-parametricmethods:•RKHS:reproductive kernel Hilbert space and multiple kernel MRKHS,us-ing BGLR(Gianola and van Kaam2008).Based on genetic distance and akernel function to regulate the distribution of marker effects.This methodsis claimed to be effective for detecting non additive effects.•RF:Random forest regression,using randomForest library(Breiman,2001,Breiman and Cutler2013).This methods uses regression models on treenodes which are rooted in bootstrapping data.Supposed to be able to cap-ture interactions between markers•SVM:support vector machine,run by e1071library.For details,see Chang,Chih-Chung and Lin,Chih-Jen:LIBSVM:a library for Support Vector Ma-chines .tw/~cjlin/libsvm•BRNN:Bayesian Regularization for feed-forward Neural Network,withthe R-package BRNN(Gianola et al2011).To keep computing time inreasonable limits,the parameters for the brnn function are neurons=2andepochs=20.inra47k9ValueThe object bwgs.predict returns Matrix of dimension nx3.Columns are:•Predict BV:the nx1vector of GEBVs for the validation set(rows of geno_valid)•gpredSD:Standart deviation of estimated GEBV•CD:coefficient of determination for each GEBV,estimated as sqrt((1-stdev(GEBVi))^2/2g) Note that gpredSD and CD are only available for methods using the BGLR library,namely GBLUP, EGBLUP,BA,BB,BC,BL,RKHS and MKRKHS.These two columns contain NA for methods RF, RR,LASSO,EN and SVM.Examplesdata(inra)#Prediction using GBLUP methodpredict_gblup<-bwgs.predict(geno_train=TRAIN47K,pheno_train=YieldBLUE,geno_target=TARGET47K,MAXNA=0.2,MAF=0.05,geno.reduct.method="NULL",reduct.size="NULL",r2="NULL",pval="NULL",MAP="NULL",geno.impute.method="MNI",predict.method="GBLUP")inra47k INRA47KDescriptioninra data contains a set of geno47K(760x47839),pheno(760x1)and MAP47K(47839x3).The phenotype pheno contains adjusted genotype means for yield trait(YLD)over multi-year/location trials.UsageMAP47KTARGET47KTRAIN47KYieldBLUE10removeSaveAtFormatAn object of class data.frame with10000rows and3columns.An object of class matrix(inherits from array)with100rows and10000columns.An object of class matrix(inherits from array)with100rows and10000columns.An object of class numeric of length100.Source<https://forgemia.inra.fr/umr-gdec/bwgs>removeSaveAt Remove saveAtfile generated by BGLR packageDescriptionRemove saveAtfile generated by BGLR packageUsageremoveSaveAt(saveAt)ArgumentssaveAt saveAtIndex∗datasetsinra47k,9bwgs.cv,2bwgs.predict,6inra47k,9MAP47K(inra47k),9removeSaveAt,10TARGET47K(inra47k),9TRAIN47K(inra47k),9YieldBLUE(inra47k),911。

Package‘dvqcc’October13,2022Type PackageTitle Dynamic V AR-Based Control Charts for Batch Process MonitoringVersion0.1.0Description A set of control charts for batch processes based on the V AR model.The package con-tains the implementation of T2.var and W.var control charts based on V AR model coefficients us-ing the couple vectors theory.In each time-instant the V AR coefficients are estimated from a his-torical in-control dataset and a decision rule is made for online classify-ing of a new batch data.Those charts allow efficient online monitoring since the veryfirst time-instant.The offline version is available too.In order to evaluate the chart's perfor-mance,this package contains functions to generate batch data for offline and online monitor-ing.See in Danilo Marcondes Filho and Marcio Valk(2020)<doi:10.1016/j.ejor.2019.12.038>. License GPL-3Encoding UTF-8LazyData trueRoxygenNote7.1.0Depends ggplot2,tsDynNeedsCompilation noAuthor Márcio Valk[aut],Danilo Marcondes Filho[aut,cre],Gabriela Cybis[aut]Maintainer Danilo Marcondes Filho<************************>Repository CRANDate/Publication2020-06-0911:40:02UTCR topics documented:offlinem (2)onlinem (4)simoff (6)simon (7)Index91offlinem Offline monitoringDescriptionOffline T2.var and W.var control charts for monitoring batch processes based on V AR model.This approach is fully described in"Marcondes Filho,D.,&Valk,M.(2020).Dynamic V AR Model-Based Control Charts for Batch Process Monitoring.European Journal of Operational Research." Usageofflinem(data,size,newdata=NULL,confidence.level=0.99,type="T2.var",covvar="empirical",plot=TRUE,var.estimates=FALSE)Argumentsdata dataframe of reference dataset.For each batch,variables are arranged in lines and columns are time-instants.The different batches are combined in a singledataset through rbindsize number of variablesnewdata dataframe of one or more new batches for monitoring(each with same number of variables and time instants of data).Different batches are combined in asingle dataset through rbindconfidence.levelH0probability to be consider to define the quantile(default is0.99) type"T2.var"for Hotelling chart(default)and"W.var"for Generalized Variance chartcovvar"empirical"for sample covariance of coefficients(default)and"theoretical"for estimated theoretical covarianceplot TRUE shows the charts plots(default TRUE)var.estimates TRUE show informations about the V AR modeling phase(default FALSE) Details#’The maximum number of variables isfive.All batches must have the same number of time-instants.The dataset of batches in"data"are considered to be in controlValuebeyond.limits:returns the batches that the T2.var(or W.var)scores are above the control limit LimT2(or LimW):T2.var(or W.var)control limitperc:perc_ref(and perc_new)returns the rate of reference batches(and new batches)above the control limit(perc_ref=g_ref/I and perc_new=g_new/Inew,where I(Inew)is the overall number of reference batches(new batches)and g_ref(g_new)is the number of reference batches(new batches)above the control limit)arl:arl_ref(and arl_new)returns the mean number of reference batches(and new batches)before a signal is given by the charts(arl_ref=1/perc_ref and arl_new=1/perc_new)varest:If var.estimates=TRUE,it returns the matrices(vec.phis and vec.phis.new)in which each row contains the estimated V AR(1)phis for each reference batches and new batches,respectively;the matrices(vec.cov.theoretical and vec.cov.empirical)with the theoretical and empirical estimated phis covariances from the reference batches,respectively;the lists(cov.B1and cov.B1new)of the theoretical estimated phis covariances of the reference and new batches,respectively;the number(I)of reference batches;the number(Inew)of new batches;and the number(n)of time-instantsSee AlsosimoffExamples#Example1:Monitoring considering two variables and Inew=10in control batches mydata=simoff()T2.var=offlinem(data=mydata$data,size=2)T2.var.new=offlinem(data=mydata$data,size=2,newdata=mydata$newdata)W.var=offlinem(data=mydata$data,size=2,type="W.var")W.var.new=offlinem(data=mydata$data,size=2,type="W.var",newdata=mydata$newdata) #Example2:Monitoring considering three variables and Inew=50out of control batches B1=matrix(c(-0.3,0,0.4,0,0.2,0,0,-0.1,0.5),3,byrow=TRUE)B1new=matrix(c(0.7,0,0.4,0,0.5,0,0,-0.1,0.5),3,byrow=TRUE)mydata2=simoff(n=100,I=100,size=3,Inew=50,B1,varcov=diag(3),B1new)T2.var=offlinem(data=mydata2$data,size=3)T2.var.new=offlinem(data=mydata2$data,size=3,newdata=mydata2$newdata)W.var=offlinem(data=mydata2$data,size=3,type="W.var")W.var.new=offlinem(data=mydata2$data,size=3,type="W.var",newdata=mydata2$newdata)onlinem Online monitoringDescriptionOnline T2.var and W.var control charts for monitoring batch processes based on V AR model.This approach is fully described in"Marcondes Filho,D.,&Valk,M.(2020).Dynamic V AR Model-Based Control Charts for Batch Process Monitoring.European Journal of Operational Research." Usageonlinem(data,size,newdata,Lc=50,Lr=30,confidence.level=0.99,type="T2.var",covvar="empirical",plot=TRUE,var.estimates=FALSE)Argumentsdata dataframe of reference dataset.For each batch,variables are arranged in lines and columns are time-instants.The different batches are combined in a singledataset through rbindsize number of variablesnewdata dataframe of one or more new batches for monitoring(each with same number of variables and time instants of data).Different batches are combined in asingle dataset through rbindLc length of the coupled vectorLr length of random vector(Lr<Lc)confidence.levelH0probability to be consider to define the quantile(default is0.99) type"T2.var"for Hotelling chart(default)and"W.var"for Generalized Variance chartcovvar"empirical"for sample covariance of coefficients(default)and"theoretical"for estimated theoretical covarianceplot TRUE shows the charts plots(default TRUE)var.estimates TRUE show informations about the V AR modeling phase and the elements for setting T2.var/W.var control charts(default FALSE)DetailsThe maximum number of variables isfive.All batches must have the same number of time-instants.This method is based on the use of"coupled vectors(cv)"(for more details,see Marcondes Filho,D.,&Valk,M.,2020").The parameters Lc and Lr define the cv structure.Considering the new ongoing batch under monitoring:Lc is the number of elements of cv.(Lc is smaller than the number of time-instants);Lr is thenumber of elements in cv randomly chosen from the in control batches in the reference dataset;Lc-Lr is the number of elements in cv from the last(Lc-Lr)observations of the new ongoing batch.Default is Lc=50e Lr=30.The batches in dataset"data"are considered to be in controlValuebeyond.limits:returns the time-instants of each batch that the T2.var(or W.var)score are above thecontrol limitarl:returns for each new batch the mean number of time-instants before a signal is given by thecontrol chart(arl=n/g,where n is the overall number of time-instants and g is the number of time-instants above the control limit)time.to.first.detection(TFD):returns for each new batch thefirst time-instant(the most recentlyinstant)that can be considered as a possible signal of process change.TFD is thefirst point(t*)above the limit followed by two consecutive points above the control limitartl:returns the cumulative rate of the overall time-instants(n)until thefirst three consecutive pointsare above the control limit.The artl is computed using thefirst of these three points,that is,artl=t*/nvarest:If var.estimates=TRUE,it returns the Lim_T2vector(or Lim_W vector)of the T2.var(orW.var)control limits to each time-instant;the T2.var(or W.var)scores for each new batch to eachtime-instant[(tnew)matrix for the T2.var and(wnew)matrix for the W.var scores];the number(I)of reference batches;the number(C=size*size)of estimated phis coefficients;the number(Inew)of new batches;the number(n)of time-instants and the(cov)list including the estimated meancovariance from the reference batches to each time-instant#’@examples#Example1:Monitoring considering two variables and Inew=5in control batchesmydata=simon()T2.var.on=onlinem(data=mydata$data,size=2,newdata=mydata$newdata)W.var.on=onlinem(data=mydata$data,size=2,type= #Example2:Monitoring considering two variables and Inew=10in control batchesB1=matrix(c(-0.3,0.4,0.4,0.5),2,byrow=TRUE)B1new=B1mydata2=simon(n=100,I=200,size=2,Inew=10,n1=50,B1=B1, T2.var.on=onlinem(data=mydata2$data,size=2,newdata=mydata2$newdata,plot=F)W.var.on=onlinem(data=mydata2$data,s plot=F)#Example3:Monitoring considering three variables and Inew=10out of control batchesB1=matrix(c(-0.3,0,0.4,0,0.2,0,0,-0.1,0.5),3,byrow=TRUE)B1new=matrix(c(0.7,0,0.4,0,0.5,0,0,-0.1,0.5),3,byrow=TRUE) mydata3=simon(n=100,I=200,size=3,Inew=10,n1=50,B1=B1,varcov=diag(3),B1new=B1new)T2.var.on=onlinem(data=myd plot=F)W.var.on=onlinem(data=mydata3$data,size=3,type="W.var",newdata=mydata3$newdata,plot=F)6simoffSee Alsosimonsimoff simulating dataset(Offline settings)DescriptionFunction to simulate datasets based on the V AR(1)model(without intercept).The dataset include in control batches and new batches for offline monitoringUsagesimoff(n=100,I=100,size=2,Inew=10,B1=matrix(c(-0.3,0.4,0.4,0.5),ncol=size,byrow=TRUE),varcov=diag(2),B1new=matrix(c(-0.3,0.4,0.4,0.5),ncol=size,byrow=TRUE))Argumentsn number of time-instantsI number of in control batch samplessize number of variablesInew number of new batch samples for monitoringB1matrix(size x size)containing the V AR(1)coefficients of in control batches varcov covariance matrix(size x size)of errorsB1new matrix(size x size)containing the V AR(1)coefficients of new batchesValuedata A dataframe of reference dataset of I in control batches.For each batch,variables are arranged in lines and columns are time-instants.The different batches are combined in a single dataset of dimension[(I*size)X n]newdata A dataframe including a dataset of Inew new batches for monitoring(each with same number of variables and time-instants of data).The different batches are combined in a single dataset of dimension[(Inew*size)X n]See Alsoofflinem,simon,onlinemExamples#Example1:Two variables(A default in control simulating dataset)mydata=simoff()#Example2:Three variables and Inew=50out of control batchesB1=matrix(c(-0.3,0,0.4,0,0.2,0,0,-0.1,0.5),3,byrow=TRUE)B1new=matrix(c(0.1,0,0.4,0,0.2,0,0,-0.1,0.5),3,byrow=TRUE)mydata1=simoff(n=100,I=100,size=3,Inew=50,B1,varcov=diag(3),B1new) #Example3:Three variables and Inew=1new out of control batchB1=matrix(c(-0.3,0,0.4,0,0.2,0,0,-0.1,0.5),3,byrow=TRUE)B1new=matrix(c(0.1,0,0.4,0,0.2,0,0,-0.1,0.5),3,byrow=TRUE)mydata2=simoff(n=100,I=100,size=3,Inew=1,B1,varcov=diag(3),B1new)plot.ts(t(mydata2$data[1:3,]),main="One in control batch sample")plot.ts(t(mydata2$newdata),main="One new batch sample")simon simulating dataset(Online settings)DescriptionFunction to simulate datasets based on the V AR(1)model(without intercept).The dataset include in control batches and new batches for online monitoringUsagesimon(n=100,I=200,size=2,Inew=5,n1=50,B1=matrix(c(-0.3,0.4,0.4,0.5),ncol=size,byrow=TRUE),varcov=diag(2),B1new=matrix(c(-0.3,0.4,0.4,0.5),ncol=size,byrow=TRUE))Argumentsn number of time-instantsI number of in control batch samplessize number of variablesInew number of new batch samples for monitoringn1number of time-instants for the new batches under the in control processB1matrix(size x size)containing the V AR(1)coefficients of in control batches varcov covariance matrix(size x size)of errorsB1new matrix(size x size)containing the V AR(1)coefficients of new batchesValuedata A dataframe of reference dataset of I in control batches.For each batch,variables are arranged in lines and columns are time-instants.The different batches are combined in a single dataset of dimension[(I*size)X n]newdata A dataframe including a dataset of Inew new batches for monitoring(each with same number of variables and time-instants of data).The different batches are combined in a single dataset of dimension[(Inew*size)X n]See Alsoonlinem,simoff,offlinemExamples#Example1:Two variables(A default in control simulating dataset)mydata=simon()#Example2:Three variables and Inew=5out of control batchesB1=matrix(c(-0.3,0,0.4,0,0.2,0,0,-0.1,0.5),3,byrow=TRUE)B1new=matrix(c(0.1,0,0.4,0,0.2,0,0,-0.1,0.5),3,byrow=TRUE)mydata1=simon(n=100,I=200,size=3,Inew=5,n1=50,B1,varcov=diag(3),B1new) #Example3:Three variables and one Inew=1out of control batchB1=matrix(c(-0.3,0,0.4,0,0.2,0,0,-0.1,0.5),3,byrow=TRUE)B1new=matrix(c(0.1,0,0.4,0,0.7,0,0,-0.1,0.5),3,byrow=TRUE)mydata2=simon(n=100,I=1,size=3,Inew=1,n1=25,B1,varcov=diag(3),B1new)plot.ts(t(mydata2$data),main="One in control batch sample")plot.ts(t(mydata2$newdata),main="One new batch sample")Indexofflinem,2onlinem,4simoff,6simon,79。

Package‘ensr’October13,2022Title Elastic Net SearcheRVersion0.1.0Description Elastic net regression models are controlled by two parameters,lambda,a measure of shrinkage,and alpha,a metric defining the model'slocation on the spectrum between ridge and lasso regression.glmnet provides tools for selecting lambda via crossvalidation but no automated methods for selection of alpha.Elastic NetSearcheR automates the simultaneous selection of both lambda and alpha.Developed,in part,with support by NICHD R03HD094912.License GPL-2Encoding UTF-8URL https:///dewittpe/ensrLazyData trueDepends R(>=3.5.0),glmnetImports data.table,ggplot2Suggests digest,ggforce,gridExtra,knitr,magrittr,microbenchmark,qwraps2(>=0.4.0),R.rsp,rmarkdownRoxygenNote6.1.1VignetteBuilder R.rspNeedsCompilation noAuthor Peter DeWitt[aut,cre],Tell Bennett[ctb]Maintainer Peter DeWitt<*************************>Repository CRANDate/Publication2019-01-2123:30:03UTCR topics documented:ensr (2)lambda_alpha_grid (3)12ensrlambda_max (4)landfill (5)predict (6)preferable (6)standardize (7)tbi (8)Index9ensr Elastic Net SearcheRDescriptionSearch a grid of values of alpha and lambda for minimum mean CV errorUsageensr(x,y,alphas=seq(0,1,length=10),nlambda=100L,standardize=TRUE,nfolds=10L,foldid,...)Argumentsx x matrix as in glmnet.y response y as in glmnet.alphas a sequence of alpha valuesnlambda The number of lambda values-default is100.standardize Logicalflag for x variable standardization,prior tofitting the model sequence.The coefficients are always returned on the original scale.Default is standardize=TRUE.If variables are in the same units already,you might not wish to standardize.Seedetails below for y standardization with family="gaussian".nfolds number of folds-default is10.Although nfolds can be as large as the samplesize(leave-one-out CV),it is not recommended for large datasets.Smallestvalue allowable is nfolds=3foldid an optional vector of values between1and nfold identifying what fold eachobservation is in.If supplied,nfold can be missing....Other arguments that can be passed to glmnetlambda_alpha_grid3 lambda_alpha_grid Lambda Alpha GridDescriptionConstruct a data frame with values for lambda and alpha with an indicator to know if the model is worthfitting.Usagelambda_alpha_grid(lambdas,alphas,nlambda=10L,lmin_factor=1e-04)Argumentslambdas a vector of max lambda values for each alpha givenalphas a vector of alpha values corresponding to the max lambdasnlambda number of lambdas to generate for each alpha before creating the gridlmin_factor the smallest lambda value is defined as lmin_factor*max(lambda)where max(lambda)is determined by this function.Examplesdata(tbi)Xmat<-model.matrix(~.-injury1-injury2-injury3-1,data=tbi)Yvec<-matrix(tbi$injury1,ncol=1)alphas<-seq(0,1,length=20)lga<-lambda_alpha_grid(alphas=alphas,lambdas=lambda_max(Yvec,Xmat,alpha=alphas)) ggplot2::ggplot()+ggplot2::theme_bw()+ggplot2::aes_string(x="a",y="log10(l)")+ggplot2::geom_path(data=lga$top)+ggplot2::geom_point(data=lga$lgrid,mapping=ggplot2::aes(color=cos(a)+sin(log10(l))))+ ggplot2::geom_contour(data=lga$lgrid,mapping=ggplot2::aes(z=cos(a)+sin(log10(l))))+ ggplot2::scale_color_gradient2(low="blue",high="red",mid="grey")4lambda_max lambda_max Lambda MaxDescriptionDetermine the lambda_max value that would be generated from a call to glmnet without making that call.Usagelambda_max(y,x,standardize=TRUE,alpha=0,lmin_factor=1e-04,...)Argumentsy the response vectorx the predictor matrixstandardize logicial,should the x matrix be standardized?alpha the glmnet alpha valuelmin_factor the smallest lambda value is defined as lmin_factor*max(lambda)where max(lambda)is determined by this function....other argsExamplesdata(tbi)Xmat<-model.matrix(~.-injury1-injury2-injury3-1,data=tbi)Yvec<-matrix(tbi$injury1,ncol=1)alphas<-seq(0,1,length=20)lambda_max(Yvec,Xmat,alpha=alphas)#Look at different options for standardizing the inputs.dat<-expand.grid(standardize=c(TRUE,FALSE),alpha=alphas)lmax<-Map(lambda_max,standardize=dat$standardize,alpha=dat$alpha,MoreArgs=list(y=Yvec,x=Xmat))gmax<-Map(glmnet::glmnet,landfill5 standardize=dat$standardize,alpha=dat$alpha,MoreArgs=list(y=Yvec,x=Xmat))dat$gmax<-sapply(gmax,function(f)f$lambda[1])dat$lmax<-unlist(lmax)par(mfrow=c(1,2))with(subset(dat,standardize==TRUE),{plot(log10(gmax),log10(lmax))abline(0,1)title(main="standardize==TRUE")})with(subset(dat,standardize==FALSE),{plot(log10(gmax),log10(lmax))abline(0,1)title(main="standardize==FALSE")})landfill Water Percolation Through A LandfillDescriptionA computer simulation of water moving through a landfill.Detailed explanation for the variablesand the construction of the data set is found in vignette("ensr-datasets",package="ensr") UsagelandfillFormatAn object of class data.table(inherits from data.frame)with974rows and48columns.See Alsovignette("ensr-datasets",package="ensr")6preferable predict Predict Methods for ensr objectsDescriptionUsing either the lambda.min or lambda.1se,find the preferable model from the ensr object and return a prediction.Usage##S3method for class ensrpredict(object,...)##S3method for class ensrcoef(object,...)Argumentsobject a ensr object...other arguments passed along to predictDetailsThe glmnet::predict argument s is ignored if specified and attempted to be passed via....The value of s that is passed to glmnet::predict is determined by the value of lambda.min or lambda.1se found from a call to preferable.preferable Preferable Elastic Net ModelDescriptionFind the preferable Elastic Net Model from an ensr object.The preferable model is defined as the model with the lowest mean cross validation error and largest alpha value.Usagepreferable(object,...)Argumentsobject an ensr object...not currently used.standardize7 standardize StandardizeDescriptionCenter and scale vectors by mean/standard deviation or median/IQR with the option to base the standardization only on unique observations.Usagestandardize(x,stats=list(center="mean",scale="sd"),use_unique=TRUE,margin)Argumentsx numeric data to standardizestats a list defining the centering and scaling statistics.use_unique use only unique values of x when determining the values for the stats.margin passed to apply if x is a matrix or array.If you want to use all the data in the array for the calculation of the statistics pass margin=0.Examplesx<-1:100standardize(x)standardize(x,stats=list(center="median",scale="IQR"))xmat<-matrix(1:50,nrow=10)standardize(xmat,margin=0)standardize(xmat,margin=1)standardize(xmat,margin=2)xarray<-array(1:60,dim=c(5,2,6))standardize(xarray,margin=0)standardize(xarray,margin=1:2)#Standardize a data.framestandardize(mtcars)#a generic list objectalist<-list(x=rep(1:10,2),y=rnorm(100),z=matrix(1:10,nrow=2))standardize(alist,margin=0)standardize(alist,margin=1)8tbi tbi Synthetic Data Set for Traumatic Brain InjuriesDescriptionThis data is synthetic,that is,it is random data generated to have similar properties to a data set used for studying traumatic brain injuries.The pcode1...pcode6,ncode1...ncode6columns are indicators for procedure or billing codes associated with a hospital stay for TBI.UsagetbiFormata data.table with1323rows and18columns.Each row of the tbi data.table is a unique subject.age age,in daysfemale indicator for sex,1==female,0==malelos length of stay in the hosptialpcode1indicator for if the patient had pcode1pcode2indicator for if the patient had pcode2pcode3indicator for if the patient had pcode3pcode4indicator for if the patient had pcode4pcode5indicator for if the patient had pcode5pcode6indicator for if the patient had pcode6ncode1indicator for if the patient had ncode1ncode2indicator for if the patient had ncode2ncode3indicator for if the patient had ncode3ncode4indicator for if the patient had ncode4ncode5indicator for if the patient had ncode5ncode6indicator for if the patient had ncode6injury1First of three specific types of TBIinjury2Second of three specific types of TBIinjury3Third of three specific types of TBISee Alsovignette("ensr-datasets",package="ensr")Index∗datasetslandfill,5tbi,8coef.ensr(predict),6ensr,2ensr-package(ensr),2glmnet,4lambda_alpha_grid,3lambda_max,4landfill,5predict,6preferable,6,6standardize,7tbi,89。

涂上黄色的表示会随职业不同而不同（可调）原公式Fin_phy_h =（Slf(Lv) *5）+Equ_phy_h_num加入自身命中的初始值bas_phy_h现公式Fin_phy_h =Bas_phy_h+（Slf(Lv) *5）+Equ_phy_h_num原公式Slf_phy_h_rt= 0.75+( Fin_phy_h - Slf(Lv)*5）*0.01删除常数 0.75现公式Slf_phy_h_rt=( Fin_phy_h - Slf(Lv)*5）*0.01原公式Fin_hp_m = Fin_con *（Pit_hp_con）+ Equ_m_hp_num + Lv_hp *（Lv*（Lv+1）/2）去掉了和等级相关的影响，用简单的升级加同样血的方法做现公式Fin_hp_m = Fin_con *（Pit_hp_con）+ Equ_m_hp_num + Lv_hp *Lv原公式Fin_phy_dg = 0.15+(（Fin_agi /（Slf(Lv)*2.5））-1)*0.25加入接口数值，Phy_dg_con1,Phy_dg_con2，Phy_dg_con3。

全部为浮点数。

现公式Fin_phy_dg = Bas_phy_dg+(（Fin_agi /（（Slf(Lv)*Phy_dg_con2）＋Phy_dg_con1））-1)*Phy_dg_con3原公式Fin_atk_spd=Bas_atk_spd*（100/武器重量）*（（敏捷/（自身等级*2）））*（1+Equ_atk_spd_per）删除了武器重量的影响，删除了敏捷的影响Fin_atk_spd=Bas_atk_spd*（1-Equ_atk_spd_per）原公式Fin_atk_pow=（Bas_atk_pow+Equ_atk_pow + Fin_str /2+（Slf(Lv)））*（Equ_weg/100）删除了武器重量的影响，加入了秒伤的概念加入接口数值，Atk_pow_con1,Atk_pow_con2,全部为浮点数。